The Science of Paediatrics_ MRCPCH Mastercourse

729 CHAPTER THIRTY-EIGHT are not actually representative of the whole school population, let alone the whole UK population? How do you know how close your (sample) mean vitamin D value is to the population mean vitamin D value, i.e. the value you would have obtained if you had indeed taken measurements from all 1000 children? This is what confidence intervals help you to estimate. Confidence intervals are derived using the standard error (Box 38.1). The standard error tells you how close a sample estimate (e.g. the sample mean, in our case the mean of our 100 children) is likely to be to the population value (e.g. the population mean, in our D levels of urban UK secondary school children. You are aware that it would be impossible to measure the vitamin D level of all UK urban secondary school children and therefore identify one urban school within your county. ii. Methods: There are 1000 children in the school, and your budget does not allow you to measure vitamin D levels in all of them. You therefore select (sample) 100 children at random whom you hope are a representative sample of all children within the school. One hopes that the sample is representative of the whole group (the cohort). iii. Results: You find that the measurements are normally distributed and you therefore calculate a mean value for the vitamin D levels of your 100 selected children. Mean vitamin D = 21.9ng/mL. You find a standard deviation of 5ng/mL. iv. Discussion: What if you had collected samples from 100 children who, by chance alone, just happened to have very high or very low vitamin D levels and Fig. 38.9 Relationship between the normal distribution and a box-and-whisker plot. A. Median and interquartile range. B. Median ±1SD and 3SD. –6σ –5σ –4σ –3σ –2σ –1σ 0 1σ 2σ 3σ 4σ 5σ 6σ 50% 24.65% 24.65% Median IQR Q1 Q3 –6σ –5σ –4σ –3σ –2σ –1σ 0 1σ 2σ 3σ 4σ 5σ 6σ 68.27% 15.73% 15.73% A B Box 38.1 Summary of calculation for SE and CI Standard error se standard deviation SD square root of sa ( ) = ( ) mple size n( ) 95% CI mean se mean se = − 1 96 1 96 . , . +

38 730Statistics p-values As with confidence intervals, in order to apply evidence from research studies, it is vital to understand what p-values mean and, crucially, what they can and cannot tell us. Testing for statistical significance involves setting up a null hypothesis (see Box 37.7) and measuring the project the mean vitamin D level in all urban UK secondary children). Using the example above, if we took, theoretically, ten different samples (groups) of 100 children from the school, we would get a range of mean vitamin D values for each group, and these mean values could each be plotted as a histogram with a normal distribution around the (‘true’) population mean. The degree to which each sample mean is likely to vary from the true population mean is the standard error. As expected, the standard error will be smaller with a larger sample size and where there is less variability of the measurements. Standard error is calculated: Standard error standard deviation SD sample size n = ( ) ( ) Therefore, in our case: Standard error = = = 5 100 5 10 0 5. This brings us back to confidence intervals, which define the range of values within which the ‘true’ population mean is ‘likely’ to lie. Because each of our sample means are normally distributed around the population mean, and in a normal distribution 95% of the values are expected to lie within 1.96 standard errors of the mean, the 95% confidence interval is calculated as follows: • Lower limit = (mean − 1.96 × standard error) • Upper limit = (mean + 1.96 × standard error) The 95% CI is an accepted norm for most research studies, but if, for example, you wanted to calculate the 99% CI, you would have to multiply the standard error by 2.58. In our example of normal vitamin D levels: • Standard error = 0.5 • Mean = 21.9 • Therefore the confidence interval (CI) = 20.9– 22.9ng/mL. The upper and lower limits of the confidence interval define the range within which you would expect to find the population mean 95 times out of 100. That means that 5 times out of 100, the population mean would be expected to fall outside of that range. We can therefore say that we would expect that the true mean of vitamin D levels in our single secondary school will fall between 20.9 and 22.9ng/mL, 95 out of 100 times. Regression to the mean Regression to the mean is the tendency for a variable which is extreme – i.e. far from the average – to be closer to the mean if measured on a second occasion. Question 38.3 Data analysis A study aims to compare time trends of a specific illness in childhood. Methods: A cross-sectional analysis of general practice national health returns from 350 practices during 2005 and 2010. Results: The age–sex standardized incidence rate of the illness per 1000 patient years in 2005 for children aged 1–5 was 17.2 (95% confidence intervals (CI) 16.6–17.8) and for those aged 6–16 was 8.6 (95% CI 8.3–8.8). In 2010 it was 10.7 (95% CI 10.2–11.2) for children aged 1–5 and 6.3 (95% CI 6.1–6.3) for those aged 6–16. The number of prescriptions issued for the illness per 1000 patients aged 6–16 in 2005 was 357 (95% CI 355– 357) and in 2010 was 368 (95% CI 366–370). Given the data shown above which of the following statements is true? Select ONE answer only. A. The change in incidence for children aged 1–5 is the same for both sexes. B. The data can be used to calculate the course of the illness for any affected child. C. The incidence of the illness studied has decreased in the 6–16 age group between 2005 and 2010. D. The number of prescriptions issued for the illness has decreased between 2005 and 2010. E. There has been no decrease in the incidence of the illness in children aged 1–5 between 2005 and 2010. Answer 38.3 C. The incidence of the illness has decreased in the 6–16 age group between 2005 and 2010. The results show two clearly separated incidence levels and no overlap between the confidence intervals. A is incorrect because no data is presented which separates the incidence between males and females. B is incorrect because this is a crosssectional and not a longitudinal study. D is incorrect because the total prescription rates and confidence intervals overlap. E is incorrect because the results show clear separation of the incidence levels and no overlap of the confidence intervals.

731 CHAPTER THIRTY-EIGHT Even when appropriate statistical tests are applied, if a study is underpowered there is a higher chance of making a type 2 error (‘false negative’). Underpowered studies are those which have an insufficient sample size given the effect size and group variability. If a study fails to find a statistically significant difference, it may not be that there is truly no effect, rather that the study fails to demonstrate an effect. Type 1 errors (‘false positives’) occur commonly in post-hoc analysis, where data is analysed after it has been collected in a way that was not specified at the start of the study. Researchers may ‘fish’ through their data, or ‘data dredge’, applying many different tests until they find a ‘significant’ result. Post-hoc analyses can be useful and may provide potential avenues of research for future studies, for example, when applied to subgroups of a large population. However, it should be clear which analyses were performed post-hoc, and the results interpreted with this in mind. Statistical tests In this section, we aim to summarize the most commonly used statistical tests, explaining which tests are appropriate in different situations, and provide better strength of evidence of the observed data against it. The null hypothesis is usually a statement of the effect of an intervention or of a difference between groups. It is a ‘null’ hypothesis because it assumes that there is no difference or no effect. For example, ‘salbutamol nebulizers given in the emergency department have no effect on admission rates in children with acute asthma’, or ‘there is no difference in the birth weights between babies born to smoking and non-smoking mothers’. When conducting studies, researchers set up a null hypothesis to test whether or not the data collected are consistent or not consistent with it. The next step is to choose the significance level of the statistical test that will be used (see below for different types of statistical test). A 95% significance level is conventionally selected. After data collection, the p-value is calculated using the appropriate statistical test. If the p-value is at or below the significance level, the null hypothesis is rejected: there is a statistically significant difference. If the p-value is greater than the significance level, the null hypothesis is accepted and there is no statistically significant difference. A p-value therefore tells us the probability of obtaining the data we have if the null hypothesis were true. It helps to quantify the strength of evidence against the null hypothesis. If a p-value is 0.04, for example, if we were to conduct this study 100 times, then we could expect to obtain this result 4 times, or 4% of the time, if the null hypothesis is true, due to chance alone. The p-value is equivalent to the type 1 error rate. A type 1 error occurs when the null hypothesis is rejected when it is in fact true (Box 38.2), i.e. when we conclude that there is an effect or difference where none actually exists, or a ‘false positive’. A type 2 error, on the other hand, occurs when the null hypothesis is accepted when in fact it is false (see Box 38.2), i.e. when we conclude that there is no effect or difference where one does in fact exist, or a ‘false negative’. Power and error The power of a test is equivalent to type 1 minus the type 2 error rate. It is the probability that a test will produce a significant difference at a given significance level. For example, where the power of a statistical test is 80, if this statistical test was performed 100 times, it would miss the true difference 20 times out of 100. The power depends on the true difference between the two populations or intervention effects, the sample size and the significance level. For research studies, a power calculation is used to help determine the sample size that would be required to detect a difference at a certain anticipated, assumed or estimated level before a study is carried out. Box 38.2 Examples of type 1 and type 2 errors Two teams of researchers are working on answering a similar question: In a pre-school aged child with viral-induced wheeze [patient], is a course of five days of oral prednisolone [intervention] compared to placebo [control] effective at preventing admission [outcome]? The null hypothesis is that there is no difference between the two groups. Team A undertake their project but only blind the study to subjects and not to the medical staff, allocating the children to the intervention and control arm. Unconscious bias is performed in allocating the patients which results in the treatment arm being statistically significant (p <0.05). If a repeat study with improved methodology finds there is no difference between the groups, this shows that Team A have introduced a type 1 error. Team B undertake their project but are only able to recruit 200 children to each group, despite a power calculation suggesting they need 350 children in each group. The results show that there is no statistical difference between the two groups. The researchers therefore accept their null hypothesis. If a repeat study with 350 in each group reveals that there is a statistically significant difference between the two groups, then Team B have introduced a type 2 error.

38 732Statistics understanding of the statistical methodologies used in the research papers. The choice of statistical test (Fig. 38.10) will depend on: • The number and type of variables • The quantity and distribution of the data. The test performed is only valid if certain assumptions are met, and these are different for each test. There may be several possible tests that could be Fig. 38.10 Statistical tests for comparing two independent groups of data. Is the data continuous? Yes No Yes No Yes No Is the data normally distributed? Is it sound to assume variance is equal? Mann–Whitney U test Two sample t-test with unequal variance Two sample t-test with pooled variance Chi-squared test for trend Can the data be categorized (>2 categories)? Is the data ordinal? Yes No Yes No No Yes Yes No All expected “events” > 5 Chi-squared test > 5 “events” in each category Fisher’s exact or chi-squared with Yates correction Analyse with care (pool if possible) applied to any statistical problem; there is often no one ‘right’ test. However, even if it is valid to apply a certain test in a certain situation, it may not be the most appropriate or powerful. When planning a research study, seeking advice from someone with knowledge and experience of statistics is advisable. Testing differences between groups Question 38.4 Differences between groups The following is a list (A–J) of statistical tests: A. Analysis of variance (ANOVA) B. Chi-squared test C. Chi-squared test for trend D. Kruskall–Wallis ANOVA test E. Mann–Whitney U test F. Multiple linear regression G. Multiple logistic regression H. Paired t-test I. Simple linear regression J. Unpaired t-test For each of the following case scenarios, select the most appropriate statistical test from the list above. Each answer may be used once, more than once or not at all: 1. A sample of 1000 seven-year-old male schoolchildren undergo BMI testing. Their BMI is remeasured one year later to make a comparison. 2. A sample of 1000 seven-year-old male schoolchildren undergo BMI testing and are compared with another group of 1000 male children of the same approximate age from a school in another country. 3. A region experiences a large outbreak of invasive meningococcal disease. A retrospective study is carried out in order to identify which factors (e.g. initial platelet count, gender) were associated with survival/death.

733 CHAPTER THIRTY-EIGHT example, the mean number of days of ventilation on PICU for infants with RSV bronchiolitis compared with infants with non-RSV bronchiolitis. In this example, an unpaired test would be appropriate, as the two populations are independent, i.e. infants with RSV bronchiolitis and infants with non-RSV bronchiolitis are two separate groups. If you are comparing the same group before and after an intervention, for example, then a paired test would be more appropriate. For example, a paired test was used to compare the paired oral and axillary temperatures (see Fig. 38.5), i.e. observations performed simultaneously on the same child. Analysis of variance (ANOVA) T-tests can be used when comparing the means of two groups that are normally distributed, but if three or more groups are compared, the appropriate test is an analysis of variance or ANOVA. For example, if you wanted to compare the change in weight from birth to six months of age in four groups of infants fed on different formula milks. Bearing in mind that the null hypothesis is that there is no difference between the means of the groups, a significant p-value derived by using ANOVA tells you that at least one of the groups has a mean that is more extreme (which could be larger or smaller) than would have been expected to have occurred by chance. If the number of groups is small and the data can be easily visualized by graphical display, it may be easy to see which sample mean is different, but if not, further tests – called multiple comparisons procedures – may need to be performed. Non-parametric tests Where data are not normally distributed, and where transformations cannot be applied in order to meet the requirements for one of the above parametric tests, it is necessary to use a non-parametric test. Parametric refers to the parameters of the distribution – i.e. the mean and the standard deviation of a normally distributed sample. Non-parametric tests do not require any assumptions to be made about the distribution of the data; they compare medians rather than means, but can still provide a measure of precision, usually expressed as a confidence interval. There are many different types of non-parametric test, and only some of the most commonly used ones are described in this section. Most parametric tests require data to be ranked in order of magnitude. This means that the differences between rank scores do not usually correspond to differences between measurements or observations. For example, using the data above for time from triage to assessment for febrile children in an emergency department, the ranking would be as shown in Table 38.1. The difference between the observations ranked 1 and 2 is eight minutes, whereas the difference between Comparison between means, percentages or proportions For large samples, we can calculate confidence intervals and p-values using standard equations and tables based on mathematical models of the normal distribution, applying the concepts discussed above. In the vast majority of cases, it is appropriate to calculate a two-sided p-value, which means that our results could go in either direction – for example, a new drug could be either better or worse than a pre-existing drug. Very rarely (certainly in medical research), a significant difference in one direction may be impossible or the direction of the difference is known with certainty, a priori, in which case a one-sided test could be applied, which considers only one tail of the distribution. Using a one-sided test, the null hypothesis is twice as likely to be rejected, so researchers must have very clear grounds for applying it. T-tests A t-test (sometimes referred to as ‘Student’s t-test’) can be used in smaller samples with continuous data that is normally distributed, as it includes a modification allowing for the expected increase in chance variation occurring in smaller samples. However, the sample has to be large enough to estimate the population standard deviation. A one-sample t-test is appropriate where a sample is compared to a known value. For example, you want to find out whether there is a difference between the birth weights of babies of UK-born mothers living in the UK and those of babies of UK-born mothers living in the USA. You may already know the mean birth weight of babies born to UK-born mothers living in the UK, and you want to compare the mean from your sample from the USA to see if there is a significant difference. Two-sample t-tests, on the other hand, compare the sample means of two different populations. For Answer 38.4 1. H. Paired t-test. Large sample, normally distributed, same observation on same individual (paired measurements). 2. J. Unpaired t-test. Large sample, normally distributed, two independent samples from potentially two independent cohorts. 3. G. Multiple logistic regression. There were both continuous variables (platelet count) and dichotomous variables (gender), so it has to be multivariate and not ANOVA. The outcome is binary.

38 734Statistics Looking at relationships between groups Chi-squared test Chi-squared tests look at the relationship between categorical variables (e.g. yes versus no or eczema versus no eczema). The chi-squared is a test employed for testing if there is an association between variables, unlike the tests above that look at differences between groups. When it is applied, the null hypothesis is that there is no association between the variables. It provides a p-value, which is the probability of obtaining the observed data if the null hypothesis were true (i.e. no association), but does not provide confidence intervals, and so does not give any indication of the magnitude of the association. Data for chi-squared tests are set up in contingency tables (with binary variables – i.e. yes/no), and the calculations are based on the differences between the observed and the ‘expected’ values (expected if there was no association between the variables). The contingency tables can be in the classic 2 × 2 table form, or for more rows, the chi-squared test can be applied to each row. For example, you might want to look at children with peanut allergy at the age of 3 years (i.e. peanut allergy – yes/no), and see if this is associated with weaning at or after six months (i.e. weaned late – yes/no). If you wanted to look at the association of peanut allergy with other factors, you could have more rows in the table, and perform a chi-squared test for association with each, for example, at least one parent with a history of atopy, diagnosis of eczema, presence of family pet, etc. Where the numbers in one or more cells of a table are small, the chi-squared test is not valid, and the Fisher’s exact test can be used instead. Correlation and regression Chi-squared tests look at the relationship between categorical variables, whereas correlation and regression are methods used to examine the relationship between numeric variables. 2 and 3 is only three minutes. Similarly, if the 10th observation was 231 minutes, the difference between that and the 9th observation would be significantly larger than the differences between the others. For many of the parametric tests, there are equivalent non-parametric tests that can be used in their place. The Mann–Whitney U test is the non-parametric equivalent of the two-sample t-test, except where samples are paired, in which case the Wilcoxon matched paired test is more suitable. For more than two groups of non-parametric data, the Kruskall–Wallis ANOVA test can be used. Table 38.1 Ranking of time from triage to assessment for febrile children in an emergency department Rank Observation – group 1 (minutes) 1 11 2 19 3 22 4 27 5 28 6 31 7 33 8 39 9 42 10 48 Question 38.5 Student’s t-test A number of standard statistical tests are used for the analysis of results in clinical trials. One such test, used to compare two populations, is the Student’s t-test. Which of the following statements about the Student’s t-test used to compare two populations is always true? Select ONE answer only. A. Tables used to calculate p-values are same as those used for chi-squared tests. B. The data used for each of the two populations studied must be independent of the other group. C. The t-test can be used for non-parametrically distributed data. D. The test can only be applied to samples containing 30 or more observations. E. The t-test can also be used to compare data from 3 or more populations. Answer 38.5 B. The data used for each of the two populations studied must be independent of the other group. Student’s t-test is only reliable when the data from each population is independent of the other. A is incorrect because different tables for p-values are required for the t-test compared to those used for chi-squared analysis. C is incorrect because the t-test is only applicable to normally distributed data. D is incorrect because the t-test was specifically developed to deal with small sample sizes of fewer than 30 data points. E is incorrect because the appropriate test for three or more populations is the analysis of variance (ANOVA) analysis.

