Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.

de Jong VMT, Rousset RZ, Antonio-Villa NE, Buenen AG, Van Calster B, Bello-Chavolla OY, Brunskill NJ, Curcin V, Damen JAA, Fermín-Martínez CA, Fernández-Chirino L, Ferrari D, Free RC, Gupta RK, Haldar P, Hedberg P, Korang SK, Kurstjens S, Kusters R, Major RW, Maxwell L, Nair R, Naucler P, Nguyen TL, Noursadeghi M, Rosa R, Soares F, Takada T, van Royen FS, van Smeden M, Wynants L, Modrák M, CovidRetro collaboration , Asselbergs FW, Linschoten M, CAPACITY-COVID consortium , Moons KGM, Debray TPA

BMJ 378 (-) e069881 [2022-07-12; online 2022-07-12]

To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Two stage individual participant data meta-analysis. Secondary and tertiary care. 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. 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. 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. 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. 30 day mortality or in-hospital mortality. 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). 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.

Category: Health

Type: Journal article

PubMed 35820692

DOI 10.1136/bmj-2021-069881

Crossref 10.1136/bmj-2021-069881

pmc: PMC9273913


Publications 8.0.0