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Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review
Morris Ogero1,2, Rachel Jelagat Sarguta3, Lucas Malla2
1School of Mathematics, University of Nairobi College of Biological and Physical Sciences, Nairobi, Kenya mogero@kemri-wellcome.org.
Insights
This systematic review found 21 models for predicting child mortality in low- and middle-income countries (LMICs). However, most models have a high risk of bias, indicating poor methodological quality.
Area of Science:
- Global Health
- Epidemiology
- Biostatistics
Background:
- Predictive models for in-hospital paediatric mortality are crucial in low- and middle-income countries (LMICs).
- Assessing the methodological rigor of these models is essential for reliable clinical application.
- Existing models may vary significantly in quality and applicability across diverse LMIC settings.
Purpose of the Study:
- To systematically identify and critically appraise the methodological quality of multivariable prognostic models for in-hospital paediatric mortality in LMICs.
- To evaluate the risk of bias in studies developing these predictive models.
- To provide recommendations for improving the development and reporting of paediatric mortality prediction models in resource-limited settings.
Main Methods:
- Systematic review of peer-reviewed literature from MEDLINE, CINAHL, Google Scholar, and Web of Science.
- Inclusion of model development studies predicting in-hospital paediatric mortality in LMICs.
- Utilized the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CCATS) framework and the Prediction model Risk of Bias Assessment Tool (PROBAST).
Main Results:
- Identified 15 studies reporting 21 distinct models for predicting in-hospital paediatric mortality in LMICs.
- A high risk of bias was identified across the included models, particularly in the statistical analyses domain.
- None of the 21 models were assessed as having a low risk of bias, indicating significant methodological limitations.
Conclusions:
- While 21 models exist for predicting paediatric mortality in LMICs, their reporting quality is generally poor due to high risk of bias.
- Future research should prioritize adherence to standardized methodological criteria and focus on validating or adapting existing models rather than developing new ones.
- Improving the methodological rigor and reporting standards of prognostic models is critical for their effective use in LMICs.
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.
