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      Risk predictors of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis

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          Summary

          Background

          The ability to accurately predict early progression of dengue to severe disease is crucial for patient triage and clinical management. Previous systematic reviews and meta-analyses have found significant heterogeneity in predictors of severe disease due to large variation in these factors during the time course of the illness. We aimed to identify factors associated with progression to severe dengue disease that are detectable specifically in the febrile phase.

          Methods

          We did a systematic review and meta-analysis to identify predictors identifiable during the febrile phase associated with progression to severe disease defined according to WHO criteria. Eight medical databases were searched for studies published from Jan 1, 1997, to Jan 31, 2020. Original clinical studies in English assessing the association of factors detected during the febrile phase with progression to severe dengue were selected and assessed by three reviewers, with discrepancies resolved by consensus. Meta-analyses were done using random-effects models to estimate pooled effect sizes. Only predictors reported in at least four studies were included in the meta-analyses. Heterogeneity was assessed using the Cochrane Q and I 2 statistics, and publication bias was assessed by Egger's test. We did subgroup analyses of studies with children and adults. The study is registered with PROSPERO, CRD42018093363.

          Findings

          Of 6643 studies identified, 150 articles were included in the systematic review, and 122 articles comprising 25 potential predictors were included in the meta-analyses. Female patients had a higher risk of severe dengue than male patients in the main analysis (2674 [16·2%] of 16 481 vs 3052 [10·5%] of 29 142; odds ratio [OR] 1·13 [95% CI 1·01–1·26) but not in the subgroup analysis of studies with children. Pre-existing comorbidities associated with severe disease were diabetes (135 [31·3%] of 431 with vs 868 [16·0%] of 5421 without; crude OR 4·38 [2·58–7·43]), hypertension (240 [35·0%] of 685 vs 763 [20·6%] of 3695; 2·19 [1·36–3·53]), renal disease (44 [45·8%] of 96 vs 271 [16·0%] of 1690; 4·67 [2·21–9·88]), and cardiovascular disease (nine [23·1%] of 39 vs 155 [8·6%] of 1793; 2·79 [1·04–7·50]). Clinical features during the febrile phase associated with progression to severe disease were vomiting (329 [13·5%] of 2432 with vs 258 [6·8%] of 3797 without; 2·25 [1·87–2·71]), abdominal pain and tenderness (321 [17·7%] of 1814 vs 435 [8·1%] of 5357; 1·92 [1·35–2·74]), spontaneous or mucosal bleeding (147 [17·9%] of 822 vs 676 [10·8%] of 6235; 1·57 [1·13–2·19]), and the presence of clinical fluid accumulation (40 [42·1%] of 95 vs 212 [14·9%] of 1425; 4·61 [2·29–9·26]). During the first 4 days of illness, platelet count was lower (standardised mean difference −0·34 [95% CI −0·54 to −0·15]), serum albumin was lower (−0·5 [–0·86 to −0·15]), and aminotransferase concentrations were higher (aspartate aminotransferase [AST] 1·06 [0·54 to 1·57] and alanine aminotransferase [ALT] 0·73 [0·36 to 1·09]) among individuals who progressed to severe disease. Dengue virus serotype 2 was associated with severe disease in children. Secondary infections ( vs primary infections) were also associated with severe disease (1682 [11·8%] of 14 252 with vs 507 [5·2%] of 9660 without; OR 2·26 [95% CI 1·65–3·09]). Although the included studies had a moderate to high risk of bias in terms of study confounding, the risk of bias was low to moderate in other domains. Heterogeneity of the pooled results varied from low to high on different factors.

          Interpretation

          This analysis supports monitoring of the warning signs described in the 2009 WHO guidelines on dengue. In addition, testing for infecting serotype and monitoring platelet count and serum albumin, AST, and ALT concentrations during the febrile phase of illness could improve the early prediction of severe dengue.

          Funding

          Wellcome Trust, National Institute for Health Research, Collaborative Project to Increase Production of Rural Doctors, and Royal Thai Government.

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          Most cited references66

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          Measuring inconsistency in meta-analyses.

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            Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

            Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
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              Bias in meta-analysis detected by a simple, graphical test.

              Funnel plots (plots of effect estimates against sample size) may be useful to detect bias in meta-analyses that were later contradicted by large trials. We examined whether a simple test of asymmetry of funnel plots predicts discordance of results when meta-analyses are compared to large trials, and we assessed the prevalence of bias in published meta-analyses. Medline search to identify pairs consisting of a meta-analysis and a single large trial (concordance of results was assumed if effects were in the same direction and the meta-analytic estimate was within 30% of the trial); analysis of funnel plots from 37 meta-analyses identified from a hand search of four leading general medicine journals 1993-6 and 38 meta-analyses from the second 1996 issue of the Cochrane Database of Systematic Reviews. Degree of funnel plot asymmetry as measured by the intercept from regression of standard normal deviates against precision. In the eight pairs of meta-analysis and large trial that were identified (five from cardiovascular medicine, one from diabetic medicine, one from geriatric medicine, one from perinatal medicine) there were four concordant and four discordant pairs. In all cases discordance was due to meta-analyses showing larger effects. Funnel plot asymmetry was present in three out of four discordant pairs but in none of concordant pairs. In 14 (38%) journal meta-analyses and 5 (13%) Cochrane reviews, funnel plot asymmetry indicated that there was bias. A simple analysis of funnel plots provides a useful test for the likely presence of bias in meta-analyses, but as the capacity to detect bias will be limited when meta-analyses are based on a limited number of small trials the results from such analyses should be treated with considerable caution.
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                Author and article information

                Contributors
                Journal
                Lancet Infect Dis
                Lancet Infect Dis
                The Lancet. Infectious Diseases
                Elsevier Science ;, The Lancet Pub. Group
                1473-3099
                1474-4457
                1 July 2021
                July 2021
                : 21
                : 7
                : 1014-1026
                Affiliations
                [a ]Section of Adult Infectious Disease, Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
                [b ]Global Digital Health Unit, Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK
                [c ]MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
                [d ]Department of Social Medicine, Hatyai Hospital, Songkhla, Thailand
                [e ]Department of Pediatrics, Queen Sirikit National Institute of Child Health, Bangkok, Thailand
                [f ]Oxford University Clinical Research Unit, Wellcome Trust Major Overseas Programme, Ho Chi Minh City, Vietnam
                [g ]Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
                [h ]Antimicrobial Resistance Collaborative, Imperial College London, London, UK
                Author notes
                [* ]Correspondence to: Dr Sorawat Sangkaew, Section of Adult Infectious Disease, Department of Infectious Disease, Imperial College London, London W12 0NN, UK sorawat.sangkaew16@ 123456imperial.ac.uk
                [†]

                Joint senior authors

                Article
                S1473-3099(20)30601-0
                10.1016/S1473-3099(20)30601-0
                8240557
                33640077
                6c1dcf19-45a5-4a01-be3f-fb684e158568
                © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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                Infectious disease & Microbiology
                Infectious disease & Microbiology

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