Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis
Authors: de Jong Valentijn M T , Rousset Rebecca Z , Antonio-Villa Neftalí Eduardo , Buenen Arnoldus G , Van Calster Ben , Bello-Chavolla Omar Yaxmehen , Brunskill Nigel J , Curcin Vasa , Damen Johanna A A , Fermín-Martínez Carlos A , Fernández-Chirino Luisa , Ferrari Davide , Free Robert C , Gupta Rishi K , Haldar Pranabashis , Hedberg Pontus , Korang Steven Kwasi , Kurstjens Steef , Kusters Ron , Major Rupert W , Maxwell Lauren , Nair Rajeshwari , Naucler Pontus , Nguyen Tri-Long , Noursadeghi Mahdad , Rosa Rossana , Soares Felipe , Takada Toshihiko , van Royen Florien S , van Smeden Maarten , Wynants Laure , Modrák Martin ,   the CovidRetro collaboration, Asselbergs Folkert W , Linschoten Marijke ,   CAPACITY-COVID consortium, Moons Karel G M , Debray Thomas P A 
Published: July 2022

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 inThe 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.