RT Journal Article
T1 Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.
A1 Klen, Riku
A1 Purohit, Disha
A1 Gomez-Huelgas, Ricardo
A1 Casas-Rojo, Jose Manuel
A1 Anton-Santos, Juan Miguel
A1 Nuñez-Cortes, Jesus Millan
A1 Lumbreras, Carlos
A1 Ramos-Rincon, Jose Manuel
A1 Garcia-Barrio, Noelia
A1 Pedrera-Jimenez, Miguel
A1 Lalueza-Blanco, Antonio
A1 Martin-Escalante, Maria Dolores
A1 Rivas-Ruiz, Francisco
A1 Onieva-Garcia, Maria Angeles
A1 Young, Pablo
A1 Ramirez, Juan Ignacio
A1 Titto-Omonte, Estela Edith
A1 Gross-Artega, Rosmery
A1 Canales-Beltran, Magdy Teresa
A1 Valdez, Pascual Ruben
A1 Pugliese, Florencia
A1 Castagna, Rosa
A1 Huespe, Ivan A
A1 Boietti, Bruno
A1 Pollan, Javier A
A1 Funke, Nico
A1 Leiding, Benjamin
A1 Gomez-Varela, David
K1 COVID-19
K1 Computational biology
K1 Human
K1 Machine-learning
K1 Medicine
K1 Prediction
K1 Systems biology
K1 Triage
AB New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.
PB eLife Sciences Publications
YR 2022
FD 2022-05-17
LK http://hdl.handle.net/10668/21673
UL http://hdl.handle.net/10668/21673
LA en
NO Klén R, Purohit D, Gómez-Huelgas R, Casas-Rojo JM, Antón-Santos JM, Núñez-Cortés JM, et al. Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study. Elife. 2022 May 17;11:e75985
DS RISalud
RD Apr 7, 2025