SynthesisBMJ (Clinical research ed.)2022
Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.
Synthesis in BMJ (Clinical research ed.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep neural networks excel in COVID-19 disease severity prediction-a meta-regression analysis.Scientific reports · 2025Pooled it
- Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses.BMC health services research · 2023Pooled it
- Predicting COVID-19 prognosis in the ICU remained challenging: external validation in a multinational regional cohort.Journal of clinical epidemiology · 2022Pooled it
- Predictive value of clinical features and CT radiomics in the efficacy of hip preservation surgery with fibula allograft.Journal of orthopaedic surgery and research · 2023Trial
- Development and Validation of an Electronic Health Record-Derived Prediction Model for Preventing COVID-19 Hospitalization and Death.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Myocardial Injury and Predictors of In-Hospital Mortality in Severe or Critical COVID-19: A Single-Center Observational Cohort Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Validation of CURB-65, CRB-65, NEWS2, qSOFA, and 4C scores for predicting mortality in COVID-19 patients across seven emergency Departments in Colombia.Frontiers in medicine · 2026Article
- How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19.Research synthesis methods · 2026Review
- Article
- Comparison of the performance of four clinical prediction rules for mortality in patients with COVID-19.PloS one · 2026Article
- External validation and comparison of clinical prediction models for cisplatin-associated acute kidney injury: a single-centre retrospective study.Journal of pharmaceutical health care and sciences · 2025Article
- AI-derived CT biomarker score for robust COVID-19 mortality prediction across multiple waves and regions using machine learning.Scientific reports · 2025Article
- Prognostic value of novel indices combining Shock Index, Reverse Shock Index, age, and oxygen saturation for predicting mortality in COVID-19 patients in Iran at emergency department triage: a cross-sectional study.Acute and critical care · 2025Article
- ACCREDIT: Validation of clinical score for progression of COVID-19 while hospitalized.Global epidemiology · 2025Article
- Article
- 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.Diagnostic and prognostic research · 2024Article
- Boosting the accuracy of existing models by updating and extending: using a multicenter COVID-19 ICU cohort as a proxy.Scientific reports · 2024Article
- Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.Scientific reports · 2024Observational
- A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.Neuroradiology · 2024Article
- COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
38 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.
designTwo stage individual participant data meta-analysis.
settingSecondary and tertiary care.
participants46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.
methodsEight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters.
main outcome measures30 day mortality or in-hospital mortality.
resultsDatasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).
conclusionThe prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.
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