735 CHAPTER THIRTY-EIGHT in using the equation to extrapolate values of x beyond those in the data range available. The assumption that the equation holds true beyond the data range in hand is unwarranted. If there is more than one independent variable, multiple regression methods can be applied. For example, taking the sample of neonates and looking at a number of different variables that could be associated with a risk of NEC, e.g. gestation, birth weight, maternal chorioamnionitis, etc. Multiple regression can also be used to adjust for confounding factors. For example, neonates with lower gestations will generally have lower birthweights, so we can use multiple regression to help determine if birthweight is a risk factor for NEC regardless of gestation. Where the dependent variable is binary rather than continuous, logistic regression is used, and where there is more than one independent variable, this is extended to multiple logistic regression. For example, you may want to look at the factors that increase the risk of a child with diabetic ketoacidosis (DKA) being admitted to PICU, including, for example, age, pH, blood glucose level, etc. Admission to PICU is the dependent variable, which is binary (i.e. a child is either admitted or not admitted), and the other factors are the independent variables. Statistics in epidemiology Many of the terms used in the statistics of epidemiology are covered in other chapters, but some important statistical concepts are covered here. Receiver operating characteristic curves In studies of diagnostic tests, sensitivity (the true positive rate) and specificity (the true negative rate) are key concepts (see Chapter 2, Epidemiology and public health). The sensitivity of a test is the proportion of people who have a disease who will test positive (i.e. how useful a test is at ruling people in), and specificity is the proportion of people without the disease who test negative (i.e. how useful a test is at ruling people out). The perfect test would have 100% sensitivity and 100% specificity (or 1, on a scale of 0–1). However, this is rarely the case. As you ‘modify’ the cut-off of a test, you may choose to have lots of ‘false positives’ (low specificity) in order to ensure that you do not miss any cases (high sensitivity) and vice versa. Receiver operating characteristic (ROC) curves plot sensitivity (the ‘true positive rate’) against 1 minus sensitivity (the ‘false positive rate’). The closer the area under the curve (AUC) is to 1.0, the closer the diagnostic test is to being 100% sensitive and 100% specific (Fig. 38.11). The correlation coefficient, r, (sometimes called Pearson’s correlation coefficient), provides a measure of linear association in normally distributed data. (If the association between the two variables is not linear, then other tests of correlation can be applied). For example, in examining the correlation between the risk to neonates of developing necrotizing enterocolitis (NEC) and gestation, gestation is the independent variable and number of neonates developing NEC is the dependent variable. If there is no correlation at all between the variables, then r = 0; if there is a perfect positive association (i.e. a positive correlation: as one increases, so does the other), r = +1; and a perfect negative association (i.e. a negative correlation: as one increases, the other decreases), r = −1. Scatter diagrams illustrate graphically the relationship between the variables and should always be constructed in the first instance. By convention, the ‘independent’ variable (e.g. gestation) goes on the xaxis and the ‘dependent’ variable (e.g. rate of NEC) on the y-axis. The dependent variable is the one that you are interested in seeing change with respect to the independent variable. In our example of NEC, we would expect a negative r value as we would expect rates of NEC to fall as gestational age rises. The correlation coefficient is calculated using an equation that calculates the ‘line of best fit’ for all of the data points. The value r 2 measures how successful the fit is in explaining the variation of the data, i.e. how much of the variation in one of the variables is due to its dependence on the other (the rest being due to other known or undetermined causes). It always takes a value between 0 and 1. If it is 0.74, for example in the NEC/gestation example above, then 74% of the variation between the neonates’ risk of NEC is accounted for by the gestation, the other 26% being due to other causes (e.g. birth weight, maternal chorioamnionitis, etc.). If the data is not normally distributed, including if there are significant outliers, or if the independent variables are not continuous, the non-parametric Spearman rank correlation method can be applied. In practice, if there are more than 30 datapoints, Pearson correlation can still be used (due to the test’s robustness). Correlation tells us how closely two variables are associated. The regression equation provides a measure of how much the value of the dependent variable y changes with a given change in the independent variable x. Since an equation is produced, it can therefore be used for prediction; for example, how much you would expect the risk of NEC to increase with every addition week of prematurity. Confidence intervals can be calculated for the regression line, and these are often displayed graphically. Caution should be used

38 736Statistics Survival analyses are used where the outcome being compared between groups is the time to a specified event, such as death (hence the term ‘survival’, though it need not be death, it could be any event, e.g. pregnancy, time to end of exclusive breastfeeding, failure of a transplanted organ, etc.). Although ‘time to event’ is a continuous variable, standard statistical analyses cannot be applied, because the times are not likely to be normally distributed and because by the time the data is analysed, events will not necessarily have occurred to all of the people in the study. These are called censored observations, and include those known to be still alive (or not yet having the event occurring), those known to have been alive previously but now lost to follow-up, and those who are known to have died, but of some unrelated cause. Kaplan–Meier curves display survival data graphically, showing cumulative survival at each time-point. Censored observations may be marked on the graph with short horizontal lines. The logrank test, so-called because it uses logarithms of the ranks of the data, can be used to compare the survival curves of different groups (Fig. 38.12). An extension of the logrank test is Cox regression, also known as the proportional hazards model, which can be used to analyse the effects of more than one risk factor on the outcome (e.g. survival). ROCs can also be used to help determine a useful cut-off point for a diagnostic test, as this will usually mean a trade-off between sensitivity and specificity. Figure 38.11 is taken from a study of children with suspected peanut allergy. We can see that in these children different tests have differing AUCs. Survival analysis Fig. 38.11 Receiver operating characteristic analysis. This example shows the receiver operating curves for basophil activation test (red), skin prick test (blue) and other tests (orange and green) to discriminate between allergy and tolerance in peanut-sensitized children. It shows how closely the test is 100% sensitive and 100% specific. (Adapted from Santos AF, et al. 2014 Basophil activation test discriminates between allergy and tolerance in peanut-sensitized children. doi:10.1016/j.jaci.2014.04.039. Published under a creative commons license.) 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 1 - Specificity Sensitivity 0.8 1.0 Fig. 38.12 Use of logrank test to show reduced probability of survival of adults ventilated using high frequency oscillation (HFOV) in early acute respiratory distress syndrome compared to control of low tidal volume and high positive end-expiratory pressure ventilation. The trial was stopped early. (Adapted from Ferguson ND, et al. High frequency oscillation in early acute respiratory distress syndrome. N Engl J Med 2013;368:795–805.) Probability of survival 1.0 0.8 0.9 0.7 0.6 0.4 0.3 0.1 0.5 0.2 0.0 0 15 30 45 60 Days since randomization P= 0.004 by logrank test No. at risk HFOV Control 275 273 169 181 98 92 54 54 26 39 Control HFOV Question 38.6 Representation of data A group of 200 babies whose mothers intend to breastfeed are followed up from birth to one year of age in order to determine at what age breastfeeding was terminated. How would such data best be depicted? Select ONE answer only. A. Dot diagram. B. Histogram of number of babies still breastfeeding with time. C. Kaplan–Meier curve. D. Scattergram of age at termination of breastfeeding by age. E. Scattergram of age at termination of breastfeeding by age with line of best fit. Answer 38.6 C. Kaplan–Meier curve. See below for explanation.

737 CHAPTER THIRTY-EIGHT Understanding the limits of statistics The well-known quote, attributed by the writer Mark Twain to the British Prime Minister, Benjamin Disraeli, ‘There are three kinds of lies: lies, damned lies and statistics’, is often employed in jest. However, one must be aware of the limits of statistics and appreciate that a ‘statistically significant’ result does not equate with ‘the truth’. Validity of the study Irrespective of the statistical methods used, a study may be subject to bias and confounding, both of which will affect its validity. It could also be a well conducted, valid study but performed in a population very different from your own, limiting its applicability. These considerations are discussed in more detail in Chapter 39, Evidencebased paediatrics and Chapter 37, Clinical research. Appropriate application of statistical tests Statistical tests are based on mathematical models with in-built assumptions, and applying them to data inappropriately will provide spurious or incorrect analyses. In practice, this is most likely to occur when researchers fail to understand the underlying mathematical reasoning behind the tests they are using. When planning a research study, it is vital to seek advice on statistical methods before starting to collect data. Association and causation Statistics can give us an idea of how closely two factors are associated or correlated, but that does not mean that one necessarily causes the other. For example, sales of barbecues may be inversely correlated with rates of admission to hospital with bronchiolitis, but that does not mean that lots of people buying barbecues prevents infants being admitted to hospital with bronchiolitis. Clinical and statistical significance A difference that is statistically significant may illustrate a genuine difference but not one that is clinically relevant or important. For example, a new type of preventative inhaler for asthma that reduces admissions by 0.7%. Further reading Bland M. An introduction to medical statistics. Oxford: Oxford University Press; 2000. Campbell MJ, Machin D. Medical statistics: a commonsense approach. 3rd ed. Chichester: Wiley-Blackwell; 1999 (See Chapter 10, Common pitfalls in medical statistics, for excellent summary of choice of statistical methods.). Campbell MJ, Swinscow TDV. Statistics at square one. 11th ed. Chichester: Wiley-Blackwell; 2009. CONSORT. <http://www.consort-statement.org>; [accessed 11.09.15]. HealthKnowledge. <http://www.healthknowledge.org.uk>; [accessed 11.09.15]. University College London. Statistics and research methodology. <https://epilab.ich.ucl.ac.uk/coursematerial/ statistics/index.html>; [accessed 11.09.15].

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LEARNING OBJECTIVES By the end of this chapter the reader should: • Understand basic principles of evidence-based medicine in order to implement it into clinical practice • Be able to formulate a clinical question • Be able to undertake a hierarchical search strategy using online search databases • Be able to critically appraise a randomized controlled trial (RCT) • Be able to interpret the commonly used measures of treatment efficacy • Be able to give an evidence-based presentation 739 CHAPTER THIRTY-NINE Introduction Evidence-based medicine (EBM) has now established itself as a key principle at the heart of modern clinical life. But what is it? In this chapter we will look at the principles of evidence-based medicine and how to practise it. When we make a clinical decision (e.g. should I give this wheezy child a nebulized or spaced β2 agonist?), we need to think about the patient and the overall outcome. There could be beneficial outcomes, but these should be weighed against the possibility of negative effects. As clinicians, we instinctively assess the chances of these outcomes, weigh them, and conclude on a course of action. If we are treating a child with an acute exacerbation of asthma, we may want to know what is the best mode of delivery for a β2 agonist? But what does best mean? Patient satisfaction? Ease of delivery? Fewer symptoms? Fewest side effects? Fewer admissions? Most cost-effective? Least expensive? For the clinician, the process of practising EBM can be difficult, time consuming, and (dare we say it) boring. In this chapter, these barriers will be tackled with examples of EBM in practice, revealing that the five minutes spent thinking this through may have saved you hours of work whilst improving the care provided to your patients. What is EBM? EBM was defined by one of the founders of the EBM movement, David Sackett, as ‘the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients’. But what does that mean in real terms? EBM is a shorthand term for five linked ideas: 1. Our practice should be a meeting of (Fig. 39.1): – Our clinical skills (including history, examination and diagnosis building) – Our patient’s own values, preferences and beliefs – The best available evidence 2. Seeking information/evidence is key to learning and should mostly be ‘just in time’ (see below), determined by an individual patient or population’s problem. 3. The care we offer should integrate quality clinical research with clinical experience, rather than relying on habit, dogma or tradition. 4. Searching and appraising evidence is only meaningful if it is applied to decisions and actions that benefit patients. 5. Clinicians, including those in training, should continuously evaluate their performance. Some misconceptions about EBM are listed in Box 39.1. Bob Phillips, Peter Cartledge Evidence-based paediatrics C H A P T E R 39

39 740Evidence-based paediatrics 2. Beneficence: When starting therapies you need the best available evidence to be certain that they have a meaningful benefit to the patient. 3. Non-maleficence: If a therapy or test has no meaningful benefit but is harming the patient (e.g. through side effects) then it should be stopped or not used in the first place. 4. Justice: Scarce resources must be fairly distributed. When rationing provisions, there must be good evidence that they are effective. If they are not, this resource could have been used elsewhere. Personal reasons for practising EBM There are also selfish reasons for wanting to practise EBM: 1. Personal development/examinations: EBM is included in the RCPCH Curriculum for Paediatric Training, which states that: ‘In addition to a detailed knowledge and understanding of diseases in children and young people, paediatricians must ensure they are up-to-date, conform with highest standards of practice, aim to promote evidence-based medicine where possible and audit practice (assessment standards 18–20).’ 2. Self-preservation: Clinicians are presented with huge volumes of research evidence. Without the time to read all this information, skills are required to filter out the good from the bad. 3. Reduced workload: Practising EBM will hopefully help patients to be diagnosed more effectively and get better quicker, therefore reducing workload. Meeting our information needs There are 11 new systematic reviews and 75 new trials published every day (Bastian 2010). It is impossible to keep up-to-date with all medical advancements, so long-term professional strategies are required to meet our learning and information needs. ‘Just in case’ information Reading this book is an example of just in case information: packing in a range of general nuggets to provide a good underlying understanding of paediatrics in order to be good clinicians. When taken to the extreme, it is the diligent, regular reading of a series of journals ‘just in case’ a particular case was to present. This is inefficient and induces guilt when you cannot manage it. ‘Just in time’ information This is a lifelong skill – the acquisition of information ‘just’ as you need it, e.g. reading a patient’s clinic notes before they arrive. In the EBM context, it is identifying information that will help us best manage our patients Why practise EBM? Altruistic reasons for practising EBM Practicing EBM improves patient care. There really should be no other incentive. Considering this in terms of the four pillars of medical ethics can be helpful: 1. Autonomy: All patients have the right to make decisions about their own care. This is central to EBM. Their values, preferences and beliefs should always be taken into account when making decisions. Fig. 39.1 Key components of EBM. Evidence-based medicine is the convergence of clinical skills (including history, examination and diagnosis building), the patient’s values, preferences and beliefs and the best available evidence. Patient’s values (and/or beliefs) Best available evidence Clinical skills EBM Box 39.1 Misconceptions of EBM There are a series of misconceptions regarding EBM which should to be dismissed as untrue: • EBM belittles or removes clinical expertise • EBM ignores patients’ values, preferences and beliefs • EBM promotes ‘cookbook’ medicine • EBM is simply a cost-cutting tool • EBM is only for tertiary hospital clinicians/ specialists • EBM is exclusively limited to undertaking research rather than using research findings • EBM leads to the renunciation of therapies when there is an absence of evidence from RCTs • EBM is performed by statisticians sitting in offices and not by clinicians working in wards, clinics or operating theatres.

741 CHAPTER THIRTY-NINE essential skill. A well-framed question must be directly related to the patient and structured in order to search for a relevant and precise answer. Framing a clear question: PICO questions A popular method for framing a clinical question is the PICO method (Box 39.2): • P – Patient, Pathology, Problem or Population: What are the key features that describe the patient or population? Be specific. • I – Intervention or Interest: Be specific about the intervention, test or risk factor you are considering. • C – Control or Comparison: What would be appropriate alternatives? This may be placebo or more often currently used treatments. • O – Outcomes: Consider patient-oriented short-term and long-term outcomes; remember negative effects too. Avoid surrogate markers (e.g. improved CRP). by seeking rapid answers to specific queries. Research has shown that a doctor in training will have two unanswered questions for every three patients they consult (Green et al 2000). An inquisitive clinician could have many more questions. As our clinical experience improves, this number is unlikely to change, but the content may become more focused. The five steps of EBM The practice of EBM is a multi-step process. Each of these steps (Table 39.1) requires individual skills and practice, though some resources will allow us to shortcut some of these steps. Throughout this chapter, there are examples of the ‘five steps’ in practice (see also Tables 39.3, 39.4, 39.6 and 39.9). Step 1: Asking a question It is all too easy to practise medicine without asking questions, as asking questions exposes potentially embarrassing gaps in our knowledge. The first step of EBM is to address this challenge and admit ignorance or uncertainty, then convert our information needs into answerable questions. This means having an inquisitive mind. Looking at the anatomy of enquiry, ask initially ‘What sort of question am I asking?’ If it is a clinical question then it can be grossly categorized as ‘foreground’ or ‘background’. Background questions are broad, and are often ‘what is’ or ‘what causes’ type questions, e.g. ‘What causes asthma in childhood?’ Foreground questions are specific and pointed, and can be fitted into a ‘PICO’ framework (patientproblem, intervention, comparison, outcome). This art is known as ‘framing a clear question’ and is an Table 39.1 Steps of EBM Step Skills required Barriers Consequences of inadequate implementation Step 1: Asking a question • Good clinical skills • Inquisitive nature • Specific PICO questions • Time to formulate questions • Excessive reliance on senior colleagues • Out-of-date practice • Dangerous practice Step 2: Acquiring information/evidence • Literature searching and storage • Lack of good quality research evidence • Inadequate database searching skills • Biased evidence found and inappropriate practice Step 3: Appraising the information/evidence • Systematic appraisal techniques • Critical mind-set • Lack of an appraisal tool (i.e. DIY appraisal) • Biased evidence applied to wrong patients Step 4: Applying the evidence to your patient • Sensitivity • Good communication • Understanding study methods, statistics and applying research to individual patients • Perceptions of curtailing clinical freedom • Conflict of opinion • Out-of-date practice • Dangerous practice Step 5: Assessing your performance • Self-awareness • Lack of time to reflect on learning or practice change (e.g. ‘audit’) • Missed opportunities to focus personal development Box 39.2 Top tips for putting EBM into practice (framing questions) • Search at the point of care. • If you are too busy to search immediately, write your questions down (e.g. a notebook, smartphone or portfolio). • Present your questions to your colleagues for feedback. • Write educational prescriptions on ward rounds (http://www.cebm.net/wp-content/ uploads/2014/04/educational_prescription _1.pdf)

39 742Evidence-based paediatrics Where to search There is probably no correct answer to the question, ‘where is best to search?’ You are likely to find databases which you are more comfortable using. Trip®, the Cochrane library® or PubMed® are good databases to be familiar with (Box 39.3). Hierarchical searches A hierarchical search aims to look for the best quality evidence first, and then work downwards if insufficient research is discovered (Fig. 39.2). If you are searching in a clinical environment, a database such as Trip® will often find results quickly (Table 39.3) and present them in evidence type. If this fails, the Cochrane library and DARE website should be searched (Table 39.4). If this does not yield any results, then PubMed’s ‘Clinical Queries’ is the next best port of call followed by a search for primary resources in PubMed. Choosing the right outcomes to search is incredibly important. If you have a strong opinion on a topic, it is likely to be drawn from experience, e.g. always giving inhaled salbutamol to wheezy children in A&E. But what do we actually want to know about our intervention? Will it stop the patient dying? Will it keep them out of hospital for longer? Will they feel better? These are all important questions. Step 2: Acquiring information/ evidence The aim of this step is the acquisition of good quality information/evidence to answer your skilfully constructed question. This can be difficult, but with practice and a few tips on where to look, it gets easier. The process therefore follows three steps: 1. Converting your PICO question into searchable terms 2. Searching for secondary sources (e.g. guidelines, Cochrane/DARE, etc.) 3. Finding primary sources if secondary sources are not available (e.g. PubMed). (See http://www.youtube.com – search for ‘PubMed Advanced Search Builder’ in YouTube for a 3-minute tutorial on how to search in PubMed.). Getting your search right is both an art and a science. A good search is both sensitive and specific. A sensitive search will not miss any relevant papers, a specific search will not have too many irrelevant articles. Converting PICO questions into searchable terms Once you have written a sound PICO question you must convert the question into searchable terms. To form a search strategy: 1. Convert each arm of your PICO into search terms (including alternate spellings, synonyms, and truncations). 2. Search for the correct MeSH term (if you are using a database that does not automatically map). ‘MeSH’ is essentially the National Institute of Health (NIH) thesaurus of medical terms that guides towards the ‘correct medical term’. This means a searching clinician is able to find the relevant research, including when the papers’ authors have not used the ‘preferred’ medical term). (See http://www.nlm.nih.gov/mesh/; Tutorial: http:// www.nlm.nih.gov/bsd/viewlet/mesh/searching/ mesh1.html). 3. Combine the search terms using the correct Boolean operators (Table 39.2). Table 39.2 Boolean operators Term Search description OR (Infant OR child) will find all articles/documents containing at least one of these keywords NOT (Infant NOT child) will find articles/documents containing the keyword infant but exclude those also including the keyword child AND (Infant AND child) will find articles/documents containing both of these keywords * (Truncation) Infant* will find articles/documents containing the keywords infant OR infants OR infantile OR infancy, etc. Box 39.3 Key website resources to add to your bookmarks/favourites toolbar Practising EBM need not be time-consuming. Add the following websites to your bookmarks in order to speed up your hierarchical search strategy: • Trip database (www.tripdatabase.com) • The Cochrane library MeSH search page (http:// onlinelibrary.wiley.com/cochranelibrary/search/ mesh/quick) • The Cochrane library advanced search page (http://onlinelibrary.wiley.com/cochranelibrary/ search/) • The DARE database (http://www.crd.york.ac.uk/ crdweb/) • PubMed Clinical Queries (http:// www.ncbi.nlm.nih.gov/pubmed/clinical) • PubMed for primary searches (http:// www.ncbi.nlm.nih.gov/pubmed)

