Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review

• Published in: BMJ open Vol. 10; no. 10; p. e035045
• Main Authors: Ogero, Morris, Sarguta, Rachel Jelagat, Malla, Lucas, Aluvaala, Jalemba, Agweyu, Ambrose, English, Mike, Onyango, Nelson Owuor, Akech, Samuel
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 19-10-2020; BMJ Publishing Group
• Series: Original research
• Subjects: Bias; Child; Hospital Mortality; Hospitals; Humans; Intensive care; Malaria; Medical prognosis; Mortality; Paediatrics; Patients; Pediatrics; Pneumonia; Prognosis; Systematic review; Africa; paediatrics; paediatric intensive & critical care; statistics & research methods
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2019-035045

ObjectivesTo identify and appraise the methodological rigour of multivariable prognostic models predicting in-hospital paediatric mortality in low-income and middle-income countries (LMICs).DesignSystematic review of peer-reviewed journals.Data sourcesMEDLINE, CINAHL, Google Scholar and Web of Science electronic databases since inception to August 2019.Eligibility criteriaWe included model development studies predicting in-hospital paediatric mortality in LMIC.Data extraction and synthesisThis 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.ResultsOur 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.ConclusionThis 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 numberCRD42018088599.