Article Text

Original research
Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review
  1. Morris Ogero1,2,
  2. Rachel Jelagat Sarguta1,
  3. Lucas Malla2,
  4. Jalemba Aluvaala2,
  5. Ambrose Agweyu2,
  6. Mike English2,3,
  7. Nelson Owuor Onyango1,
  8. Samuel Akech2
  1. 1School of Mathematics, University of Nairobi College of Biological and Physical Sciences, Nairobi, Kenya
  2. 2Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
  3. 3Nuffield Department of Medicine and Department of Paediatrics, Oxford University, Oxford, UK
  1. Correspondence to Morris Ogero; mogero{at}kemri-wellcome.org

Abstract

Objectives 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.

  • statistics & research methods
  • paediatrics
  • paediatric intensive & critical care
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Footnotes

  • Twitter @SmockinMorries

  • Contributors The roles of the contributors were as follows: ME, SA and AA conceptualised the study. MO, LM and JA conducted electronic searches to identify eligible models and did analyses. MO drafted the initial manuscript with SA, NOO, RJS, AA and ME contributed to its development. All authors read and approved the final manuscript.

  • Funding Funds from The Wellcome Trust (#207522) awarded to ME as a Senior Fellowship together with additional funds from a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Programme (#092654 and #203077) supported this work. SA was supported by the Initiative to Develop African Research Leaders (IDeAL) Wellcome Trust award (#107769).

  • Disclaimer The funders had no role in drafting or submitting this manuscript.

  • Map disclaimer The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article or uploaded as online supplemental information.

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