743 CHAPTER THIRTY-NINE EBM. Though double-blinded RCTs are often considered the ‘gold standard’ for establishing treatment effects, they will not be the best at answering questions about diagnosis, prognosis and/or harm. Secondly, there may not be RCTs available to answer certain treatment questions. There are barriers to research in child health and this often means that good quality information is unavailable (see Box 37.1). If no literature is available to answer your question, then an email/discussion with an expert may be the ‘current best evidence’ for your clinical question. Do not negate the importance of qualitative research, which significantly adds to the wealth of evidence available on particular subjects. Levels of evidence An understanding of ‘levels of evidence’ will enable you to search for the ‘current best evidence’. Evidence hierarchies are often used to describe the relative If performing a more detailed search (e.g. for guideline development) then an extensive search should be undertaken, preferably with the help of an information specialist at your local library. Boolean operators Almost all database search engines use Boolean operators (see Table 39.2) to combine search concept. Search filters When searching PubMed, a search filter can be used to find evidence relating to different areas of practice (e.g. therapy or diagnosis); these are handily available via the PubMed ‘Clinical Queries’ page. This can quickly make the search more sensitive and specific. What is ‘current best evidence’? A common misconception regarding EBM is that only RCTs or systematic reviews constitute the ‘evidence’ in Fig. 39.2 Hierarchical search strategy. (*BET = Best Evidence Topic.) Quick case-based/ clinical search Detailed search e.g. SR or guideline development Enter search strategy into Cochrane library (including MeSH terms), see below (BETs*) Appraise the evidence, apply if good quality and appropriate Consider: Is there a nationally approved clinical guideline on this subject? Search: Tripdatabase.com Pre-appraised evidence (e.g. UpToDate.com)? Appraise the evidence and apply if good quality and appropriate Cry Search: Pubmed Limit to study type Nil found or not good quality/ not appriopriate Nil found or not good quality/ not appriopriate Nil found or not good quality/ not appriopriate Nil found or not good quality/ not appriopriate Appraise this guideline and apply if good quality and appropriate Search: Pubmed Clinical Queries (systematic reviews) Appraise article and apply if good quality and appropriate

39 744Evidence-based paediatrics which underpins a management decision. This concept is what underpins the ‘grades of recommendation’ found in guidelines. A large number of methods for describing the quality of evidence behind recommendations have been created. Most systems have the same principles at their heart. The guideline developers are first asked to assess the methodological quality of the studies, then developers are asked to evaluate the whole of the evidence and how it applies to the recommendation at hand: this gives the ‘grade (or strength) of recommendation’. The systems vary in how the study quality is assigned, which factors are included in assessing ‘a strength’ of recommendation, and if different axes are used for different types of question (for example, therapeutic, diagnostic, and prognostic). The Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) system has been developed by the international GRADE Working authority of various types of medical research. There is no individual, universally accepted hierarchy, and a tiny, very poorly conducted RCT will be less use than a well-constructed cohort study. A simple hierarchy has been described by Professor Greenhalgh in How to read a paper: 1. Systematic reviews and meta-analyses 2. Randomized controlled trials with definitive results 3. Randomized controlled trials with non-definitive results 4. Cohort studies 5. Case-control studies 6. Cross-sectional surveys 7. Case reports GRADE A better approach to ‘levels of evidence’ might be to describe how convincing the totality of evidence is Table 39.3 Best evidence topic (BET) – treatment Cates CJ, et al. Holding chambers (spacers) versus nebulizers for beta-agonist treatment of acute asthma. Cochrane Database Syst Rev 2006;(2):CD000052. Treatment – acute exacerbation of asthma Scenario: A 6-year-old child is admitted with a moderate exacerbation of asthma. He has been given a nebulizer of salbutamol in the A&E department and needs admission for a trial of inhaler and holding chamber (spacer). You wonder if he could have been given the inhaler in the A&E and the child sent home sooner? Step 1: Asking a question (PICO): In a child with a moderate exacerbation of asthma [patient], is inhaled, spaced, salbutamol [intervention] as effective as nebulized [comparison] β2 agonist in terms of time to resolution of symptoms, likelihood of admission and deterioration [outcomes]? Step 2: Acquiring evidence: Search terms: Asthma AND spacer AND nebulizer. Databases: Trip database – 110 results. Best result – Cochrane review. Step 3: Appraising the evidence: Study group Intervention Study type Outcomes Key results Comments 2295 children in 27 trials from emergency and community setting. Any β2 agonist given by any nebulizer versus the same β2 agonist given by metered-dose inhaler with any spacer. Cochrane systematic review. Only RCTs considered for review. Primary outcomes: Admission to hospital or duration of stay. Secondary outcomes: Duration in emergency department, change in respiratory rate, blood gases, pulse rate, tremor, symptom score, lung function, use of steroids, relapse rates. Meta-analysis of probably heterogeneous results. Spacer versus nebulizer relative risk of admission was 0.72 (95% CI: 0.47 to 1.09). In children, length of stay in the emergency department was shorter with spacer, mean difference of −0.53 hours (95% CI: −0.62 to −0.44 hours). Clear primary and secondary outcome measures. Particular emphasis on the allocation concealment, which in general appears poor in most papers. Commentary: Acute exacerbation of asthma is common in both hospital and primary care. The airways are narrowed due to mucosal oedema, hypersecretion and bronchospasm. β2 agonists have been used successfully to relieve the bronchospasm. This paper included RCTs including adults and children. It can be argued that adults and children differ in their ability to use the devices being tested. Therefore, the results for adults and children were separated for each outcome. In this systematic review it was found that the method of delivery of β2 agonist did not appear to affect hospital admission rates but did significantly reduce the duration of stay in the emergency department. Step 4: Applying the evidence (the clinical bottom line): 1. No outcomes were significantly worse with spacers, and in most cases spacers can be substituted for nebulizers to deliver β2 agonists in acute asthma (excluding life-threatening asthma). 2. Spacers offer a significant advantage in terms of time spent in emergency department, oxygenation and side effects. 3. Spacers should be routinely used instead of nebulizers to administer β2 agonists for acute asthma in children and young people. (Strong recommendation, high quality evidence.) Reference:

745 CHAPTER THIRTY-NINE trade-off between benefits and risks is less certain, either because of low-quality evidence or because high-quality evidence suggests that benefits and risks are closely balanced, weak recommendations are made. (For further details, see GRADE working group: http://www.gradeworkinggroup.org) ‘But more research is needed’ How can we decide if questions really do ‘need’ more research? It may be worth thinking of how likely benefits and harms may be, what the importance of these outcomes is and, finally, how much would you consider reasonable to pay for the answer? For example, Group. GRADE is a transparent, structured process for developing and presenting summaries of evidence and is integrated into Cochrane reviews and NICE guidelines. The GRADE system classifies quality of evidence as high, moderate, low, and very low (Table 39.5). Grade of recommendations The GRADE system goes on to combine this quality of evidence with a judgement about the balance of risks and benefits to produce strong or weak recommendations. When the benefits of an intervention clearly outweigh its risks and burden, or clearly do not, a strong recommendation is produced. When the Table 39.4 Best evidence topic (BET) – diagnosis Thangaratinam S, et al. Pulse oximetry screening for critical congenital heart defects in asymptomatic newborn babies: a systematic review and metaanalysis. Lancet 2012;379(9835):2459–64. Diagnosis – cyanotic heart disease Scenario: You start at a new hospital where you undertake ‘postnatal checks’ on newborn infants. You notice that in this hospital you do not need to perform post-ductal pulse oximetry testing. You discuss this with your consultant, who asks you to find out more exact details on the benefits of this. Step 1: Asking a question (PICO): In an asymptomatic newborn infant [patient], does post-ductal pulse oximetry [intervention] increase the number of infants correctly identified with congenital heart disease or reduce mortality rates [outcomes]? Step 2: Acquiring evidence: Search terms: (Infant, newborn OR infant* OR newborn OR ‘newborn infant’ OR neonat*) AND (heart defects, congenital OR congenital heart defect* OR Defect*, congenital heart OR heart, malformation of OR heart abnormalit* OR congenital heart disease OR cyanotic heart disease OR cyanotic heart defect OR congenital heart malformation) AND (oximetry OR oximetry, pulse OR blood gas monitoring, transcutaneous OR oximetry, transcutaneous OR oximetry, transcutaneous OR saturation*, oxygen OR oxygen saturation*) Databases: Cochrane: 164 results, 4 non-Cochrane reviews including below meta-analysis. Step 3: Appraising the evidence: Study group Intervention Study type Outcomes Key results Comments 13 eligible studies with data for 229,421 asymptomatic newborn babies. 118 infants with critical congenital heart defects. 748 false positives. 33 false negatives. Pulse oximetry. 60% of studies used foot alone (postductal). Systematic review and meta-analysis of 12 cohort and 1 case-control study. Detection of critical congenital heart defects. Sensitivity 76.5% (95% CI 67.7–83.5), specificity was 99.9% (99.7–99.9), false positive rate 0.14 (0.06–0.33). Likelihood ratio positive 549 (238–1195), likelihood ratio negative 0.24 (0.17–0.33). Lower false positive rate if oximetry >24 hours (p=0.0017), but no effect on sensitivity. Clear description of search strategy. No statistical description of heterogeneity. Significant publication bias was reported. Commentary: Screening for critical congenital heart defects in newborn babies can aid early recognition, with the prospect of improved outcome. In this case, the new doctor was not interested in an individual patient but a population. As the search had the potential to lead to widespread change in the clinical assessment of all newborns, the search for evidence needed to identify the most relevant and highest quality available. The search was therefore very comprehensive. Though this systematic review found that pulse oximetry is highly specific for critical congenital heart disease, it does not look at broader outcomes. For example, do infants who have their diagnosis made earlier using screening have better long-term outcomes (e.g. mortality)? This would be an important consideration when balancing the cost (equipment, time, etc.) of implementing such a screening tool. Step 4: Applying the evidence (the clinical bottom line): 1. Pulse oximetry is a non-invasive test that is easy to perform with high accuracy and could identify other disorders such as septicaemia or symptomatic pulmonary hypertension. 2. The false-positive rate for detection of defects was significantly lower when pulse oximetry was done after 24 hours 3. ‘In view of the many babies that have now been tested with pulse oximetry, further research in this area is unlikely to produce substantially different findings’ 4. Routine pulse oximetry should be undertaken on neonates prior to discharge, ideally after 24 hours. (Strong recommendation, moderate quality evidence.) NB: The * in the search terms is referring to search truncations to ensure optimum sensitivity of the search (see Table 39.2). Reference:

39 746Evidence-based paediatrics its use. If you are aiming to moderately improve someone’s well-being with a side-effect free intervention, the evidence can be much weaker. A summary of top tips for searching for evidence is shown in Box 39.4. Step 3: Appraising the information/ evidence Once you have acquired your information/evidence, you must critically appraise it for its importance and validity. The skills of appraisal, like all skills, take time to learn and improve. To continue to develop your skills, it needs to be integrated into what you do on a regular basis. Appraisal is not the overzealous criticism of research to prove that it is flawed and therefore not applicable. It is rather the process of teasing out important details which will have a bearing on whether you use a test, treatment, etc., and just how useful they are likely to be based on this research. There are four main considerations when appraising any evidence: 1. Relevance 2. Validity 3. Importance 4. Applicability. An appraisal checklist is shown in Box 39.5 and an example of appraising the evidence in Table 39.4. 1. Relevance This stage is largely intuitive. Using your PICO question will enable you to focus your attention and quickly decide whether the study is assessing the relevant intervention in the appropriate patients and reporting the important outcomes. In Table 39.6, we can see quickly that the paper identified is relevant to our clinical scenario and the question that the patient’s mother has asked of us. 2. Validity Can you believe the results presented? This question delves into the biases that may be produced by ‘Do antihistamines reduce itch in chickenpox?’ versus ‘Are oral antibiotics as effective as intravenous antibiotics in children with neurodisability and pneumonia?’ Do we really need more research to prove the use of antihistamines in chickenpox? If you compare this with the implications of ineffective management of a significant lung infection in a disabled child? Such value judgements are important; they will have different answers from different perspectives; they will be subject to political influences from pressure groups; being aware of them might stop us from frequently expounding ‘more research is needed’. Conversely, there are times when a ‘lack of significance’ can be falsely interpreted that an intervention is not effective, when in reality the methods employed have not been powerful enough to find the truth to the question posed. In this case more research is needed. Table 39.5 GRADE quality of evidence (with definitions) Grade Definition High Further research is very unlikely to change our confidence in the estimate of effect Moderate Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate Low Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate Very Low Any estimate of effect is very uncertain Box 39.4 Top tips for putting EBM into practice (searching) • Search at the point of care. • Use search engines that you are comfortable with. • Ask a hospital librarian to sporadically check your searches. • Register (i.e. create an account) with your favourite search engine to allow you save searches for later review. Key point – Is it possible for health professionals in developing countries to access journal articles? Yes. HINARI provides free or very low cost online access to the major medical journals to local, not-for-profit institutions in developing countries (www.who.int/hinari/en/). What to do when you find no evidence? The problem of ‘not enough evidence’ is a common one we face as paediatricians and one that will frequently frustrate our attempt to implement evidence into practice. It is a big problem, so what do we do in this situation? The issues depend on: • What is the goal of treatment? • What are the potential benefits and harms? • The uncertainty about our estimates of these • The ‘regret’ associated with getting the decision wrong. If you have a potentially toxic drug that you are using in an asymptomatic population to prevent a fatal disease, you need strong evidence to recommend

747 CHAPTER THIRTY-NINE Box 39.5 Appraisal checklists An appraisal checklist is a useful tool for being systematic in appraising the quality of a paper and applying it to a patient. Different checklists are available for different research methodologies. There are several options freely available on the internet. These can be used for: • Becoming more familiar with appraisal techniques. Avoiding DIY appraisal. • Formally appraising literature for guideline development. • Appraising literature for a journal club. Oxford University Centre of Evidence-based Medicine provides ‘appraisal checklists’ for: • Systematic reviews • RCTs • Diagnostic studies • Prognosis studies These can be found free at http://www.cebm .net/critical-appraisal/ Table 39.6 Best evidence topic (BET) – treatment Treatment – pneumonia Scenario: A previously well 4-year-old child presents with fever and signs of respiratory distress. Community-acquired pneumonia (CAP) is confirmed radiologically. He is mildly tachypnoeic but has no oxygen requirement. His mother asks you if it is really necessary for him to stay in hospital for intravenous antibiotics? Step 1: Asking a question (PICO): In a pre-school aged child with pneumonia [patient], are oral antibiotics [intervention] as effective as intravenous antibiotics [comparison] for time to resolution of symptoms, rate of hospital admission, length of stay and rate of complications [outcomes]? Step 2: Acquiring evidence: Search terms: (child, preschool OR pre-school child* OR preschool child*) AND (Pneumonia OR lower respiratory tract infection OR LRTI) AND (antibiotic OR anti-bacterial agents) AND (oral OR administration, oral) AND (intravenous OR administration, intravenous). Databases: Cochrane – no results. PubMed (Limit: randomized controlled trial) – 15 results. Step 3: Appraising the evidence: Study group Intervention Study type Outcomes Key results Comments 246 children (1.5–5.4 years) with CAP admitted to eight hospitals in UK Oral amoxicillin versus intravenous benzylpenicillin Multicentre randomizedcontrolled equivalence study 1. Time for temperature to decrease to <38°C for 24 hours 2. Cessation of O2 requirement 3. Length of stay 4. Complications No significant difference in temperature resolution, O2 requirement or complications Length of hospital stay was shorter in the oral group (1.77 versus 2.1 days, p=0.001). Equivalent demographics between groups. Clear inclusion criteria. Concealment of allocation. Intention to treat. Non-blinded. Commentary: Pre-school children admitted with CAP are often commenced on intravenous antibiotics. This treatment carries the risk of harm such as painful cannulation, extravasation, thrombophlebitis, and prolonged hospital stay. If oral antibiotics were found to be equally effective, these harms could be avoided. When searching for papers to answer this PICO question, the outcomes would be very important, e.g. a significant outcome would be ‘development of empyema’. Speed of radiological resolution (with no clinical measures) would not be a significant outcome (it is a surrogate marker). In this paper: children admitted to both tertiary and district general hospitals were included. Complications and treatment failure were similar in both groups. Of the 246 children, three developed empyema, all were in the intravenous arm of the study. We can therefore give this mother the advice that her child can be safely treated at home with oral antibiotics. She should be counselled that if her son’s respiratory difficulties were to deteriorate, she should return immediately to the hospital. Step 4: Applying the evidence (the clinical bottom line): 1. Oral antibiotics are as effective as intravenous antibiotics in the treatment of CAP in pre-school aged children. (Strong recommendation, high quality evidence) 2. In all but the sickest of children, oral antibiotics should be the first line treatment for CAP (in combination with observation of tolerance and symptoms) (Strong recommendation, high quality evidence). NB: The * in the search terms is referring to search truncations to ensure optimum sensitivity of the search (see Table 39.2). Reference: Atkinson M, et al. Comparison of oral amoxicillin and intravenous benzyl penicillin for community acquired pneumonia in children (PIVOT trial): a multicentre pragmatic randomised controlled equivalence trial. Thorax 2007;62:1102–6.

39 748Evidence-based paediatrics different elements of study design. Despite all the best intentions, a research study might not produce results that are ‘based upon truth’. A good understanding of research methods (see Chapter 37, Clinical research) is required to fully appraise the validity of any evidence you have found and apply it appropriately to your patient. Assessing validity involves asking critical questions of the way the research was performed. For example, if appraising an RCT, you may need to ask critical questions such as: • Was the assignment of patients to treatments appropriately randomized? • Were all of the patients who entered the trial properly accounted for at its conclusion? • Were patients, health workers and study personnel ‘blind’ to treatment? • Were the groups similar at the start of the trial? • Aside from the experimental intervention, were the groups treated equally? Different study designs will have different critical questions to ask. Hence the importance of using appraisal tools (see Box 39.5). Validity of common trial designs A number of different quantitative trial designs exist – systematic review and meta-analyses (Box 39.6), randomized controlled trial (RCT), cohort, case-control, cross-sectional surveys. Whilst the double-blinded randomized controlled trial is frequently seen as the gold standard approach for investigating a new treatment intervention, each study design can be the most appropriate choice for a given clinical setting (see Table 37.2). Each trial design has individual limitations and biases which must be borne in mind when interpreting the results and applying them to your patient. Reducing bias When appraising a paper, one should assess if the researchers have used common techniques to reduce bias where possible. Common techniques are: • Allocation concealment: This refers to the security of the randomization. Before a patient is offered a place on a trial, there should be no way of the investigator knowing which treatment the patient will receive. Why conceal? A ‘subconsciously’ biased investigator keen on intravenous antibiotics (see Table 39.6) may put sicker Box 39.6 Cochrane reviews Cochrane reviews are systematic reviews of primary research in health. They investigate the effects of interventions for prevention, treatment and rehabilitation and the accuracy of diagnostic tests. These reviews are unparalleled in terms of quality, volume and scope. What also sets Cochrane reviews apart is the continuous effort to update them. The logo of the Cochrane Collaboration (see above, Copyright © The Cochrane Collaboration) provides an excellent example of how systematic reviews can change the widescale practice of clinicians. It is based around the 1989 Cochrane review, which asked the questions: “In a pregnant woman presenting in premature labour [patient], does a short course of a corticosteroid [intervention] compared with placebo [control] reduce respiratory morbidity and mortality in the premature infant? A question now rarely asked on neonatal units around the world. The ‘forest-plot’ at the centre of the logo reveals the results of the seven RCTs, each being represented by a horizontal line. The diamond represents their combined results. If a horizontal line crosses the vertical line, it indicates that that particular study found no difference between intervention (steroids) and control (placebo). The position of the diamond to the left of the vertical line indicates that the treatment studied is beneficial. Conversely, a diamond to the right would reveal a treatment that did more harm than good. This review brought home the message of the huge gap between available evidence and clinicians’ awareness of its existence. Because no systematic review of these trials had been published until 1989, most obstetricians had not realized that the treatment was so effective. As a result, tens of thousands of premature babies had probably suffered and/or died unnecessarily. This is just one of many examples of the human costs resulting from the failure to perform systematic, up-to-date reviews of RCTs and to apply them to clinical practice. References: www.cochrane.org; www.thecochranelibrary.com

749 CHAPTER THIRTY-NINE Question 39.1 Treatment of eczema herpeticum A 7-year-old child with chronic atopic eczema is admitted with eczema herpeticum. You want to start acyclovir in combination with an antibacterial agent. His mother is concerned about the number of courses of antibiotics he has had and wants to know if this is really necessary. You find a multicenter retrospective cohort study of 2000 children ages 2 months to 17 years admitted with eczema herpeticum which reveals the following: Acyclovir with oral antibiotics Acyclovir without oral antibiotics Hospitalization 40 60 No hospitalization 960 940 What is the number needed to treat (NNT) to prevent hospitalization? Select ONE answer only. A. 20 B. 30 C. 40 D. 50 E. 60 children into the IV group as he wants these children to get better faster, which might make intravenous antibiotics look ineffective in the research project. • Intention to treat analysis: For whatever reason, the patients’ data should always be analysed according to the group allocated at randomization. Randomization is the point in the study when patient characteristics (and confounding variables) are matched. There may be a systematic reason (connected to the treatment) why certain patients cannot comply with the protocol. In Table 39.6, if children on oral antibiotics deteriorated and were changed onto intravenous antibiotics, then they should be analysed in the ‘oral’ arm of the study, not the intravenous arm. • Blinding (or masking): The process of obscuring to patient, observer, or both the treatment to which they are allocated. It relies on two therapies having no clearly discernible effects to ‘unmask’ the allocation. In Table 39.6, it would be impossible to blind the patients to which treatment they were going to receive as one requires cannulation. 3. Importance Are the results likely to result in an important improvement/harm/correct diagnosis? A good understanding of statistics (see Chapter 38, Statistics) is required to fully appraise the significance of any evidence you have found and apply it appropriately to your patient. When appraising the ‘significance’ of the results, you will need to ask critical questions, such as: • How large was the treatment effect? • How precise was the estimate of the treatment effect? (i.e. What are its confidence limits?) Similar to validity, different study designs will need different critical questions regarding significance. Measurements of efficacy If a dichotomous outcome is present (e.g. having cerebral palsy or not having cerebral palsy; Table 39.7), then Table 39.7 Presentation of dichotomous results (TOBY study, cerebral palsy in cooled survivors of hypoxic–ischaemic encephalopathy; see Box 37.13) (Data from Azzopardi DV et al, New England Journal of Medicine 2009:361;1349–58.) Event (Cerebral palsy) No Event (No cerebral palsy) Totals Intervention group a b a + b (Moderate cooling) (33) (87) (120) Control group c d c + d (Standard care) (48) (69) (117) Total a + c (81) b + d (156) a + b + c + d (237) a variety of measures can be generated. Of these, those that give information of the risk reduction (relative, absolute, or its inverse, the number needed to treat) are useful. We have used the groundbreaking TOBY study described in Chapter 37, Clinical research, to demonstrate these terms (Table 39.7). Table 39.8 gives the definitions of some of the common measurements of efficacy derived from dichotomous results of RCTs. Answer 39.1 D. 50 The NNT = 1/Absolute Risk Reduction (ARR) (see Table 39.8). Therefore, to work out the NNT first of all you need to calculate the absolute risk reduction: 60/(940 + 60) − 40/(960 + 40) = 0.02 (20 per 1000 children). Therefore the NNT in this case = 1/0.02 = 50.

