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Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
VMT. de Jong, RZ. Rousset, NE. Antonio-Villa, AG. Buenen, B. Van Calster, OY. Bello-Chavolla, NJ. Brunskill, V. Curcin, JAA. Damen, CA. Fermín-Martínez, L. Fernández-Chirino, D. Ferrari, RC. Free, RK. Gupta, P. Haldar, P. Hedberg, SK. Korang, S....
Language English Country England, Great Britain
Document type Journal Article, Meta-Analysis
NLK
ProQuest Central
from 1996-01-01
Nursing & Allied Health Database (ProQuest)
from 1996-01-01
Health & Medicine (ProQuest)
from 1996-01-01
- MeSH
- Data Analysis MeSH
- COVID-19 * MeSH
- Humans MeSH
- Hospital Mortality MeSH
- Prognosis MeSH
- Models, Statistical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Meta-Analysis MeSH
OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 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 The BMJ, and through PROSPERO, reference checking, and expert knowledge. 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. METHODS: Eight 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 MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets 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). CONCLUSION: The 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.
Cochrane Netherlands University Medical Centre Utrecht Utrecht University Netherlands
Data Analytics and Methods Task Force European Medicines Agency Amsterdam Netherlands
Department of Biomedical Data Sciences Leiden University Medical Centre Leiden Netherlands
Department of Cardiovascular Sciences College of Life Sciences University of Leicester Leicester UK
Department of Development and Regeneration KU Leuven Leuven Belgium
Department of Infectious Diseases Karolinska University Hospital Stockholm Sweden
Department of Pharmacy University Hospital Centre of Nîmes Nîmes France
Department of Respiratory Medicine University Hospitals of Leicester NHS Trust Leicester UK
Department of Respiratory Sciences College of Life Sciences University of Leicester Leicester UK
Dirección de Investigación Instituto Nacional de Geriatría Mexico City Mexico
Division of Infection and Immunity University College London London UK
Division of Infectious Diseases Department of Medicine Solna Karolinska Institute Stockholm Sweden
EPI centre KU Leuven Leuven Belgium
Faculty of Chemistry Universidad Nacional Autónoma de México México City Mexico
Health Data Research UK and Institute of Health Informatics University College London London UK
Heidelberger Institut für Global Health Universitätsklinikum Heidelberg Germany
Industrial Engineering Department Universidade Federal do Rio Grande do Sul Porto Alegre Brazil
Infectious Diseases Service UnityPoint Health Des Moines Des Moines IA USA
Institute for Global Health University College London London UK
Institute of Microbiology of the Czech Academy of Sciences Prague Czech Republic
John Walls Renal Unit University Hospitals of Leicester NHS Trust Leicester UK
Laboratory of Clinical Chemistry and Haematology Jeroen Bosch Hospital Den Bosch Netherlands
Maxima MC Veldhoven the Netherlands
MD PhD Program Faculty of Medicine National Autonomous University of Mexico Mexico City Mexico
NIHR Leicester Biomedical Research Centre University of Leicester Leicester UK
School of Population Health and Environmental Sciences King's College London London UK
Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark
University of Iowa Carver College of Medicine Iowa City IA USA
References provided by Crossref.org
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