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00925njm 22002777a 4500 dc Klén, Riku author Purohit, Disha author Gómez-Huelgas, Ricardo author Casas-Rojo, José Manuel author Antón-Santos, Juan Miguel author Núñez-Cortés, Jesús Millán author Lumbreras, Carlos author Ramos-Rincón, José Manuel author García Barrio, Noelia author Pedrera-Jiménez, Miguel author Lalueza Blanco, Antonio author Martin-Escalante, María Dolores author Rivas-Ruiz, Francisco author Onieva-García, Maria Ángeles author Young, Pablo author Ramirez, Juan Ignacio author Titto Omonte, Estela Edith author Gross Artega, Rosmery author Canales Beltrán, Magdy Teresa author Valdez, Pascual Ruben author Pugliese, Florencia author Castagna, Rosa author Huespe, Ivan A author Boietti, Bruno author Pollan, Javier A author Funke, Nico author Leiding, Benjamin author Gómez-Varela, David author 2022-05-17 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. 10.7554/eLife.75985 2050-084X eLife http://hdl.handle.net/10668/21673 35579324 http://hdl.handle.net/20.500.12105/18650 COVID-19 computational biology human machine-learning medicine prediction systems biology triage Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.