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dc.contributor.authorOgero, Morris
dc.contributor.authorSarguta, Rachel J
dc.contributor.authorMalla, Lucas
dc.contributor.authorJalemba, Aluvaala
dc.contributor.authorAgweyu, Ambrose
dc.contributor.authorEnglish, Mike
dc.contributor.authorOnyango, Nelson O
dc.contributor.authorAkech, Samuel
dc.identifier.citationOgero M, Sarguta RJ, Malla L, Aluvaala J, Agweyu A, English M, Onyango NO, Akech S. Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review. BMJ Open. 2020 Oct 19;10(10):e035045. doi: 10.1136/bmjopen-2019-035045. PMID: 33077558; PMCID: PMC7574949.en_US
dc.description.abstractObjectives: To identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs). Design: Systematic review of peer-reviewed journals. Data sources: MEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019. Eligibility criteria: We included model development studies predicting in-hospital paediatric mortality in LMIC. Data extraction and synthesis: This systematic review followed the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies framework. The risk of bias assessment was conducted using Prediction model Risk of Bias Assessment Tool (PROBAST). No quantitative summary was conducted due to substantial heterogeneity that was observed after assessing the studies included. Results: Our search strategy identified a total of 4054 unique articles. Among these, 3545 articles were excluded after review of titles and abstracts as they covered non-relevant topics. Full texts of 509 articles were screened for eligibility, of which 15 studies reporting 21 models met the eligibility criteria. Based on the PROBAST tool, risk of bias was assessed in four domains; participant, predictors, outcome and analyses. The domain of statistical analyses was the main area of concern where none of the included models was judged to be of low risk of bias. Conclusion: This review identified 21 models predicting in-hospital paediatric mortality in LMIC. However, most reports characterising these models are of poor quality when judged against recent reporting standards due to a high risk of bias. Future studies should adhere to standardised methodological criteria and progress from identifying new risk scores to validating or adapting existing scores. Prospero registration number: CRD42018088599.en_US
dc.publisherUniversity of Nairobien_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectpaediatric intensive & critical care; paediatrics; statistics & research methods.en_US
dc.titlePrognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic reviewen_US

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