39 750Evidence-based paediatrics Table 39.8 Key statistical terms (using the example of cerebral palsy in survivors of hypoxic–ischaemic encephalopathy (HIE) who were cooled or not; see Table 39.7) Term Calculation Explanation TOBY example In lay terns Risk Risk (in intervention group) = a/(a+b) Risk (in control group) = c/(c+d) Number of ‘events’ compared to all the ‘events and no events’ together. Risk (in cooling group) = a/(a+b) = 33/120 = 0.275 Risk (in control group) = c/(c+d) = 48/117 = 0.41 In all the infants who were cooled the risk of cerebral palsy is 28%, whereas the risk for all those who are not cooled it is 41% Relative risk (RR) (risks ratio) RR / / = + + a a b c c d ( ) ( ) How many times more likely it is that an event will occur in the treatment group relative to the control group: RR < 1 Effective RR = 1 Ineffective RR > 1 Harmful RR following cooling = 0.275/0.41 = 0.67 (67%) Infants who were cooled had two-thirds of the chance of developing cerebral palsy. p-values Dependent upon the statistical test employed. How likely it is that the result you found is due to chance. The relative risk of cerebral palsy in survivors of HIE is 0.67 (p=0.03) ‘I am 97% sure that cooling an infant will reduce the chance of them developing cerebral palsy. There is a 3 in 100 possibility that these findings occurred completely by chance’ (if p=0.03) Confidence intervals Dependent upon the statistical test employed. The range within the true effect is ‘likely’ to fall. The likelihood is described using p-value. RR confidence interval = 0.47– 0.96 (p=0.03) If this research project were repeated 100 times, 97 of the studies would give a relative risk within the range of 0.47–0.96. ‘I am 97% confident that the true effect size lies in this range.’ Odds Odds (in intervention group) = a/b Odds (in control group) = c/d Number of ‘events’ compared to ‘no events’ in one of the groups. Odds (in cooling group) = a/b = 0.38 (38:100) Odds (in control group) = c/d = 0.70 (70:100) In the infants who were cooled, for every 38 infants who developed cerebral palsy there were 100 infants who did not develop cerebral palsy. In the infants who were not cooled, for every 70 infants who developed cerebral palsy there were 100 infants who did not develop cerebral palsy. Odds ratio (OR) OR = = ⋅ ⋅ a b c d a d b c The ratio of the odds of an event in the intervention group to the odds of an event in the control group OR < 1 Effective OR = 1 Ineffective OR > 1 Harmful OR following cooling = 0.38/0.70 = 0.54 There is a 46% reduction in the likelihood of developing cerebral palsy in those who were cooled compared to those who were not. Absolute risk reduction (ARR) ARR / / = + − + c c d a a b ( ) ( ) The absolute difference in the rates of events between the two groups. The ARR gives an indication of the baseline risk and treatment effect: ARR > 0 Effective ARR < 0 Harmful ARR = 0.410 − 0.275 = 0.135 (14%) Cooling an infant following HIE reduces the risk of cerebral palsy by 14% Control event rate (CER) = + c c d( ) Same as risk (control group) CER = c/(c+d) = 48/117 = 0.41 Same as risk (control group) Relative risk reduction (RRR) Relative Risk Reduction Absolute Risk Reduction CER = The reduction in rate of the outcome in the intervention group relative to the control group RRR = 0.135/0.41 = 0.33 (33%) If you cool your newborn baby having suffered HIE, you reduce their risk of cerebral palsy by 33%. Number needed to treat NNT ARR B = 1 Number of patients needed to treat in order to prevent one adverse event: NNT ≥ 1 Effective NNT ≤ −1 Harmful NNT = 1/0.135 = 7.4 For every seven infants who we cool after HIE, we will prevent one case of cerebral palsy

751 CHAPTER THIRTY-NINE 4. Applicability Can I apply the results so that my patients receive similar benefits to the study participants? The question of applicability is the natural bridge into Step 4: Applying the evidence to your patient, and involves asking questions such as: • Is my patient similar to the patients studied? • Were all clinically important outcomes considered? • Do the potential harms of treatment/investigating outweigh the benefits? • Can I offer the treatment/test? Step 4: Applying the evidence to your patient The evidence must next be applied to the individual patient taking into account the patient’s and family’s own values, preferences or beliefs. Part of our appraisal process (Step 3) is critically analysing the paper in the context of our own patient. We should ask ourselves several critical questions, which are considered below. Is my patient similar to those patients studied? In most cases, the treatments we use have not been tested in trials where the populations matched ours exactly. In paediatrics, often the evidence is translated from adult studies. The outcomes recorded may only be surrogates, rather than clinically important changes. In order to use the best evidence in practice, we must consider how they apply to our patients. To do this we should: • Ask if there are biological differences between the populations. In terms of febrile seizures (Table 39.9), does your patient have developmental delay that would make him/her distinctly different from the population studied? • Consider whether differences in psychology, social setting, or economy will stop the data being applicable. If there are significant differences in economic or social setting, it may strongly affect the results. For example, a child with a previous febrile seizure, who attends nursery, may have a Questions 39.2–39.4 Antenatal steroids for preterm birth You are asked to speak to a pregnant woman who is in preterm labour. She is very hesitant to receive steroids as she wants a ‘natural birth’. She wants to know just how beneficial steroids will be to her infant. You know that a Cochrane review was performed on this and so refresh yourself on the data. Treatment with antenatal corticosteroids was associated with a reduction in death. Antenatal steroid No steroid Total Death 261 341 602 Survival 1552 1473 3025 Total 1813 1814 3627 Risk steroid = 0.143 Risk no steroid = 0.188 Question 39.2 Which of the following is the true absolute risk reduction for death? Select ONE answer only. A. 4.5% B. 24.0% C. 31.5% D. 73% E. 76% Question 39.3 Which of the following is the true relative risk reduction for death? Select ONE answer only. A. 4.5% B. 24.0% C. 31.5% D. 73% E. 76% Question 39.4 Which of the following is the number needed to treat to prevent one death? Select ONE answer only. A. 1.3 B. 1.4 C. 3.2 D. 4 E. 22 Answers 39.2–39.4 Question 39.2 A. 4.5%. Question 39.3 B. 24.0%. Question 39.4 E. 22. Refer to Table 39.8 for method for calculations.

39 752Evidence-based paediatrics radiograph findings in Table 39.6)? As with everything in EBM, these guides do not give you the rules to act on, but tools to think through. Benefits and harms When applying the results of a clinical trial, it is often difficult to tangibly understand the balance that should be struck between the beneficial and adverse effects of a treatment. If, hypothetically, intravenous antibiotics had been found to reduce hospital stay in every three treated (NNT = 3) but resulted in 1 in 50 having a significant episode of MRSA bacteraemia (number needed to harm (NNH) = 50), then is it worth using the treatment? higher risk of fever episodes and therefore a higher risk of febrile seizure. Social circumstances may mean that one family will opt for ambulatory care with the possibility of readmission, where another requires in-patient facilities. Were all clinically important outcomes considered? Did the researchers consider all the outcomes that are important to your patient? What is the information on side effects? Is there any information about adverse events in children? Are the outcomes described directly relevant to our patients (e.g. length of time in hospital) or surrogate outcomes (resolution of chest Table 39.9 Best evidence topic – prognosis Prognosis – febrile seizure (FS) Scenario: A developmentally normal 18-month-old infant with no family history of febrile seizures presents with a febrile seizure (FS) during an upper respiratory tract infection and fever of 38.5°C. His mother is very anxious about what has happened and would like to know if it is likely to happen again. Step 1: Asking a question: In a child with a first febrile seizure [patient] without complex features [interesting thing], what is the risk of future febrile seizures [outcomes]? Step 2: Acquiring evidence: Search terms: (Seizures, febrile OR febrile Convulsion* OR Febrile Fit) AND (Child OR child* OR infant*). Databases: Cochrane library – nil. PubMed Clinical Queries (Prognosis, Systematic Reviews) – 72 results. Step 3: Appraising the evidence: Study group Interesting thing Study type Outcomes Key results Comments 2496 children with 1410 episodes of recurrent FS. Seizure occurring with fever (≥38°C). Excluded children with neurologic abnormality (including developmental delay) or who had received prophylactic antiepileptic medication. Pooled analysis of five cohort studies (two populationbased, three clinic-based). All had same definition of FS. Risk factors: 1. Family history (FH) of seizure in a 1st degree relative. 2. Initial FS type (e.g. >1 in 24 hours, partial, prolonged (>15 mins). 3. Temperature at time of FS. 4. Number and type of FS recurrence. 5. Complex seizure recurrence. 30% of children will have a recurrence after a first FS. 7% will have a complex seizure recurrence. 47% of these will have a further recurrence. Hazard (number of recurrences per child/ month): • Age (12m) = 0.03 • Age (24m) = 0.25 • Age (>36m) = <0.005 Hazard ratios: • FH = 1.42 • Fever <40.0°C = 1.54 Hazard ratio described for temperature above or below 40°C, but this is not clinically useful In day-to-day practice the temperature is not always taken at time of seizure. Commentary: FS is a common acute presentation to general paediatric units. There are theories that children have a genetically determined (FH) threshold of seizures during a certain age period. This is consistent with these results. A pooled analysis is in essence a form of metaanalysis. It pools the raw data rather than the published data. This has the advantage that some errors in the data or analysis can be partially eliminated. These are therefore very expensive and time-consuming to conduct but result in higher-quality evidence. This mother can therefore be counselled that recurrence rate is 30% but for her child it is slightly lower as there is no FH of note and because of his age. His low fever of 38.5°C (i.e. it was <40°C) does increase his risk of recurrence. Step 4: Applying the evidence (the clinical bottom line) 1. Recurrence hazard declines with time and is highest in the first 6 months after the initial seizure. 2. Children aged <18 months at onset have a higher recurrence hazard. 3. Temperature (<40.0°C) and FH are the only other risks associated with recurrent seizure. (Weak recommendations, moderate quality evidence.) NB: The * in the search terms is referring to search truncations to ensure optimum sensitivity of the search (see Table 39.2). Reference: Offringa M, et al. Risk factors for seizure recurrence in children with febrile seizures: a pooled analysis of individual patient data from five studies. J Pediatr 1994;124(4):574–84.

753 CHAPTER THIRTY-NINE Two people can look at the same data but come to opposite conclusions. It can seem daunting to try to change our own practice, let alone the practice of senior clinicians. It very much depends on personalities and relationships, but it is often a refreshing change to have new evidence presented, or an enquiring mind focusing on improving what is undertaken. Some bosses are like oil tankers in the time and effort it takes to change direction, but many are not and will happily modify practice if it is going to improve the care of their patients. The right approach can help: assume wisdom and helpfulness and ask something like ‘Could you find some time to look at this research I came across? It seems at odds with what we do, and might be important to discuss.’ However, before changing the practice of others, it is good to learn how to alter the way you behave yourself. We do not usually change rapidly but we need to become aware, then accept, then learn the ‘how to’, and then finally take the plunge. Step 5: Assessing your performance e-portfolio The RCPCH e-portfolio is used by trainees in the UK. Trainees in paediatrics are expected to log two clinical questions each month in order to meet their competencies of training. Use these cases as an opportunity to direct a case-based discussion (CbD). In this respect, not only will your clinical skills be appraised but also your EBM skills. Giving an evidence-based presentation Case-based journal clubs Sadly many journal clubs continue to run along the lines of ‘I found this interesting article in last month’s edition of Archives, let’s have a look at it.’ This is an example of ‘just in case’ information and therefore will waste one hour of time for each of the busy clinicians attending. An evidence-based journal club should focus around a case and a relevant paper. Appraisal should consider ‘how good is the study?’ rather than ‘how poor is the study?’ Such a journal club can be split into three sections, cycled between subsequent weeks: 1. A question is devised, based around a real patient recently seen in the department. 2. A search performed and the best paper(s) found. 3. An analysis of the best paper(s) and how the results can be applied to the patient. There is a coarse way to approach this: say ‘Yes, of course it is’ and give the drug. Then there is a purely qualitative way: ‘How would you like this drug that might be good at getting you home quickly but might make you very sick?’ And then there is another way which seeks to quantify the differences by asking: ‘How much worse is MRSA bacteraemia versus being in hospital?’ If the patient says ‘It’s 25 times as bad’, use it to adjust the estimates like this: NNT for good thing versus NNH for bad thing/relative importance: NNT = 3 vs weighted-NNH = 50/25 NNT = 3 vs wNNH = 2. This means it would be rational for this person to take oral medication as the chance of benefit from intravenous antibiotics (one in three) is outweighed by the adjusted chance of adverse effects (one in two). This method of adjusting NNTs provides a way of conceptualizing in a rational, individual, way. Feasibility Is the treatment, test, etc., available within your clinical environment? Do I have the facilities and resources (e.g. time, money) to ensure the treatment is administered safely? When considering if you can go ‘beyond the evidence’, look at biological and psychological differences, consider the inherent risk and co-morbidities, examine all the outcomes closely, and assess if the action is possible. Then you will have a better idea of how far you can apply ‘best evidence’ to your practice. Presenting statistics to patients How we present research results to patients is important. We could say: • ‘For every ten people I give this drug to, one will benefit’ or • ‘Taking the drug improves success rates by 10%’ or • ‘Taking the drug will double your chances of success.’ The above statements all refer to the same underlying difference of treatment success in 20% of patients versus 10% in the comparison group. The statements refer to the NNT, the ‘absolute risk reduction’ and the relative risk, respectively. Examples of how to present statistics to patients are given in Table 39.8. What to do when the evidence you find contradicts practice Why do we find it so difficult to break our clinical practice habits? It may be just something deeply entrenched in our human psychological make-up.

39 754Evidence-based paediatrics Answer 39.5 1. E. Number needed to treat 2. B. Confidence interval 3. J. Relative risk reduction Question 39.5 Statistical terms Following is a list of statistical terms: A. Absolute risk reduction B. Confidence interval C. Control event rate D. Number needed to harm E. Number needed to treat F. Odds G. Odds ratio H. p-value I. Relative risk J. Relative risk reduction K. Risk Which of the following is best described by the lay descriptions of statistical terms above? Select ONE answer only for each question. Each answer may be used once, more than once, or not at all. 1. You are treating a pre-school aged child with an acute exacerbation of asthma. On discharge you want to start an inhaled steroid. His mother is worried about side effects and whether it is worth starting the medicine. You find a randomized controlled trial that is applicable to the patient. You are able to tell the mother that 41 children of this age would need to be given the treatment to prevent one exacerbation requiring oral-steroid. 2. You are treating a pre-school aged child with an acute exacerbation of asthma. On discharge you want to start an inhaled steroid. His mother is worried about side effects and whether it is worth starting the medicine. You find a systematic review that is applicable to the patient. You are able to tell the mother that from this paper you are 95% certain that steroids do reduce the number of children who have further exacerbations. 3. You are treating a pre-school aged child with an acute exacerbation of asthma. On discharge you want to start an inhaled steroid. His mother is worried about side effects and whether it is worth starting the medicine. You find a randomized controlled trial that is applicable to the patient. The results of this study show that inhaled steroid reduced the risk of exacerbations requiring oral steroids by 27% from 15% to 11%. These steps may need to be dissected so that they can be performed over subsequent meetings. The problems you are likely to face when doing this include: • Lack of answers to the questions asked. • Research nihilism – no paper is perfect so no answer can be given. • Lack of access to papers. • Staff changes and constantly revisiting the basics. Evidence-based presentations 1. When you are asked to give a presentation – whether a case, an audit or a research project – you can use the framework of EBM to guide your work. Outline the ‘patient’s dilemma’ and the clinical question. 2. Refer to how you found the information you are presenting (you do not need to screenshot your searches for this). 3. Appraise the information; tell your audience about its strengths and weaknesses. 4. Synthesize this and come up with an action and learning points. 5. Ask – perhaps beforehand – for feedback you can place in your e-portfolio. Depending on what you are focusing on, the proportion devoted to discussion will vary. For example, if you have a research project/paper to describe, then still set the background using the EBM framework and how you found the information or paper, but your own appraisal will take up most of the presentation.. Practising in an evidencebased way EBM: ‘doing’ or ‘using’ The doing mode of EBM is the purest form and involves at least the first four steps of EBM (see Table 39.1). This allows tailor made searches for evidence for individual patient needs. The using mode of EBM involves searching sources where evidence has already been searched and appraised by a secondary party. This allows rapid and efficient acquisition of evidence that has already been searched and appraised, a limiting factor being that the evidence may not be as highly relevant to your patient’s needs. Neither of these is

755 CHAPTER THIRTY-NINE more ‘correct’ than the other and most practitioners of EBM will move between the two. Guidelines A common definition of a guideline is ‘a systematically developed statement to assist practitioner and patient decisions about appropriate healthcare for specific clinical circumstances’. Some guidelines are excellent sources of information, and the authors have usually done the job of collating and appraising all relevant evidence into a clinically relevant document. Wellproduced guidelines are an excellent way of providing both ‘just in time’ and ‘just in case’ information. When is it appropriate to work outside of guidelines? Guidelines are guides, not cages. They summarize and rationalize the management pathways for the vast majority of children, young people and families with medical problems. Sometimes, though, the guideline is not appropriate for the patient in front of you, and learning when to deviate from guidelines is a highlevel skill that needs to be mastered to be a truly competent clinician. There are some things that are obvious: if a child has had an anaphylactic reaction to penicillin, the guideline-advised piperacillin-tazobactam will be dangerously inappropriate. There are some less obvious: occasionally the crackly, wheezy, bluish child in front of you does demand a chest radiograph to detect the mediastinal mass that is causing the problem. Learning where the balance of clinical experience, research-based data and patient/family characteristics come together is the art of EBM. Just in case EBM There is still a need to have a trickle of new information entering your life, not just the reactive ‘pull’ of clinically relevant answers. The best ways of getting it Box 39.7 Good sources of ‘using’ EBM information Your sources of ‘just in case’ information should be highly filtered – along EBM lines: • Set up an ‘Evidence update’ account with BMJ McMaster and have relevant evidence sent to you, for free (http://plus.mcmaster.ca/ EvidenceUpdates/) • Archimedes section of Archives of Disease in Childhood (http://adc.bmj.com) • Journal of Paediatrics: Current Best Evidence (http://www.jpeds.com/content/ societyCollectionCBE) • UpToDate (www.uptodate.com) • Evidence-Based Child Health: A Cochrane Review Journal • Bandolier • Clinical Knowledge Summaries vary, but require you to source a digestible, personally acceptable form of highly-EBM filtered news (Box 39.7). Further reading Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med 2010;7(9):e1000326. Centre for Evidence Based Medicine, University of Oxford. EBM tools. <http://www.cebm.net/index.aspx?o=1023>; [accessed 11.09.15]. GRADE Working Group. GRADE guidelines – best practices using the GRADE framework. <http:// www.gradeworkinggroup.org/publications/JCE_series.htm>; [accessed 11.09.15]. Green ML, Ciampi MA, Ellis PJ. Residents’ medical information needs in clinic: are they being met? Am J Med 2000;109(3):218–23. Greenhalgh T. How to Read a Paper: The Basics of EvidenceBased Medicine. Chichester: Wiley-Blackwell; 2010.

