TY - JOUR

T1 - Clinical prediction models for mortality in patients with covid-19

T2 - external validation and individual participant data meta-analysis

AU - de Jong, Valentijn M T

AU - Rousset, Rebecca Z

AU - Antonio-Villa, Neftalí Eduardo

AU - Buenen, Arnoldus G

AU - Van Calster, Ben

AU - Bello-Chavolla, Omar Yaxmehen

AU - Brunskill, Nigel J

AU - Curcin, Vasa

AU - Damen, Johanna A A

AU - Fermín-Martínez, Carlos A

AU - Fernández-Chirino, Luisa

AU - Ferrari, Davide

AU - Free, Robert C

AU - Gupta, Rishi K

AU - Haldar, Pranabashis

AU - Hedberg, Pontus

AU - Korang, Steven Kwasi

AU - Kurstjens, Steef

AU - Kusters, Ron

AU - Major, Rupert W

AU - Maxwell, Lauren

AU - Nair, Rajeshwari

AU - Naucler, Pontus

AU - Nguyen, Tri-Long

AU - Noursadeghi, Mahdad

AU - Rosa, Rossana

AU - Soares, Felipe

AU - Takada, Toshihiko

AU - van Royen, Florien S

AU - van Smeden, Maarten

AU - Wynants, Laure

AU - Modrák, Martin

AU - Asselbergs, Folkert W

AU - Linschoten, Marijke

AU - Moons, Karel G M

AU - Debray, Thomas P A

N1 - © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - COVID-19

KW - Data Analysis

KW - Hospital Mortality

KW - Humans

KW - Models, Statistical

KW - Prognosis

U2 - 10.1136/bmj-2021-069881

DO - 10.1136/bmj-2021-069881

M3 - Journal article

C2 - 35820692

VL - 378

JO - The BMJ

JF - The BMJ

SN - 0959-8146

M1 - e069881

ER -