{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Klen R"],"funding":["Max Planck Society"],"pagination":["e75985"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9129872"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11"],"pubmed_abstract":["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-"],"journal":["eLife"],"pubmed_title":["Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study."],"pmcid":["PMC9129872"],"funding_grant_id":["Publication cost"],"pubmed_authors":["Klen R","Martin-Escalante MD","Gross Artega R","Boietti B","Pedrera-Jimenez M","Titto Omonte EE","Gomez-Varela D","Ramirez JI","Ramos-Rincon JM","Onieva-Garcia MA","Lumbreras C","Garcia Barrio N","Lalueza Blanco A","Leiding B","Gomez-Huelgas R","Anton-Santos JM","Castagna R","Young P","Pollan JA","Purohit D","Casas-Rojo JM","Valdez PR","Pugliese F","Huespe IA","Funke N","Rivas-Ruiz F","Nunez-Cortes JM","Canales Beltran MT"],"additional_accession":[]},"is_claimable":false,"name":"Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.","description":"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-","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2026-07-14T17:07:02.25Z","creation":"2024-11-08T09:17:13.787Z"},"accession":"S-EPMC9129872","cross_references":{"pubmed":["35579324"],"doi":["10.7554/eLife.75985"]}}