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LEARNING OBJECTIVES By the end of this chapter the reader should: • Be aware of the main quality issues relating to paediatric care • Understand the concepts of quality improvement • Be able to describe basic improvement tools • Be able to provide examples of quality improvement in child health 757 CHAPTER FORTY Peter I Lachman, Ellie Day, Lynette M Linkson, Jane Runnacles Quality improvement and the clinician C H A P T E R 40 Question 40.1 Quality improvement and patient safety concepts Following is a list of patient safety and quality improvement terminology: A. Effectiveness B. Healthcare-associated harm C. Natural variation D. Medication error E. Patient safety F. Reliable healthcare G. Risk management H. Unwarranted variation Which of the above terms is the most appropriate description for each of the following scenarios? Select ONE answer for each question: 1. Children presenting in status epilepticus receive treatment according to the NICE guideline when they present to the emergency department at their local hospital. 2. A child on the PICU dies as a result of a central line associated bacteraemia and review reveals that the central line care bundle has not been followed. 3. Discharge plans for asthmatic children in the paediatric department are different depending on which consultant is on call. The concept of quality in healthcare and the understanding of its relevance and role have evolved over the past twenty years. Our aim as paediatricians is to improve the quality of care and outcomes for children. Our challenge is to work with children and families to understand the issues they face, to jointly define the potential solutions and then develop improvement programmes to implement them. We now understand that advances in medical science achieved in the last century are insufficient to guarantee good clinical outcomes. The UK reviews following the paediatric cardiac surgery clinical crisis at Bristol and high profile child protection cases confirm that we need more than our knowledge about clinical medicine and guidelines to achieve good clinical outcomes. These seminal events led first to the development of clinical governance and clinical audit, and subsequently raised the profile of the patient safety agenda and the concept of quality improvement.

40 758Quality improvement and the clinician Quality improvement in healthcare is ‘the combined and unceasing efforts of everyone, i.e. healthcare professionals, patients and their families, researchers, commissioners, planners and educators, to make the changes that will lead to better patient outcomes (health), better system performance (care) and better professional development (learning)’. This implies that we all are responsible for the quality of care we deliver and that quality improvement should be part of our job as clinicians and other healthcare providers. The purpose of quality improvement in paediatrics and child health is to ensure that children receive the care needed and wanted the first time, every time. This requires safe, effective, care that is reliably and consistently delivered and the consideration of its value. The latter is defined from the viewpoint of the child and their family, i.e. what do they value in terms of the care that is delivered, both in terms of value for money but more importantly the value in achieving the child’s desired outcome? There is a definite gap between what we know to be best practice from research and the care that is delivered to children. Quality improvement provides the framework to close this gap. Major quality issues facing the health services in general The Institute of Medicine identified six dimensions to quality care: • Patient or person centeredness: ensures that patient values guide all clinical decisions. • Equity: aims to provide care that does not vary in quality because of personal circumstances including geographic location, ethnicity and socio-economic status. • Patient safety: intends to avoid harm from the care that is intended to help. • Effectiveness: the provision of evidence-based care based on the need of the patient. • Timely care: aims at ensuring access when and where care is needed. • Efficiency: refers to the need to decrease waste, duplication and improve the performance of healthcare. Question 40.2 Variation in healthcare A review of services at your local hospital reveals that there is a significant variation in the management of children admitted with asthma. This is most likely to suggest: A. A lack of evidence on which treatment decisions for individual children can be based. B. A well-constructed local guideline is in place, which identifies areas where the evidence base is inconclusive. C. An incomplete understanding of the pathophysiology and natural history of the condition in childhood. D. Outcomes are generally better for children when didactic algorithms are not in place. E. Outcomes for asthma care in this centre are likely to be worse than expected. The above principles can be translated into practical standards, which form a useful framework for an approach to quality improvement: • Governance for safety and quality in health service organizations, which incorporates clinical governance and the structures required for quality and safety. • Partnering with patients to develop services and how services are delivered. • Preventing and controlling healthcare-associated infections. • Ensuring medication safety from prescribing to dispensing to administration and reconciliation. • Patient identification and procedure matching at all times. • Reliable clinical handover at all interactions. • Blood and blood products safety. • Preventing and managing pressure injuries. • Recognizing and responding to clinical deterioration in acute settings. • Preventing falls and harm from falls. • Ensuring that the access to and the flow of patients through the system is timely and efficient. Answer 40.2 E. Outcomes for asthma care in this centre are likely to be worse than expected. See below for discussion. Answer 40.1 1. F. Reliable healthcare. 2. B. Healthcare-associated harm. 3. H. Unwarranted variation

759 CHAPTER FORTY increases the number and rate of arrival of patients above the normal predicted range. This in turn can lead to problems in access and equity of delivery. Some areas may have long waiting times and others are seen promptly, depending on how the service is designed. Where a child lives determines the quality of the care they will receive. The Royal College of Paediatrics and Child Health (RCPCH) has highlighted this problem as one of the challenges that the NHS must address. Reports on disease-specific outcomes, e.g. on epilepsy, have demonstrated the impact variation can have on clinical outcomes. 2. Variation in choice of care interventions In the past, the evidence base in paediatrics was not as robust as it is today. Many aspects of what we do now is well evidenced and the development of guidelines has provided a basis for what works and what does not. Parents will often defer to the opinion of the clinician, believing that it is based on the best evidence available. In cases where robust evidence does not yet exist, there needs to be local consensus on how to manage certain conditions in order to minimize variation. 3. Variation in the delivery of effective care Where there is a clear evidence base and a gold standard of care for a specific disease or patient group, every child with that condition should receive the same care. Variation from standardized protocols, not dictated by the clinical needs of the child, may be unwarranted and harmful. Variation is to be expected in all clinical activity and is an inevitable consequence of differences between systems, i.e. different hospitals, different clinicians, different patients, etc. Unwarranted variation in care is that which is not explained by the clinical need or the choice of the parent and child. Unwarranted variation in care is widespread and occurs when clinicians: • Overuse treatments or procedures that do not help patients get better • Underuse interventions that can work • Misuse interventions that can harm patients. Variation in clinical care derives from decisions that clinicians make regarding diagnosis and treatment interventions and is the responsibility of clinicians. Resource and service design are less likely to be under the clinician’s control. Trainees are aware of the different preferences of their consultants in the treatment of children where they work. Guidelines are often not followed and variation can be confusing for both staff and families and ultimately has a detrimental impact Quality problems facing children Most of the above are adult focused but also apply to children. However, in paediatric and child health practice, children are subject to multiple and complex additional issues either in hospital or in the community. Any paediatric improvement programme either at national or local level needs to take account of and address these specific challenges. Variations in service provision As in adult care, variation in service provision is a key problem. An analysis of the way health services for children are organized has revealed that there are wide disparities in service provision across Europe (Wolfe et al 2013). A more consistent approach to the way we design and integrate services and how we address the totality of children’s needs would result in the development of equivalent outcomes. On a system or policy level (known as the macro level), it has been recommended that in order to improve outcomes, one needs to look at the whole healthcare system and consider reconfiguration of the different elements of care in health as well as in social care and education. It is estimated that approximately 1500 preventable deaths occur in paediatric departments in the UK each year, while many more children live with preventable disabilities, chronic pain and unequal access to the services they need. Across the UK, variation in how care is delivered in different areas has a major impact on local outcomes. There are three key components to variation in service provision: variation in how services are designed, variation in the choice of care interventions, and variation in the delivery of effective care. 1. Variation in how services are designed The design of services and what can be provided is a major factor in the standard of care that children receive, and on their resultant outcomes. Variation is to be expected in all that we do and in all processes we undertake. This may be in the way we set up the systems and processes of care, or in the way we treat individual children. An example of systems variation is the difference in the way emergencies are managed within the working day, overnight and over weekends or bank holidays. The processes are clearly different; there is variation in the clinical expertise available and in the outcomes that are achieved. Another example is in emergency care provided when a major accident

40 760Quality improvement and the clinician Approximately 10–20% of errors are reported, and of those many do not cause harm. The measurement of harm is complex and requires a number of perspectives. Harm is best defined as ‘anything that one would not like to happen to oneself, one’s own child or a member of one’s family’. Using a broad definition that is personal at the same time helps one understand how pervasive harm can be. The key is that in patient safety one wants to reduce harm to the minimum possible. For children, the key challenges for safety are: • The healthcare system is not child-focused, e.g. seeing children in an adult hospital. • The environment may not be child-centred or designed around the needs of the child. • The equipment may not be appropriate for all the children of different ages and needs. • All the staff may not be trained in paediatrics or child health, or be fully aware of the nuances of child health and development. • The children themselves cover a wide range of disease presentations as they vary in age from neonates to adolescents and young adults. • The tasks may not be appropriate for the level of training or experience of the clinicians. Specific problems include: • Delayed and missed diagnosis of conditions such as sepsis, juvenile idiopathic arthritis, brain tumours and sepsis, to name a few. • Failure to recognize clinical deterioration resulting in collapse and death. • Medication harm due to prescribing errors, administration mistakes and poor reconciliation of prescriptions across healthcare settings. • Infections of different types, especially peripheral and central line infections. • Tissue viability due to cannulation. An increasing number of patient safety tools are emerging to reduce medication harm (such as drug calculator apps and zero tolerance prescribing) and healthcare-associated infections (including care bundles to reduce central line infections), and to improve both the identification and management of the deteriorating child (Paediatric Early Warning Score (PEWS)) and communication between healthcare professionals (Situation, Background, Assessment, Recommendation (SBAR)). Clinical care bundles are collections of processes required to effectively and safely deliver care for patients undergoing particular treatments with inherent risks. A bundle is a grouping of several scientifically grounded elements essential to improving clinical outcomes. Several interventions are bundled together and significantly improve patient care outcome. A patient gets a ‘Yes’ when all elements are achieved every on outcomes. Despite short-term clinical placements, trainees are well placed to undertake small tests of change and lead team-based improvement projects. Trainees have an opportunity to highlight and then address local variation through the use of improvement methodology. To understand variation further, one also needs an understanding of the individuals who work in healthcare, i.e. their beliefs and attitudes, which form the culture of their work environment. Doctors are taught to be professionally autonomous and this influences their ability to work in teams. Changes in working patterns due to policies such as the European Working Time Directive require new ways to work together. These require a shift in culture and a change in previously held beliefs. Particular attention needs to be placed on the interaction of different professional groups within multidisciplinary teams; and across primary, secondary and social care boundaries. An understanding of the culture of the different professions, different clinical teams and the organization as a whole is essential to achieve improvement. Improvement and data collection needs to be seen primarily as a means to improve care rather than to judge how well individuals are doing or score performance. Higher risk of harm Patient safety is defined as freedom from healthcareassociated, preventable harm. Patient safety in children is different from adults, as they are vulnerable, dependent on adults and may lack a voice. Children rely on adults, and adult-trained clinicians frequently provide their healthcare in adult-oriented facilities. Children vary in size, posing problems in interventions such as medication, e.g. weight-dependent drug dosing increases the risk of medication errors. Children are also more vulnerable from the failure of health professionals to recognize abnormal vital signs, such as heart rate, which vary with age. A patient safety incident is any healthcare-related event that was unintended, unexpected and undesired and which could have caused or did cause harm to patients. Serious patient safety incidents are usually caused by multiple systems failures, rarely simply by frontline staff error. Errors may occur many times without any consequence; however, they only need to align once to cause a serious harm event. Patient safety projects need to consider a wider perspective and address the process of care rather than the individual care provider. Risk management is the process by which one identifies factors that prevent the provision of safe, efficient and effective care. Traditional ways of detecting adverse events in paediatrics have relied on voluntary reporting.

761 CHAPTER FORTY the service and wider system is designed to meet the needs of children. Clinicians need to ask about the users’ experience of healthcare and to enlist parents and children to co-design services in order to improve quality of care. Improving healthcare The translation of research findings into practice is problematic. Implementation science refers to the study of the methods used to translate and implement the findings of clinical research into clinical practice. This often involves the study of human behaviour and the way clinicians apply new knowledge. Improvement science is grounded in testing and learning cycles to ensure that change results in measurable improvement. It requires an understanding of statistics and psychology, and is grounded in a ‘learning cycle’. Just as one would design an experiment to test a hypothesis in a scientific experiment, a QI project involves identifying a problem that needs improvement, assessing the factors that may cause the problem, introducing an anticipated improvement, followed by data collection to show if this change has led to the anticipated improvement. Box 40.1 summarizes some examples of paediatric improvement interventions. Achieving improvement is not easy. Simply asking people to improve by working harder has not been effective. Improvement requires leadership, a clear vision, an understanding of ways to test changes followed by spreading and sustaining the improvement. Most of the theories behind the methods currently used were derived by statisticians or psychologists. Healthcare organizations have adopted a number of approaches to improvement, ranging from comprehensive organization-wide methods which aim to change the total culture (Box 40.2), to simple techniques, described below, which an individual clinician or clinical team can apply. Whichever method is used, the evidence suggests certain conditions need to be in place for successful implementation. This includes resources and training, clinical engagement, managerial participation, and organization-wide coordination and use of data. Healthcare can be a challenging environment for quality improvement, especially for trainee doctors. Success depends on fitting the chosen method with the local context and this may include adapting the approach to circumstance. When outcomes of an improvement project are less than optimal, it is rarely to do with the chosen method and more likely to do with the way individuals interact within the clinical team (this is termed the clinical micro system). In most health systems, the method that has been recommended for the individual clinician or the time and a ‘No’ even if one element is left out. One must achieve 100% compliance of all elements of the bundle. An example is the central line bundle, for which the key components are hand hygiene, barrier precautions on insertion, chlorhexidine skin cleaning, optimal catheter site selection and daily review of the line. Coordination of care for chronic conditions Perhaps the most challenging part of healthcare for children and their families is the coordination of care between the different healthcare providers, and across agencies such as social care and education. The many complex chronic paediatric conditions managed by multiple agencies make it difficult to deliver an integrated model of care which places the child at the centre of the process. It has also been hard to measure quality and outcomes. This is compounded by the lack of a single unifying IT infrastructure to support integrated care. Key problem areas include: • Safeguarding of children, where there have been periodic catastrophic failures in protective services, with communication often being the underlying problem. • Child mental health, with problems of children with behavioural problems not having access to services. • Transition into adult care. In the UK, at least 20% of children are living with a chronic illness. These numbers are likely to increase with improved survival in disabling childhood conditions and UK data suggests that 26% of those with a chronic condition have multiple diagnoses. The commonest chronic diseases are arthritis, heart disease, respiratory problems, skin disorders and mental health conditions. Lack of child-centred care Healthcare services for children are traditionally designed around the healthcare professional, and are often attached to adult services, with poor integration of community and hospital care. For example, children may wait in adult emergency departments or use adultfocused community-based primary care. There is growing recognition of the need to adapt working practices to ensure high quality care is provided to children at all times. The National Service Framework for Children in England set national standards for children’s health and social care, but there are still inequalities in children’s health outcomes. Healthcare professionals need to consider whether the individual care provided to a child/young person is patient-centred and whether

40 762Quality improvement and the clinician Box 40.1 Examples of paediatric quality improvement interventions Case 1 Safety: How can the recognition of deterioration of seriously ill children be improved? Background: A review of serious safety events (SSEs) and ward-to-ICU transfers identified five risk factors: family concerns, high-risk therapies, presence of an elevated early warning score, watcher/clinician gut feeling and communication concerns. Intervention: Unit-based huddles (structured safety briefings) and 3-times-daily inpatient huddles were developed to identify patients at increased risk. Nurses reported any patient with a risk factor to the charge nurse every 4 hours; allowing escalation of concerns. Results: UNSAFE (unrecognized situation awareness failures events) were measured. The rate of UNSAFE transfers per 10,000 non-ICU inpatient days was significantly reduced from 4.4 to 2.4. The days between inpatient serious safety events (SSEs) increased significantly (Brady PW, et al. Pediatrics 2013;131:e298–e308). Case 2 Medications: Reducing medication errors in PICU by changing the prescribing system. Background: Prescribing errors account for a large number of paediatric medication errors on intensive care units but voluntary reporting tends to underestimate the error rates. Intervention: A zero tolerance prescribing policy, a dedicated prescribing area to reduce distractions and a formal set of rules for all was introduced. Nursing staff were asked to refuse to administer inadequate prescriptions and daily verbal feedback of prescribing errors was given. A monthly bulletin providing anonymous feedback of errors was published. Results: There was a significant reduction in prescribing errors from 892 errors per 1000 PICU occupied bed days to 447 (an absolute risk reduction of 44%) (Booth R, et al. Intensive Care Med. DOI 10.1007/s00134-012-2660-7). Case 3 Efficiency: Improving paediatric discharge/length of stay without increasing readmissions. Background: Inefficient discharge impacts patient flow through the hospital and studies suggest that 1 in 4 paediatric patients experience unnecessarily long admissions. Intervention: Improvement science was used to standardize discharge criteria for common conditions and plan for discharge proactively to reduce delays. Changes were tested using a series of PDSA cycles and statistical process control (SPC) charts (see Box 40.6) assessed the impacts of interventions over time. Results: Within 18 months, the percentage of patients discharged within 2 hours of being medically fit had improved significantly (42% to 80%). This was associated with decreased median length of stay. There was no increase in readmission rates or decrease in patient satisfaction (White CM, et al. BMJ Qual Saf 2014;23:428–36). Box 40.2 Lean six sigma methodology Lean is a concept that was developed in Toyota motor manufacturing to describe the way in which production processes are organized. It is basically about getting the right things to the right place, at the right time, in the right quantities, while minimizing waste and being flexible and open to change. Lean thinking focuses on what the customer values: any activity that is not valued is waste. If you remove the waste, the customer receives a more value-added service, which in healthcare could mean any activity that helps patients manage their symptoms or get better. The 5 principles of Lean are: 1. Specify value by involving patients in your work: map their ‘journey’ through your organization to allow staff to see what the patient sees. 2. Identify and visualize the value stream, i.e. what makes the patient journey worthwhile. Mapping the different stages of the process helps to understand how a patient receives care. 3. Analyse the steps to see the obstacles that prevent free flow of the patient on their journey, or the unnecessary steps that are not of benefit to them. 4. Pulling patients along their journey may be more effective than ‘pushing’ patients from one queue to another. This is more important for those patients with more than one problem. 5. Perfection aims to continually improve the patient journey through ongoing development of these principles. In healthcare, Lean has a strong focus on reducing waiting times, since time spent waiting is not value-added. For example, Hereford Hospital has applied Lean to improve turnaround times in pathology and pharmacy. They have adopted rapid improvement events where staff identify waste using a form, and then decide on the areas to focus their improvements. They have run similar projects focused on the entire patient journey. (See Further reading for more examples.)

