COVID-19 research briefs: Development of a mortality score for hospitalized patients

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Published: 2020-11-14 © 2020 John Wiley & Sons, Inc.

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Clinical question
Can data predict mortality in people hospitalized with COVID-19?

Bottom line
Clinical data and laboratory values can predict mortality in patients hospitalized with COVID-19. (LOE = 2c)

Reference
Knight SR, Ho A, Pius R, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ. 2020;370:m3339.

Study design: Qualitative

Setting: Population-based

Synopsis
Research Brief #63: This consortium of researchers used data from 35,463 patients hospitalized with COVID-19 to develop a clinical score — the Coronavirus Clinical Characterisation Consortium Mortality Score (4C Mortality Score) — to predict mortality, then validated it on another sample of 22,361 patients. All the patients were adults admitted to one of 260 hospitals in England, Scotland, and Wales. The authors used robust approaches to guide this development and validation process. From 41 candidate variables, the final model (0 to 21 points) included age, sex, number of comorbidities, respiratory rate, oxygen saturation, level of consciousness, urea level, and C-reactive protein level. The overall mortality rate was 32% in the development cohort and 30% in the validation cohort. In the validation cohort, the model was 77% accurate, which the authors report is better than other scores. The model was generally best at ruling out mortality than ruling it in. Finally, the score has limited applicability and would not be useful in the community setting with patients at lower risk of death, since well over half the study patients were in a high-risk group.

Henry C. Barry, MD, MS
Professor
Michigan State University
East Lansing, MI

Copyright © 2020 John Wiley & Sons, Inc.