Logo image
Back
Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study
Journal article   Open access   Peer reviewed

Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study

Belen Gutierrez-Gutierrez, Maria Dolores del Toro, Alberto M. Borobia, Antonio Carcas, Inmaculada Jarrin, Maria Yllescas, Pablo Ryan, Jeronimo Pachon, Jordi Carratala, Juan Berenguer, …
The Lancet infectious diseases, v 21(6), pp 783-792
01 Jun 2021
DOI: https://doi.org/10.1016/S1473-3099(21)00019-0
PMID: 33636145
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
http://www.thelancet.com/article/S1473309921000190/pdfView
Published, Version of Record (VoR) Open
url
https://doi.org/10.1016/S1473-3099(21)00019-0View
Published, Version of Record (VoR) Open

Abstract

Infectious Diseases Life Sciences & Biomedicine Science & Technology
Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. Methods In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2 center dot 5% (95% CI 1 center dot 4-4 center dot 3) for patients with phenotype A, 30 center dot 5% (28 center dot 5-32 center dot 6) for patients with phenotype B, and 60 center dot 7% (53 center dot 7-67 center dot 2) for patients with phenotype C (log-rank test p<0 center dot 0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5 center dot 3% [95% CI 3 center dot 4-8 center dot 1] for phenotype A, 31 center dot 3% [28 center dot 5-34 center dot 2] for phenotype B, and 59 center dot 5% [48 center dot 8-69 center dot 3] for phenotype C; external validation cohort: 3 center dot 7% [2 center dot 0-6 center dot 4] for phenotype A, 23 center dot 7% [21 center dot 8-25 center dot 7] for phenotype B, and 51 center dot 4% [41 center dot 9-60 center dot 7] for phenotype C). Interpretation Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated. Funding Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundacion SEIMC/GeSIDA. Copyright (c) 2021 Elsevier Ltd. All rights reserved.

Metrics

10 Record Views
71 citations in Web of Science
68 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Infectious Diseases
Logo image