763 CHAPTER FORTY Fig. 40.1 The Model for Improvement. (From Langley GJ, et al. The improvement guide, 2nd ed. San Francisco: JosseyBass; 2009, with permission.) What are we trying to accomplish? How will we know that a change is an improvement? What changes can we make that will result in improvement? Act Study Do Plan PDSA cycle Fig. 40.2 Plan Do Study Act (PDSA) cycle. (From Langley GJ, et al. The improvement guide, 2nd ed. San Francisco: Jossey-Bass; 2009, with permission.) PDSA cycle Act • What changes are to be made? • Next cycle? Plan • Objective • Predictions • Plan to carry out the cycle (who, what, where, when) • Plan for data collection Study • Analyse data • Compare results to predictions • Summarize what was learned Do • Carry out the plan • Document observations • Record data Box 40.3 Clinical example of use of the Model for Improvement Problem A 5-year-old child is noticed not to be interacting all the time in class at school. There is no past medical history and no family history of note. There is no past illness and the child is otherwise well with a normal examination. The ECG is normal and the EEG indicated a pattern suggestive of petit mal epilepsy. Aim (What are we trying to accomplish?) To decrease the episodes of absence seizures from 10 per day to zero. Measure (How will we know that a change is an improvement?) The parents of the child and the nursery school teacher will record the number of absence seizures per day. Parents will provide an assessment of the other reactions to the medication, e.g. behaviour, sleep concentration, etc. Change or intervention (What changes can we make which will result in improvement?) The child will be commenced on the lowest dose of the recommended anticonvulsant. PDSA PLAN: Discuss with parents the intervention, side effects and how to measure the number of absence seizures. DO: The parents implement the recommended treatment. STUDY: At the one month review, the effects of the intervention are assessed using the measures as well as other responses to the medication (behaviour, sleep, etc.). ACT: A decision is made either to increase the dose or remain on the same dose. And the cycle restarts. clinical team is the Model for Improvement (Fig. 40.1), also called ‘a small test of change’. This is a method that is very similar to what we use in our dayto-day clinical practice (Box 40.3). The methodology allows one to combine thinking and action through small-scale cycles of testing change (e.g. starting with one clinician and one patient). The process involves making a plan and predicting what will happen, implementing the small test of change, and then studying what happened before retesting (Fig. 40.2). A clear aim statement and a set of defined measures to capture impact is the first step to a successful improvement project either on a small or large scale. Interventions, or ideas for change, can then be

40 764Quality improvement and the clinician generated. Some will need to be aimed at the macro level; but changes should be made and measured by the front-line teams, so that improvement addresses local patient and clinical concerns. Empowering clinicians to lead change and make a difference to patient care adds value to patients and to the front-line clinician who has a duty to continually improve. The first step is to look for possible areas to develop a project. Table 40.1 suggests potential areas for improvement, which can be approached using the Model for Improvement framework outlined above. A driver diagram (Fig. 40.3) can help determine how you approach your improvement project. It breaks the project aim into the key factors or components/drivers, which impact in the aim. The primary drivers are the fundamental requirements, the secondary drivers enable the primary driver to be achieved and these are followed by the changes to be tested. The example below looks at the overall problem of deteriorating children, one of the key reasons there is a higher mortality rate in the UK than in other European countries. The outcome of cardiac arrest in children is poor, but the majority of arrests are preventable. Analysis of medical/nursing notes in the period prior to arrest may reveal documented changes in physiological parameters or variations in management/investigations that may have predicted or even prevented the arrest. A quick review of the medical and nursing notes may be all that is required to identify valuable lessons for dissemination amongst relevant teams. This analysis should develop ownership from the clinicians themselves to encourage a no-blame safety culture. Table 40.1 Changes to consider in healthcare Domain Example Safety • Decrease in prescribing errors • Prevention of unexpected clinical deterioration • Improvement of handover between clinical teams • Decrease in healthcare-associated infections (HAI) Effectiveness • Implementation of protocols • Developing ‘reliability’ in service provision, which means that the child receives the right care in the right place every time by ensuring that guidelines are followed all the time, or that staff changes do not mean a change in the quality of the service provided • Coordination of transitions • Improve communication between service providers using defined tools Child-centredness • Decreasing starving of children pre-operations • Environment in emergency waiting rooms • Integrated care with community services Efficiency • Starting clinics or operating lists on time • Decrease in repeat of blood tests • Decrease DNAs in clinic Equity • Change in services to improve utilization by disadvantaged children • Reduction of unwarranted variation in care (e.g. access to services, such as epilepsy specialist nurses, in different regions) Timeliness • Decrease waits for appointments • Timely review of new admissions by a consultant • Laboratory results returned for quick diagnosis Question 40.3 Measurement of improvement You have designed an improvement project aiming to decrease emergency admission rates for your patients with epilepsy. A colleague, who has read the relevant NICE guidelines, suggests that you measure the number of patients who have been reviewed in outpatient clinics within the last 12 months. She feels that infrequent review is contributing to poor control of epilepsy in some children. As part of your project, this measure would be classed as a: A. Balancing measure B. Outcome measure C. Process measure D. Standard measure E. Structural measure The framework below has been successful, with anticipation and awareness being the major intervention. Once a problem has been identified, it is important to define the aim for the project – what does one want to improve, by how much, where, on which group of patients and by when? This is demonstrated in Box 40.4. Measurement for improvement

765 CHAPTER FORTY Fig. 40.3 Driver diagram. *SBAR, Situation, Background, Assessment, Recommendation. (From Runnacles J, Moult B, Lachman P. Developing future clinical leaders for quality improvement: experience from a London children’s hospital. BMJ Quality and Safety, November 2013, with permission.) Aim What we want to achieve Primary drivers What we need to do to achieve our aim Secondary drivers How we’re going to do it Measure vital signs according to RCN standards Act upon early warning scores promptly Calculate early warning scores accurately Calculate early warning scores with each observation Detect deterioration Escalations/referrals using SBAR communication tool* Ward handover “flags” at risk patients Escalations/referrals are documented Communicate To reduce respiratory and/or cardiac arrests on all children’s wards (excluding ICU) at our hospital by 50% by end of December Staff are trained to respond to clinical deterioration Care is appropriate and follows current guidelines Resuscitation trolleys are ready for use in all areas Staff are trained in resuscitation by simulation Transfer to ICU/ referral to retrieval team is appropriate and timely Manage deterioration Answer 40.3 C. Process measure. Data is the basis for all research, clinical interventions and decision-making, and also for improvement. Data for improvement is used to drive change. Data for research is used to develop new knowledge; and data for judgement is what one does in clinical audit – assess whether the set standards have been met. In the process of improvement, one needs data to understand the baseline (initial audit), develop an improvement plan, test changes and implement further change. A set of measures are defined to allow assessment of impact of each small change and sequential progress toward the overall aim. Once the aim has been achieved, measurement helps to ensure the improvement is sustained and the new process is part of normal working. This approach differs from a randomized controlled trial in which the hypothesis and prediction is fixed throughout the process. With improvement projects, the aim is constant but the predictions and methods change depending on the results of small tests of change. The measurements obtained still have significance (p-values can be calculated), but the approach is different to the scientific teaching of medical research. There are four main types of measures: • Outcome measures, which tell us what actually happens to the child – what are we ultimately trying to achieve? (e.g. for a paediatric diabetes service: average HbA1c level). • Process measures, which tell us about how the system works – are we doing the right things to get there? (E.g. percentage of patients with HbA1c level measured twice in the past year.) • Balancing measures – are the changes we are making to one part of the system causing problems in other parts of the system? (E.g. rates of hypoglycaemic episodes – to make sure they are not increasing with better glycaemic control.) • Structural measures – do we have the right tools and resources (human, physical, financial) at our disposal? (E.g. percentage of patients who have access to a diabetes nurse specialist.) In addition, one needs to consider the cost of the improvement and the value (quality over cost) it derives. The most important measures to collect are outcome measures that have been shown to improve care for patients. Outcomes are more challenging to

40 766Quality improvement and the clinician measure than processes, and therefore process measures can be used as a proxy in the short term, particularly if they are based on good evidence. How to use run charts to study variation and demonstrate improvement In quality improvement projects, we use run charts (Box 40.5, Fig. 40.4) and statistical process control (SPC) charts (Box 40.6) to demonstrate continual improvement. These are a form of time series analysis, where data is plotted against time; statistical process control (SPC) charts are the simplest way to show the variation in a system. They allow for identification of variation that is normal, or variation due to extenuating factors that need to be investigated. These charts also help to assess whether an improvement has been made. There are numerous rules for the interpretation of run charts that allow for statistical significance to be inferred. Box 40.5 Using run charts to measure change When collecting data, one needs to use measures that occur frequently, e.g. daily or weekly, so that learning accrues quickly. When identifying the chosen measure, one needs to consider how data will be collected, who will collect the data and how it will be analysed. First, measure the process for a short while, e.g. one week if frequent, and then plot the baseline. Extend the median and begin the test and annotate where you begin. Continue to plot as the data changes. There are rules as to when a significant change has occurred and once that has been proven, the median can be redrawn. Four of these rules are: • If there are eight or more consecutive points on one side of the median excluding points on the median, this indicates that a special cause has influenced the process, i.e. if a change has been made, and there are eight points on the positive side of the median, then the improvement is significant (see Fig. 40.4A). • A trend is when five consecutive points occur in the same direction and this indicates that a special cause has occurred (see Fig. 40.4B). • If you see a pattern that recurs eight or more times in a row, it is a good idea to look for a special cause (see Fig. 40.4C). • If there is one data point far outside the mean, look for a special cause (see Fig. 40.4D). Box 40.4 Example of setting up an aim and measures for a local project on a ward Problem Failure to detect the clinical deterioration of children has been one of the challenges all front-line clinicians need to face. The early identification of the deteriorating child has been facilitated by the introduction of Paediatric Early Warning Scores (PEWS). These are used to convert the data from a combination of clinical and vital signs into a composite single score which can identify children at risk of sudden deterioration. There are different versions, but early warning scores consist of a combination of scores from a selection of routine observations of patients, e.g. pulse, respiratory rate, respiratory distress, and conscious level. If a child deteriorates, the score increases and gives an indication that the child needs to be assessed. However, they may not be the only factor which needs to be assessed and other signs may be present. The introduction of the score is difficult, as it requires a change in attitudes and behaviour. Aim To ensure that every child on the ward has an early warning score that is recorded and acted upon by the end of the year. Plan Meet with colleagues and other stakeholders to brainstorm ideas for implementing PEWS. Choose an idea that the team support, for example a new observations chart with PEWS. Develop the chart (the task) and then test it. Predict what will happen – will anyone use it, will the chart be appropriate, will it pick up the ill children? Can you measure the change? Do Do a trial (the small test of change) with this new chart for 10 patients on the ward for one day. Study Look at chart completion rates and get feedback from nurses about ease of use. You may also wish to study escalations to the medical team. Act Ensure staff members have access to results of the study phase. Consider next steps, for example redesign of charts if needed, posters and staff awareness/training activities. Each of these could form a new PDSA cycle.

767 CHAPTER FORTY Fig. 40.4 Examples of run charts. See Box 40.5 for explanation. (From Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety 2011;20(1):46–51, with permission.) 0 1 2 3 4 5 6 7 8 9 10 Time 5 10 Measure or characteristic 15 20 25 RULE 3: NUMBER OF RUNS Data line crosses once Too few runs: Total 2 runs Time 60 50 40 30 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Average cycle time (Min) 70 80 90 RULE 4: ASTRONOMICAL DATA POINT 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time 14 15 16 17 18 19 20 21 22 23 24 25 5 10 Measure or characteristic 15 20 25 RULE 1: SHIFT RULE 2: TREND 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Time 14 15 16 17 18 19 20 21 22 23 24 25 5 10 Measure or characteristic 15 20 25 Question 40.4 Quality improvement projects This is a list of possible steps in a quality improvement project: A. Apply lean six sigma methodology B. Audit the previous 12 months’ data C. Decide on your first PDSA (Plan, Do, Study, Act) cycle D. Devise a driver diagram E. Engage the multi-professional team F. Invite patient input G. Organize a process mapping session H. Present to the senior management team I. Record data using a run chart J. Write a SMART (Specific, Measurable, Achievable, Realistic and Time-bound) aim Which of the above is the most appropriate next step for each of these projects? Select ONE answer for each. 1. You have been tasked to improve the flow of new admissions from the emergency department (ED) to the ward (to reduce delays, ensure observations are recorded and improve completion of nursing admission documents). You have already engaged the Emergency Department and paediatric multidisciplinary teams, who seem to have differing opinions regarding the correct sequence of actions when admitting a patient, so you need to clarify this. 2. Due to a number of complaints, your team plans to improve patient experience in the outpatient department to ensure it is more child-friendly. You have already engaged the play specialists and outpatient nursing team, who are keen to redesign the waiting area. 3. You are concerned about the large number of medication errors highlighted in a recent audit and have formed a team to improve this. The team have written a specific aim and planned the first PDSA cycle to see if a pharmacist present on ward rounds can reduce the number of errors. Answers 40.4 1. G. Organize a process mapping session. 2. F. Invite patient input. 3. I. Record data using a run chart. Box 40.7 provides eleven top tips to getting started with an improvement project.

40 768Quality improvement and the clinician Box 40.6 Use of a statistical process control (SPC) chart A paediatric medical team is struggling to get their ward discharge summaries completed within the trust target of 24 hours post discharge. An incident has occurred where a patient has missed a treatment appointment due to a delayed discharge summary. Aim: To reduce the number of days taken to complete discharge summaries and increase the percentage completed within 24 hours within 12 months. Measure: Actual time taken (average number of days) and percentage completed within 24 hours (trust target). There are two measures for this project as they provide different information. A reduction in the average number of days taken to complete summaries is not demonstrated by the percentage measure. The baseline data showed the average number of days taken to complete summaries was around 5 days. It was important to track a reduction in the measure as this was likely to happen before an improvement in the number completed within 24 hours. Change or intervention: There are meetings with all ward team members to discuss the issues. The team maps out the current process for the completion of discharge summaries. This identifies a number of issues and ideas. The team then identifies their first test of change to improve the process. Discharge summaries will be allocated to a specific doctor of the team at the main weekly ward round. Naming the person responsible for the summaries’ completion will prevent confusion and ensure the work is fairly distributed amongst the team. PLAN: All doctors in post-graduate training and consultants are informed of the plan and are asked to allocate a doctor to each patient on the Tuesday ward round. This is recorded on the handover sheet. DO: The consultant allocates the name and the clinician’s assistant records it on the handover sheet. STUDY: After two weeks, the team meets to discuss how the tests have gone. The implementation of the new system is variable and not all the consultants are adopting the change. Trainees who have been allocated a discharge summary are still struggling to complete them within 24 hours because of their other clinical commitments. ACT: The team discusses the issues and questions that have arisen and clarifies the confusion regarding the content and structure of discharge summaries. The team identifies a need for training and education to ensure there is a standardized content for discharge summaries. The next PDSA cycle will look at ensuring all consultants allocate names on the ward round. Fig. 40.5 A. Example showing percentage of patients with discharge summary completed within one day of discharge. B. Average number of days between discharge and completion of discharge summary. (Courtesy of Great Ormond Street Hospital NHS Foundation Trust.) 24-Feb 31-Mar 05-May 09-Jun 14-Jul 18-Aug 22-Sep 27-Oct 01-Dec 05-Jan 20.0 15.0 10.0 5.0 0.0 Average days from discharge to discharge summary complete: rheumatology 0% 24-Feb 31-Mar 05-May 09-Jun 14-Jul 18-Aug 22-Sep 27-Oct 01-Dec 05-Jan 20% 40% 60% 80% 100% % Patients with discharge summary complete within 1 day of discharge (weekly): rheumatology

769 CHAPTER FORTY Box 40.7 Tips to start an improvement project 1. Seek mentorship/senior support and use local and regional networks. 2. Form a multi-professional team and do not attempt it alone. 3. Use all available resources, and be imaginative. 4. Consider all relevant stakeholders, engage them from the start. 5. Break the problem down into manageable parts. 6. Develop a driver diagram, which is a breakdown of all the factors needed to achieve the aim. 7. Commit yourself to writing a SMART aim (specific, measurable, achievable, time-bound), e.g. in 6 months, 100% of prescriptions on the children’s ward will have 100% accuracy. 8. Make small changes and collect data continuously; use PDSA cycles. 9. Review progress regularly; it is easy to get distracted by clinical duties. 10. Share and publicize your data; it helps motivate your team. 11. Do not be afraid of failure; there is valuable learning from all projects. is paramount to provide good and high-quality care. However, these are not sufficient to provide continual improvement of patient outcomes and care that is safe and effective. Don Berwick outlined the challenge for all clinicians when he stated: ‘Mastery of quality and patient safety sciences and practices should be part of initial preparation and lifelong education of all health care professionals, including managers and executives.’ The delivery of quality care is a shared responsibility; all of us working in paediatrics and child health have a responsibility to address concerns of variation, harm, care coordination and experience to improve the quality of care we provide to children and families. The start of a project begins with defining the problem – what do you want to improve? Further reading Institute for Healthcare Improvement. <www.ihi.org>; [accessed 12.09.15]. Provides information and tools and reviews on quality improvement and patient safety. Panasar S, Carson-Stevens A, Salvilla S, Sheikh A. Patient safety and healthcare improvement at a glance. Chichester: WileyBlackwell; 2014. Special edition on Quality Improvement and Patient Safety. Curr Treat Options Pediatr 2005;1(4). <http://link.springer. com/journal/40746/1/4/page/1>; [accessed 18.12.15]. The Health Foundation. <www.health.org.uk>; [accessed 12.09.15]. Provides evidence scans and up-to-date reviews of quality improvement. Wolfe I, Thompson M, Gill P, et al. Health services for children in Western Europe. Lancet 2013;381:1224–34. Conclusion In any clinical practice, a good grounding in subject matter knowledge, technical skills and understanding of the pathophysiology, psychology and medical issues

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771 Page numbers followed by “f” indicate figures, “b” indicate boxes, and “t” indicate tables. A AABR. see Automated auditory brainstem response (AABR) Abandonment, and neglect, 133 Abdominal pain, functional, 474 Aberrant behaviour, allergy and, 308–309, 309b ABO incompatibility, 200–201 ABO system, 459, 459t Abortion Act 1967, 676b–677b ABR testing. see Auditory brainstem response (ABR) testing Absolute risk reduction (ARR), 750t Absorption, 258, 687–688 intestinal, 260 Abuse emotional, 134–135 physical, 126–130 sexual, 131–133 suspected, what to do when, 138 types of, 126 Abusive head trauma (AHT), and physical abuse, 130 investigations in, 130 Accessory muscles, diaphragm and, 321 Accessory pathway-mediated tachycardia, 359–360 Accidents, 101–117, 102f, 102t prevention of, 102, 102b Accommodation, eye, 599–600 Accuracy, in population screening, 23, 23t ACE inhibitors, for heart failure, 354t Acetylcholine, in heart rate, 349–350 aCGH. see Array comparative genomic hybridization (aCGH) Acholic stool, 407f Achondroplasia, 150, 528b–529b Acid-base disturbance, 571–572, 572t biochemistry in, 571 definitions of, 572t diagnosis of, 572 management of, 572 presentation of, 571–572 Acidosis hyperchloraemic, 85 lactic, 572, 572t metabolic, 85, 85t, 571, 572t renal tubular, 369–370 Acne, 494 Acquired aplastic anaemia, 454–455 Acrocyanosis, 36 ACT. see Artemisinin combination treatment (ACT) Action potential, 547 non-pacemaker, 354–355, 354f Activated charcoal, 110–111 Active immunization, 292 Acute disseminated encephalomyelitis (ADEM), 559 Acute febrile illness, 643b–644b Acute haemorrhage, 666 Acute illness, impact on gastrointestinal tract, 274–275 Acute intensive care, for spinal muscular atrophy, 679b–680b Acute intestinal obstruction, 666 Acute kidney injury (AKI), 387–388, 388t Acute liver failure complications and pathophysiology of, 412–413 in infancy, 409–411, 411b in older child, 412–413, 412t transplant in contraindications for, 413 indications for, 413 Acute lymphoblastic leukaemia (ALL) assessment of, 425b diagnosis of, 429b, 436b, 436f incidence of, 423 risk stratification for, 430 survival improvement, 713b, 714f Adaptive immune system, 284–285 ADEM, 559 Adenosine triphosphate-dependent (ATP) pump, 547 Index ADH. see Antidiuretic hormone (ADH) ADHD. see Attention deficit hyperactivity disorder (ADHD) Adie pupil, 606 Adjuvant medication, 661, 661b–662b Adnexal structures, anatomy of, 484–485, 484f Adolescence, 627–628 development in, 630–632 physical, 630 psychological, 630–631 social, 631–632, 631b epidemiology in, 628 exploratory and risk behaviours during, 634–635 integrating psychological and physical development through neuroscience during, 632, 632b, 632f maltreatment and, 135, 136b mental health in, 463–478, 655 mortality in, 629, 629b, 629t sexual health in, 637–638 transitional care in, 638 youth-friendly health services, 638–639, 639b Adolescent health, 654 determinants of, 628–629 improvement of, 654 major threats to, 654 Adolescent medicine, 627–639 definitions pertinent to, 628b engaging young people and, 632–634 resilience, 629, 630b ADR. see Adverse drug reaction (ADR) Adrenal excess, 516 Adrenal gland, 515–518 anatomy of, 515 diseases of, 517–518 embryology of, 515 history and examination of, 515–516 investigations for, 516–517 physiology of, 515 Adrenal insufficiency, 515–516

