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Methodological rigor of prognostic models for predicting in-hospital paediatric mortality in low- and middle-income

Morris Ogero1,2, Rachel Sarguta2, Lucas Malla1

  • 1Health Service Unit, KEMRI / Wellcome Trust Research Programme, Nairobi, P.O Box 43640-00100, Kenya.

Wellcome Open Research
|July 30, 2020
PubMed

Insights

This systematic review evaluates prognostic models for in-hospital paediatric mortality in low- and middle-income countries (LMICs). It aims to identify methodological weaknesses and models for external validation to reduce child deaths.

Area of Science:

  • Global Health
  • Medical Informatics
  • Biostatistics

Background:

  • Limited healthcare resources in low- and middle-income countries (LMICs) necessitate efficient treatment decisions to reduce paediatric mortality.
  • Validated prognostic models are crucial for early risk identification and improved resource allocation in paediatric care.

Purpose of the Study:

  • To systematically review and appraise the methodological quality of multivariable prognostic models predicting in-hospital paediatric mortality in LMICs.
  • To identify statistical and methodological shortcomings in existing models.
  • To identify models suitable for external validation.

Main Methods:

  • Systematic review protocol following PRISMA-P guidelines.
  • Comprehensive literature search across MEDLINE, Google Scholar, CINAHL, and Web of Science (inception to 2019).
  • Data extraction using Cochrane Prognosis Methods and CASP checklists; methodological quality assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).

Main Results:

  • This section is not applicable as the abstract describes a planned systematic review and not the results of the review itself.
  • The review is designed to identify methodological rigor and shortcomings in prognostic models for paediatric mortality in LMICs.
  • The findings will highlight models for potential external validation.

Conclusions:

  • This systematic review will provide critical insights into the quality of prognostic models for paediatric mortality in LMICs.
  • Identifying robust models and methodological gaps can guide future research and improve clinical decision-making.
  • The ultimate goal is to contribute to the development of reliable tools to reduce in-hospital paediatric deaths in resource-limited settings.

Keywords:
Prognostic modelsin-hospital paediatric mortalitymodelprediction

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