ArticleDiagnostic and prognostic research2024
Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis.
Article in Diagnostic and prognostic research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Evaluating Artificial Intelligence Models for ICU Length of Stay Prediction: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2026Review
- Global guidelines, local realities: toward equitable neurocritical care, local data generation and practice patterns in low- and middle-income countries.Critical care science · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundWe evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs).
methodsWe identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe). Model performance was assessed in terms of discrimination and calibration.
resultsOut of 144 eligible models, 140 were excluded due to a high risk of bias, predictors unavailable in LIMCs, or insufficient model description. Among 11,338 participants, the remaining models showed good discrimination for predicting in-hospital mortality (3 models), with areas under the curve (AUCs) ranging between 0.76 (95% CI 0.71-0.81) and 0.84 (95% CI 0.77-0.89). An AUC of 0.74 (95% CI 0.70-0.78) was found for predicting ICU admission risk (one model). All models showed signs of miscalibration and overfitting, with extensive heterogeneity between countries.
conclusionsAmong the available COVID-19 prognostic models, only a few could be validated on data collected from LMICs, mainly due to limited predictor availability. Despite their discriminative ability, selected models for mortality prediction or ICU admission showed varying and suboptimal calibration.
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Registered trials
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