772Index Adrenaline, 89–90 for anaphylaxis, 302 for heart failure, 354t Adrenergic inhibitors, for hypertension, 382t Adrenoleukodystrophy, 66, 517 Advanced Life Support Group (ALSG), 79 Adverse drug reaction (ADR) definition of, 694 in paediatric patients, 695t unknown mechanisms of, 696 AEDs. see Antiepileptic drugs (AEDs) Aerosol deposition, mechanisms of, 334, 335f AFP. see Alpha-fetoprotein (AFP) Afterload, 351, 351t Agammaglobulinaemia, X-linked, in gastrointestinal tract, 273 Age, effect on metabolism, 692–693 Agenesis, of kidneys, 372b Ages & Stages Questionnaire™ (ASQ), 54 AHT. see Abusive head trauma (AHT) AIH. see Autoimmune hepatitis (AIH) AIHA. see Autoimmune haemolytic anaemia (AIHA) Air leaks, thoracic, in neonates, 191 Airway(s) lower, 319 paediatric, 80–81, 80b opening manoeuvres for, 81 upper, 318, 319f Airway adjuncts, use of, in basic and advanced life support, 81 Airway obstruction, 325–326, 326f upper, 319, 329–330, 330f Airway resistance, 319–320 Airway restriction, 326, 327f AITP. see Autoimmune thrombocytopenia (AITP) Alagille syndrome, 410t Alanine aminotransferase (ALT), 406 Albinism, 496, 591 Albright syndrome, 479–480 Albright’s hereditary osteodystrophy, 155 Albumin, chronic liver disease and, 413–414 Alcohol pregnancy and, 160 as teratogen, 171, 171f, 171t use of, of adolescent, 634–635 Aldosterone, 515 in hormonal compensatory changes, 352–353 Aldosterone antagonists, for heart failure, 354t Alkaline phosphatase, 406 Allergen avoidance, 312 immunotherapy, 313 Allergic eye disease, 606 Allergic march, 301, 301b–302b, 302f Allergic rhinitis (AR), 313–315 classification of symptoms of, 314f features of, 314t Allergy, 297–316 aberrant behaviour and, 308–309, 309b in food, 302–308, 303f, 304b IgE-mediated, 303–305, 304b, 305f non-IgE-mediated, 307–308 hygiene hypothesis in, 301 hypersensitivity reactions and, 297–299, 298f–299f immunity and, 297–299, 297f, 297t paediatric, 299–301 pharmacotherapy in, 312–313, 313f risk of, environmental factors in, 300–301 skin diseases and, 310–311 viral infection and, 311 Allocation concealment, 748–749 Alloimmunization, 461 Alopecia areata, 486t Alpha globin synthesis, 442 Alpha-1-antitrypsin deficiency, 410t, 417–418 Alpha-fetoprotein (AFP), as tumour marker, 428 Alport syndrome, 614t ALSG. see Advanced Life Support Group (ALSG) ALT. see Alanine aminotransferase (ALT) Altered drug metabolism, 695 Alternative hypothesis, 710 Altruistic reasons, for practising evidence-based medicine, 740 Alveolar-arterial gradient, measuring, 328, 328b Amblyopia, 599 Amino acids, 584t Aminoglycosides, 617 therapeutic drug monitoring for, 691 Aminosalicylates, for inflammatory bowel disease, 272–273 Ammonia, biochemistry of, 574 Amniocentesis, 162f Amniotic tissue, 523–524 Amoxicillin, 283b, 701 Anaemia, 444–445 acquired aplastic, 454–455 Diamond-Blackfan, 445–446, 454t Fanconi, 453b–454b, 454t haemolytic autoimmune, 450 causes and mechanisms of, 447–448, 447t investigation of, 445, 445t red cell aplasia and, 445–446 Anagen effluvium, 486t Analgesia, 660–662, 661f, 698 Analgesics, 565, 636t, 660–662, 661f, 698–700 Analysis of variance (ANOVA), 733 Analytical observational study, 15 Anaphase lag, 144 Anaphylaxis, 302 in children, 307 Androgen receptor, defects of, 397 Androgen-mediated precocious puberty, 227–228 Androstenedione, 515 Aneuploidies autosomal, 145 sex chromosome, 146 Angelman syndrome, 155 Angioedema, hereditary, 310, 310b Angiotensin II, 380 in hormonal compensatory changes, 352–353 Angiotensin-converting enzyme inhibitors, for hypertension, 382t Angiotensinogen, 515 Angiotensin receptor blockers, for hypertension, 382t Anion gap, 572t, 573f calculation of, 85, 85t Aniridia, 590–591 Anisometropia, 599 Anophthalmos, 590 Anorectal anomalies, 257 Anorectal manometry, 270 Anorexia, 665 Anorexia nervosa, 472, 473b 4Ps framework for, 473t Anosmia, congenital, 47b ANOVA. see Analysis of variance (ANOVA) Antenatal clinic attendance, 161 Antenatal screening, 24 Antenatal steroids, 4b for preterm birth, 748b, 751b for threatened preterm delivery, 180 Antenatal treatment, with maternal dexamethasone, for congenital adrenal hyperplasia (CAH), 397 Anterior horn cells, 67 Antibiotics, 281b, 700–702, 700b–701b for cystic fibrosis (CF), 337–338 for ocular inflammation and infection, 606 principles of, 282b rational use of, 280–282 topical, 489 Anticholinergics, in children, 336 Anti-cyclic citrullinated protein (anti-CCP) antibodies, 536 Anti-diarrhoeal medications, 272 Antidiuretic hormone (ADH), 90, 509–510 in hormonal compensatory changes, 352–353 Antidotes, for poisoning, 112 Anti-double stranded DNA antibodies (dsDNA), 536 Antiemetics, 565 Antiepileptic drugs (AEDs), 477 mechanisms of action of, 568b, 569 Antifungals, 282–283 topical, 489 Antihelminth agents, 283 Antihistamines, for anaphylaxis, 302 Anti-IgE therapy, 313b in children, 336–337

773 Antimicrobial stewardship, 282b Anti-migraine therapy, 564t Anti-Müllerian hormone (AMH) activity, abnormalities of, 397–398 Anti-muscarinic drops, 605 Anti-muscarinics, 272 for drooling, 70 Anti-neutrophil cytoplasmic antibodies, 536 Anti-neutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis, 385 Antinuclear antibodies, 536 Anti-phospholipid antibodies, 536 Anti-protozoal agents, 283 Antipsychotics, atypical, 477 Antipyretic, 698 Anti-rheumatic drugs, diseasemodifying, 539 Anti-spasmodic agents, 272 Antiviral therapy, 282 for ocular inflammation and infection, 606 Antral peristaltic contractions, 259 Anus, examination of, and sexual abuse, 132–133 Aorta, anatomy of, 342 Aortic flow murmur, 38 Aplasia, red cell, 445–446 Aplasia cutis, 486t Apnoea in child with brain injury, 130 with spinal muscular atrophy, 679b–680b Apocrine glands, 485 Appraisal checklist, 746, 747b Aqueducts, vestibular, widened/enlarged, 616 AR. see Allergic rhinitis (AR) Arginine, 574 Arginine vasopressin (AVP), 509–510, 510f Argininosuccinate lyase, 574 Armed conflict, and children, 656 ARR. see Absolute risk reduction (ARR) Array comparative genomic hybridization (aCGH), 142–143, 143f Arrhythmia(s), 359–361 action potentials and, 356 Artemisinin combination treatment (ACT), for malaria, 644 Arterial system, development of, 347 Arthritis juvenile idiopathic, investigations of suspected, 537b–538b septic, 532–533, 532b Arthrogryposis, 524 Artificial respiratory support, 195–197 Ascites, 40–41 chronic liver disease and, 413–414 ASD. see Atrial septal defect (ASD) Aspartate aminotransferase (AST), 406 Asphyxia, chronic in-utero partial, 178 Aspirin, 2 overdose of, 114 poisoning, 114, 114b ASQ. see Ages & Stages Questionnaire™ (ASQ) Assent, in clinical trial, 714–716 Assistive listening devices, 626 Association(s) in congenital abnormalities, 166–167 in statistics, 737 AST. see Aspartate aminotransferase (AST) Asthma, 331–332 allergy and, 311 child with, 679b spacer or nebulizer treatment, 744 Astigmatism, 599 Astrocytes, 545 Asylum seekers, unaccompanied, and maltreatment, 138 Ataxia, 63 Ataxia telangiectasia, 286t Atenolol, for heart failure, 354t Atomoxetine, for ADHD, 75 Atopic dermatitis, 310–311, 492, 492b–493b, 493f ATP7B gene, 417 Atresia biliary, 408, 409b duodenal, 254–255 rectal, 168, 169f Atria, anatomy of, 341–342 Atrial fibrillation, 359 Atrial flutter, 359 Atrial septal defect (ASD), 345b Atrioventricular (AV) nodal re-entry tachycardia (AVNRT), 360, 360f Atrioventricular (AV) node, 356 Atrioventricular (AV) re-entry tachycardia (AVRT), 359–360 Atrioventricular septal defects (AVSD), 345b Attachment, in children’s mental health, 467–468 Attention deficit hyperactivity disorder (ADHD), 74–76, 309b common co-morbidities of, 76 management of, 75–76 medications for, 477 pathophysiology of, 75 risk factors for, 74–75 Attention problems, Down’s syndrome and, 466b Audiogram, 622f Audiometry, 620–621 Auditory brainstem implants, 626 Auditory brainstem response (ABR) testing, 621, 623f Auditory dyssynchrony, 618 Auditory neuropathy, 618 Auditory processing tests, 623 Auditory system, 612 Auspitz sign, 493 Autism spectrum disorders, 76–77 diagnosis of, 76–77 Down’s syndrome and, 466b management of, 77 pathophysiology of, 77 risk factors for, 76 Autoantibodies, in systemic sclerosis, 537 Autoimmune conditions genetic susceptibility in, 529 hearing loss and, 618 Autoimmune haemolytic anaemia (AIHA), 450 Autoimmune hepatitis (AIH), 414 Autoimmune liver disease, 414 Autoimmune thrombocytopenia (AITP), maternal, 172–173 Autoinflammatory disease, 540 Automated auditory brainstem response (AABR), 49 Autonomic compensatory changes, in heart failure, 352 Autonomous actions, 675–676 Autonomy, 675–676, 740 Autosomal aneuploidies, 145 Autosomal dominant genetic disorders, 148–150, 149b, 149f Autosomal recessive genetic disorders, 150–151, 150f, 150t, 151b Autosomal recessive hyper-IgE syndrome, 288t AVNRT. see Atrioventricular (AV) nodal re-entry tachycardia (AVNRT) AVP. see Arginine vasopressin (AVP) AVRT. see Atrioventricular (AV) re-entry tachycardia (AVRT) AVSD. see Atrioventricular septal defects (AVSD) Azathioprine, for inflammatory bowel disease, 273 Azelaic acid, for acne, 494 Azoles, in antifungal therapy, 283 B B cells, suppression of, steroids in, 414 ‘Back to Sleep’ campaign, 9 Baclofen, for spasticity, 64 Bacteria, 278–280, 278f cell wall of, 278–279 disruption of, 281 classification of, 278, 279t pathogenesis of, 279–280 structure of, 278–279, 279f Bacterial conjunctivitis, 606 Bacteroides thetaiotaomicron, 263 Bandages, 490 Bar charts, 725, 725f Baroreceptors, 90, 350 in heart failure, 352 Bartter syndrome, 368–369 Basic and advanced life support, science of, 80–82 airway adjuncts in, 81 airway opening manoeuvres in, 81 endotracheal intubation and, 81–82, 81b–82b, 82f laryngeal mask airway in, 81, 81f paediatric airway in, 80–81, 80b

774Index Bath additives, 489 Bayley-III Screening test, 54 Beau’s lines, 486t Becker muscular dystrophy, 69 Beckwith-Wiedemann syndrome, 155, 224 Behavioural development, 464–465 Behavioural problems management of, 474–477 consistency in, 475 e-therapies in, 477 family therapy in, 476, 476b individual psychological therapy in, 476–477, 476b parenting in, 475–476 patience in, 475–476 positivity in, 475 responsivity in, 475 social/environmental, 474–475 structure in, 475 systemic intervention in, 476 pathways to, 469–473, 469t sleep, 470, 470b 4Ps framework for, 470t Beneficence, 676, 740 Benign tumour, 421 Benzodiazepines, 569 Benzyl peroxide, for acne, 494 Bereavement, 669–671, 669b children’s responses to, 670 Bernard-Soulier syndrome, 458 Best evidence topic (BET) diagnosis in, 745t prognosis in, 752t treatment in, 744t, 747t BET. see Best evidence topic (BET) Beta-blockers, for heart failure, 354t Bias, 4 epidemiological data and, 16 reduction of, 748–749 Bicarbonate (HCO3), 85 Bile, 30f, 404, 404b Biliary atresia, 408, 409b Biliary tract, 404b physiology of, 404 Biliary tree, anatomy of, 404, 405f Bilirubin, 406 encephalopathy, 197–198 excretion of, 199 metabolism of, 198–199, 198b, 198f disorders of, 407–408, 407b protein-displacing effect on, 695 Binary data, 723 Bioavailability, 687 Biobanking, in clinical trial, 717 Biochemical tests, for liver, 406, 406b Biologic therapies, for inflammatory musculoskeletal disorders, 539–540 Biomarkers, clinical, 705, 705b, 721 Biopsy, types of, 489b BiPAP. see Biphasic positive airway pressure (BiPAP) Biphasic positive airway pressure (BiPAP), 87 Birth and death, linkage of, 11, 11b transitional changes after, 176–177 Birth registration, 11 Birth weight, low, 12 Bite marks, and physical abuse, 127–128 Blaschko’s lines, 479–480 Blau syndrome, 541t Bleeding disorders, investigation of, 455–456, 456t Blinding, 749 Blood maternal, 160 oxygenation of, 322 Blood-brain barrier, 550, 550b Blood gas abnormalities in, 84–85, 85t patterns of, 84t Blood glucose control, in postresuscitation care, 84 Blood glucose regulation, 500–501 anatomy for, 500, 500f diseases of, 502–503 type 1 diabetes, 502–503, 504b type 2 diabetes, 503 embryology of, 500 physiology of, 500–501, 501f Blood groups, 459–460, 459t, 460b Blood pressure (BP) assessment of, 379b control of, 351, 352t pathophysiology of, 379–380, 381f examination of, 34 measurement of, 379 Blood transfusions, 459–461, 460b complications of, 460–461 of other blood products, 460 red cell, 460 for sickle cell disease, 453 Blood vessels, peripheral, 342, 343f Body composition, 234–236 growth and, 233–236 measurement of, 234–236, 235f, 236t Body mass index, 234 Bone(s) development and pathology of, 524–526 infection in, organisms associated with, 290t rapid growth of, of infancy, 406b remodeling, 525–526 Bone-anchored hearing aids (BAHA), 49–50 Bone marrow failure, 453–455, 453b–454b, 454t Boolean operators, 742–743, 742t Botulinum toxin for drooling, 70 for spasticity, 64–65 Box-and-whisker plots, 726, 727f Boyle’s law, in plethysmography, 327 Bradykinin pathway, in hereditary angioedema, 310b Brain, general anatomy of, 551f Brain disorders, in emotional and behavioural function, 473 Brain injury mechanisms of, during hypoxiaischaemia, 211 patterns of, 211–212, 212f Brain malformations, 66 Brain tumours, 423, 437b Brainstem death, 681b Branchio-oto-renal syndrome (BOR), 619 Breast milk, composition of, 239, 239t Breastfeeding, 208–209, 209f, 239 scientific evidence supporting, 240–241, 241b Breath sounds, normal, 324–325, 324f Breath tests, 268, 268f Breathing control of, 320–321, 320f rate and pattern of, 322–323 British Paediatric Association, 4–5 Bronchi, anatomy of, 319 Bronchiectasis, 332, 332f, 333t Bronchiolitis, 330–331, 331b, 331f Bronchomalacia, 193 Bronchopulmonary dysplasia, 188 Bronchopulmonary sequestration, 193 Bruising, physical abuse and, 126–127, 127b, 127f ageing of, 127 differential diagnosis of, 127 investigations to exclude a bleeding disorder and, 127 Brunner’s cells, 262 Bruton’s disease, 286t Bulimia nervosa, 472 Bulla, 488t Bullying, and maltreatment, 136–137 Bundle, definition of, 760–761 Burns, 104–105 classification of, 105t local and systemic reactions in, 105 pathophysiology of, 104–105, 104b in physical abuse, 128 risk factors for, 104 Burns unit, 105 C Cachexia, 665 Cadaveric organ donation, 682b–683b CAF. see Common Assessment Framework (CAF) Café-au-lait macules, 496 CAH. see Congenital adrenal hyperplasia (CAH) Calcineurin inhibitors, topical, 490 Calcium channel blockers, 565 for hypertension, 382t Calcium currents, medications acting on, 568 Calculi, metabolic disorders causing, 385b Cancer aetiology of, 425 definition of, 421–423

775 development of, 421–423, 422b, 422f epidemiology of, 423–426 genetics, 430 incidence of, 423, 424f investigations and diagnostic work-up for, 428 long-term follow-up for, 435, 435t malignancies of, 437 management of, 430–433 chemotherapy in, 430–432, 431b, 431t–433t, 432f oncological emergencies in, 433, 434t, 435b radiotherapy in, 432–433 supportive care in, 434t surgery in, 433 pathology of, 428 patterns of presentation of, 426–427, 427t radiology for, 428, 428t risk factors for environmental, 425–426 genetic, 426 survivors of childhood cancer as, 426 risk stratification for, 430 staging of, 428, 429t survival rates for, 423–424, 424f symptom control and palliative care for, 437 treatment for, principles of, 428, 429b tumour markers for, 428 Capillary refill time (CRT), 33–34 CAPS. see Cryopyrin-associated periodic syndrome (CAPS) Captopril, for heart failure, 354t Caput medusae, 413 Carbamazepine, 568 Carbamoyl phosphate, 574 Carbohydrate, absorption of, in small intestine, 260–261 Carbon dioxide in control of breathing, 320 in gas exchange, 321–322 Carbon monoxide poisoning, 106 Cardiac arrest, in children, 79 Cardiac catheterization, 361–362, 362t Cardiac compensatory changes, in heart failure, 352 Cardiac compressions, for cardiopulmonary arrest, 83 Cardiac conduction, 353–356 Cardiac disease, liver and, 419 Cardiac lesions duct-dependent, 348b syndrome associated with, 349t Cardiac output, 89, 348b factors affecting, 352f Cardiac silhouette, 361 Cardiff Child Protection Systematic Review Group, 126 Cardinal veins, development of, 346 Cardiology, 341–364 Cardiomyopathy in children, 363 dilated, 351 Cardiopulmonary arrest, 83–84 causes of, 83 treatment of, 83 Cardiopulmonary resuscitation (CPR), 83 Cardiovascular system anomalies of, recurrence risk of, 348 examination of, 37–39, 37b abnormal heart sounds and murmurs in, 37–38, 37t, 38f innocent heart murmurs in, 38–39 jugular venous pulse, 39 physiology of, 348–351 Care, uncertainties in, reduction of, 5–6 Carotid bruit, 38 Carter effect, 259b Cartilage cells, 521 Case-based journal clubs, 753–754 Catabolism, prevention of, 586 Cataract childhood, 595 management of, 595 congenital, 595 Catecholamines, heart rate and, 349–350 Categorical imperative, 674 Catheter shunts, for fetal pleural effusions, 161–162 Causation, in statistics, 737 CBT. see Cognitive behaviour therapy (CBT) CD40 ligand deficiency, 287t CDI. see Central diabetes insipidus (CDI) Ceftriaxone, 696 for shock, 91–92 Cefuroxime, 533, 701 Cell apoptosis, neural, 547–548 Cell differentiation, brain, 545–546, 546b Cell growth, neural, 547–548 Cell migration, neural, 546 Cell surface receptors, 499 Cell wall synthesis, inhibitors of, 701 Cellular hyperexcitable state, 566 Censored observations, 736 Central diabetes insipidus (CDI), 386 Central hypotonia, 65–66, 65b Central nervous system (CNS), 209–214 bilirubin toxicity in, 199 development of, 167, 168f, 543–550 cell differentiation, 545–546, 546b cell growth and apoptosis, 547–548 cell migration, 546 craniocaudal and centrifugal, 548 inside-out, 543–544 ion channels and action potential, 547 myelination, 547 neuronal plasticity, 548–549, 548f, 549b synaptogenesis, 546–547 embryology of, 209, 544f, 546t infections of, in children, 290–291 inflammatory diseases, 552 stimulants, 636t Central projections, hearing and, 612 Centrifugal development, 548 Centromere, 141 Cephalosporins, 701 for shock, 91–92 CER. see Control event rate (CER) Cerebellar examination, 555b Cerebellar lesions, 42 Cerebellar system disease, acquired nystagmus due to, 604 Cerebellum, 42 Cerebral oedema, in acute liver failure, 412 Cerebral palsy, 63–65, 63b–64b common patterns of, 63t conditions mimicking, 64t management of, 64–65 Cerebral visual impairment, 598 Cerebrospinal fluid (CSF), 550–552 meningitis and, 551–552 Cervical cord, damage to, 67 Cestodes, 280 CF. see Cystic fibrosis (CF) CFTR protein. see Cystic fibrosis transmembrane conductance regulator (CFTR) protein CGD. see Chronic granulomatous disease (CGD) Chance, epidemiological data and, 15 Charcot-Marie-Tooth syndrome, 67 CHARGE syndrome, 614t Chemoprophylaxis, for tuberculosis, 646–647 Chemoreceptors, in shock, 90 Chemotherapy, 430–432, 431b, 432f general side effects of, 432t myeloablative, 432 phase specificity and mechanisms of action in, 431t specific side effects of, 433t stem cell transplant and, 432 Chest radiography, 197 Chest x-ray, 361 Chief cells, 259 Child abuse, 119 in children’s well-being, 468 Child-centred care, lack of, 761 Child deaths fallen, 643 major causes of, 642f occurrence of, 641 Child development, 45–59 change in neurology with growth in, 46b cognitive theory and, 50–51, 50b key principles of, 45 patterns of, 57–58, 57f delay in multiple domains in, 57 delayed, 57 deviated but normal, 57 leapfrog, 57 normal, 57 plateau or static, 57 in preterm infant, 57–58 regression, 57 senses in, 47–50

776Index study of, 51–56, 52b commonly used developmental screening tools in, 53–56, 56t developmental concerns in, 56, 56t interaction of domains in, 56, 56b speech and language development in, 52–53 Child development team, 58–59 Child health, 641–657 determinants of, 17, 17f, 18b indicators in measuring, 642–643, 643t inequalities of, 17–19, 642–643 causes of, 17–18 measurement of, 18, 18b tackling, 18–19 international programmes in, 643 role of, 643 main indices of population, 10–12 deaths in later childhood, 12 under five year mortality, 12 identification of cause of death, 11 infant mortality rate, 10–11, 10b linkage of births and deaths, 11, 11b low birth weight, 12 measurement of, 12–13, 12b–13b neglected issues in, 654–655 screening programmes in, 24 antenatal, 24 for children, 24 newborn and infant, 24 sustainable development goals for, 656–657 Childhood obesity, health inequalities and, 18–19, 19b pattern of, 19t Childhood visual impairment, epidemiology of, 589 Child labour, 655–656 Child Protection Plan, 120, 120f Children armed conflict and, 656 behavioural threshold testing in, 621–623 cardiac symptoms and conditions in, 362–363 examination of, 31–35 healthcare services for, 761 history-taking in, 28–29, 29b medicines for, 3 prescribing for, 696–697 protection of, 119–139 quality problems facing, 759 rational use of medicines in, 702 research importance of, 2, 3b relevance of, 3 respiratory medicines in, pharmacology of, 334–338 advances in, 338–339 seriously ill or injured, recognition and management of, 79, 80b with special needs, 61–77 definition and numbers, 61–62 developmental regression in, 73–74 global developmental impairment and learning disability in, 72 with neurodisability, medical issues common to, 69–71 sensory development in, 71–72 social development of, 72 specific developmental disorders of, 74–77 street, 656 vulnerable, 655–656 well-being of, 467b Children Act of 1989, 119 Children’s Voices project, 28 Child-resistant packaging, 112 Child survival, 641b epidemiology and, 641–643 major threats to, 643–650 Chi-squared test, 734 Chloramphenicol, 701–702 Chlorothiazide, 505, 506b Cholestatic liver disease, genetically inherited, 409, 410t Chondrocytes, 521 Choreo-athetoid, 63 Chorionic villus sampling (CVS), 162f, 165 Chorioretinal coloboma, 591f Chromosomal abnormalities/disorders, 141–142, 144 in genital development, 391 types of, 141–142 Chromosomal deletion, 142 in imprinting disorders, 154, 154f partial, 146 Chromosomal duplication, 142 partial, 146 Chromosome testing, 142–144 Chromosome translocation, 141, 144 reciprocal, 144 Robertsonian, 144 Chromosomes, 141 Chronic conditions, coordination of care for, 761 Chronic disease, short stature and, 222 Chronic fatigue syndrome, 473–474 symptoms of, 474t Chronic granulomatous disease (CGD), 288t, 289b Chronic in-utero partial asphyxia, 178 Chronic kidney disease (CKD), 388–389, 389t Chronic obliterative bronchiolitis (COB), 332 Chronic pain syndromes, during adolescence, 634 Ciclosporin-A, for autoimmune hepatitis, 414 Ciliary dyskinesias, 334, 334f Circumoral cyanosis, 36 CIs. see Confidence intervals (CIs) Citrulline, 574 CKD. see Chronic kidney disease (CKD) Clamping, of umbilical cord, 176, 176b Clarithromycin, 702 Cleft lip, 168, 169f Cleft palate, 168, 169f Clinical care bundles, 760–761 Clinical need, identification of, 709, 709f Clinical ‘omics’, 722, 722t Clinical practice, principles of, 715b Clinical research, 703–722 biomarkers of, 705, 705b challenges facing in, 704b common trial designs of, 706, 707b, 708t evolution of attitudes to, 4–5, 5b methods of, 704–709 regulation of, 5, 6b role of, 703–704, 704b setting of, 704–706, 705f trial phases I-IV of, 705, 706t Clinical significance of clinical trial, 719 statistical significance versus, 737 Clinical study, future approaches of, 721–722 Clinical trial(s) data analysis of, 718–721 designing of, 709–712 enrolling young people in, 712–717 biobanking in, 717 consent and assent in, 714–716 information sharing in, 712–714 randomization in, 716–717, 716b refusal of participation in, 716 follow-up of, 718–721 influence on practice of, 720–721, 721b monitoring of, 717–718, 717b reporting of, 720, 720b role of, in paediatric oncology, 424–425 Clinical trial of investigational medicinal products (CTIMPs), 684, 715b Clinician, quality improvement and, 757–769 Clonus, 42 Clostridium tetani, 653–654 Clotting factors, vitamin K dependent, 457 Clotting screen, 456t Clubbing, 35–36, 36t, 486t proposed scientific explanations for, 35–36 CNS. see Central nervous system (CNS) Coagulase negative staphylococcus (CONS), 204 Coagulation, for haemostasis, 455, 455f Coagulation disorders acquired, 457–458 hereditary, 456–457 Coagulopathy, acute liver failure and, 412 Co-amoxiclav, 701 Coarctation, of aorta, 347b COB. see Chronic obliterative bronchiolitis (COB) Cocaine, as teratogen, 171 Child development (Continued)

777 Cochlea, 610, 611f Cochlear implants, 49–50, 625–626 diagram of, 625f Cochlear microphonics, 618 Cochrane reviews, 748b, 748f Codeine, metabolism of, 700 Coeliac disease, 262 diagnostic blood tests for, 269, 269b genetic/environmental interaction in, 263–264 Cognitive behaviour therapy (CBT) for ADHD, 75 in emotional and behavioural problem management, 476–477, 476b Cognitive development, in emotional and behavioural development, 464 Cognitive theory, 50–51, 50b Collodion baby, 481, 481f Collusion, 669, 669b Coloboma, 590, 591f Colomycin, nebulized and intravenous, 337 Colon, functions of, 262 Colonic transit studies, 271 Combined variable immunodeficiency (CVID), 286t Common Assessment Framework (CAF), 123–124, 123f Communication in adolescence, 632–633 good, 27–28 physician-parent-child, 28 skills for, 28b Compensatory changes, in heart failure, 352–353, 353f Complement deficiency, 288t Complementary foods for preterm infants, scientific evidence of, 241–242, 242t for term infants, 248–249, 248b Complex feeding problems, 470–471 4Ps framework for, 471t Compliance, 319–320 Complicated grief, 670b ‘Complicated’ malnutrition, 652 Compound alginate preparations, for gastro-oesophageal reflux disease, 69 Computed tomography, cranial, 558 Concomitant squints, 601 Concrete operational stage, of cognitive development, 464 Conducting airways, of respiratory system, 319 Conductive hearing loss, 609b, 622f, 624–625 Cones, of retina, 592–593 Confidence intervals (CIs), 15, 728–730, 729b, 750t Confounding, epidemiological data and, 16–17 Congenital adrenal hyperplasia (CAH), 396–397, 396f, 397b 21-hydroxylase deficient, 517 Congenital anomalies, 164–168 Congenital bullous ichthyosiform erythroderma, 491t Congenital diaphragmatic hernia, 192–193 Congenital dystrophy, of levator muscle, 605b Congenital heart disease, 341, 343–347 Congenital lobar emphysema, 193–194 Congenital malformation, definition of, 165–167 Congenital pulmonary airway malformations, 194 Congenital pulmonary lymphangiectasis (CPL), 194 Congenital thoracic malformations (CTMs), in lung development, 317 Conjugate horizontal eye movement, inter-nuclear control of, 602 Conjugated neonatal jaundice, 408 Conjunctivitis, bacterial, 606 Connective tissue disease, investigation of, 538b CONS. see Coagulase negative staphylococcus (CONS) Consent in clinical trial, 714–716 for paediatric research studies, 684 and UK law, in adolescence, 633b, 634 Consequentialism, 674 Consequentialists, 674 Consistency, in emotional and behavioural problem management, 475 Constipation, 70, 665 investigation in, 270 medications for, 272 motility studies and, 270 opioid-induced, 665 Constricted uterus, 523–524 Consultation, style of, 28 Continuous data, 723 Continuous positive airway pressure (CPAP), 87 Contraception, in sexually transmitted infections, 638 Contrast studies, of gastrointestinal tract, 270 Control event rate (CER), 750t Convergence insufficiency, 599b Convergence-retraction nystagmus, 604 Convulsive status epilepticus, 94 cause of, 94–95 Coordination of care, for chronic conditions, 761 Cord compression, 666 Corpus callosum, 548 Correlation, 734–735 Cortical spreading depression, 564 Corticosteroids, 539 for autoimmune hepatitis, 414 for inflammatory bowel disease, 273 inhaled, 312 pharmacology of, in children, 336, 337f Corticotrophin-releasing hormone (CRH), 515 Cortisol bile and, 409 in children’s mental health, 468 Cough, 323–324 Cox regression, 736 CPAP. see Continuous positive airway pressure (CPAP) CPL. see Congenital pulmonary lymphangiectasis (CPL) CPR. see Cardiopulmonary resuscitation (CPR) Crackles, 324–325, 324f Cranial defects, 544 Cranial nerves, 554f examination of, 555b in head injury, 94b supplying the eye, 600t Cranial neuralgias, 566 Craniocaudal development, 548 Craniostenosis, 209–210, 210f Craniosynostosis, 209–210, 210f C-reactive protein (CRP), 535 Crescendo pain, 666 CRH. see Corticotrophin-releasing hormone (CRH) Crigler-Najjar syndrome, type I and II, 407–408 Critical care, paediatric, 79–100 Crohn’s disease, 262 CRP. see C-reactive protein (CRP) CRT. see Capillary refill time (CRT) Crust, 488t Cryopyrin-associated periodic syndrome (CAPS), 541t Cryptorchidism, 398–399 Crystallins, 595 CSF. see Cerebrospinal fluid (CSF) CTIMPs. see Clinical trial of investigational medicinal products (CTIMPs) CTMs. see Congenital thoracic malformations (CTMs) Cuffed endotracheal tubes, use of, 82 Cumulative meta-analysis, 4, 4b Cupula, 612 Cushing’s syndrome, 517 as growth failure cause, 223 Cutaneous mosaicism, 479–480 patterns of, 480f, 480t CVID. see Combined variable immunodeficiency (CVID) CVS. see Chorionic villus sampling (CVS) Cyanosis, 36–37, 36b Cysteinyl leukotriene receptor antagonists, 312–313 Cystic fibrosis (CF), 151, 333–334, 333b gastrointestinal manifestations of, 274 gene, abnormal, manipulation of, 338 key respiratory medicines in, 337–338 antibiotics, 337–338 mucolysis, 338 liver disease associated with, 417

778Index Cystic fibrosis transmembrane conductance regulator (CFTR) protein, 333 Cystic renal disease, 386–387 Cysts, duplication, 257 Cytochrome P450 enzyme, 692 paracetamol and, 415 Cytokine modulators, in rheumatic disease, 540b, 540t Cytomegalovirus (CMV) infection, congenital, 173–174, 173t, 618 D DAI. see Diffuse axonal injury (DAI) ‘Dancing eyes’, 427, 427b Dantrolene, for spasticity, 65 DAT. see Direct antiglobulin test (DAT) Data, statistics describing, 726–731, 726b displaying, 724–726, 724b spread of, 728, 729f types of, 723–724, 724f Data analysis, in clinical trial, 718–721 Data transformation, tests of, 727 DBPCFC. see Double-blind, placebocontrolled food challenge (DBPCFC) DCD. see Developmental coordination disorder (DCD) DDH. see Developmental dysplasia of hip (DDH) Deafness, genetic causes of, 612b Death advanced care planning and, 668 and birth, linkage of, 11, 11b cause of, identification of, 11 before onset of labour, 11 in or shortly after labour, 11 postnatal, 11 children’s responses to, 670 factors influencing, 670–671 imminent, 667 in later childhood, 12 period and practicalities to, 667–668 place of care and, 667–668 Death registration, 11 Declaration of Helsinki, 4–5, 683–684 Deficiency of lL-1-receptor antagonist (DIRA), 541t Deformation, definition of, 165 Dehydration, 95–96 types of, 96 Dehydroepiandrosterone (DHEA), 515 Dehydroepiandrosterone sulphate (DHEAS), 515 DEJ. see Dermal-epidermal junction (DEJ) Denominator, 10 as measure of disease frequency, 13 Dental neglect, 133 Denver Developmental Screening Test© (DENVER II©), 53, 55f advantages of, 53 limitations of, 53, 53b–54b Deontology, 674–675 Deoxyribonucleic acid (DNA), 147 replication of, inhibition of, 281 sequencing of, for imprinting disorders, 154 Dermal-epidermal junction (DEJ), 482 defect in, 482b Dermatitis, atopic, 310–311, 492, 492b–493b, 493f Dermatology, 479–498 Dermis anatomy of, 484, 484f development of, 482 Desaturation, during sleep, 321b Descriptive observational study, 15 Desferrioxamine, 116 Desmopressin, for haemophilia A, 457 Determinants, of well-being, 468 Developmental coordination disorder (DCD), 77 co-morbidities of, 77 diagnosis of, 77 management of, 77 Developmental dysplasia of hip (DDH), 523 Developmental problems, 61–77 causes of, 62, 62t clinical manifestations of, 62–69 Developmental regression, 73–74 Developmental screening tools, 53–56, 56t Dexamethasone, antenatal treatment with, for congenital adrenal hyperplasia (CAH), 397 Dexamphetamine, for ADHD, 75–76 Dextrocardia, 343b DHEA. see Dehydroepiandrosterone (DHEA) DHEAS. see Dehydroepiandrosterone sulphate (DHEAS) Diabetes insipidus, 386 cranial, 511 familial neurohypophyseal, 512b nephrogenic, 511 Diabetes mellitus, 499–519 insulin pump therapy, 21b maternal, 172 other forms of, 503 type 1, 502–503, 504b type 2, 503 Diabetic ketoacidosis (DKA), 97 Diamond-Blackfan anaemia, 445–446, 454t transient erythroblastopenia of childhood versus, 446, 447t Diaphragm, in respiration, 321, 322t Diarrhoea, 264–267, 264t, 644 acute, 265 see also Gastroenteritis non -infective causes of, 266 aetiology of, 265 chronic, 266–267, 266b with blood, 267b infective causes of, 266 non-infective causes of, 267 osmotic, 264–265 secretory, 265 Diazepam, 569 for spasticity, 64 Diazoxide, 505, 506b DIC. see Disseminated intravascular coagulation (DIC) Diet, children’s mental health and, 467 Dietary Reference Intakes (DRIs), 237, 238t Dietary Reference Values (DRVs), 237, 237f, 238t Dietary treatment, principles of, 585–587 ketogenic diet, 586–587 supplying a deficient product, 585–586 supplying adequate energy and preventing catabolism, 586 toxic substrate by restricting intake, preventing, 586 Diffuse axonal injury (DAI), 102, 103f Diffuse parenchymal lung disease (DPLD), 332 Diffusion, 321–322 DiGeorge syndrome, 146, 287t, 528t Digestion, 257 Digoxin, for heart failure, 354t Diiodotyrosine, 506–507 Dilated cardiomyopathy, 351 Dimorphic fungi, 280 DIRA. see Deficiency of lL-1-receptor antagonist (DIRA) Direct antiglobulin test (DAT), 448 Disability, epidemiology of, 61b Discrete data, 723 Disease frequency, measures of, 13–14 measures of, 9–10 Disease-modifying antirheumatic drugs (DMARDs), for autoimmune hepatitis, 414 Disorders of sexual development (DSD), 395–398 assessment of, 395 examination in, 395 history in, 395 investigations in, 395 gonadal dysgenesis, 398 management of, 396b true hermaphroditism, 398 virilization of 46 XX female, causes of, 396–397 of 46 XY male, inadequate, 397–398 incomplete, idiopathic, 398 Dispersion, 728 Disruption, definition of, 165 Disruptive behaviour, 471–472, 472b 4Ps framework for, 472t Disseminated intravascular coagulation (DIC), 457 Disseminated intravascular coagulopathy, 411b Dissociation curve, of oxygen, 322, 323f