Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study
Young Pablo; Gross Artega Rosmery; Purohit Disha; Ramirez Juan Ignacio; Lumbreras Carlos; Nunez-Cortes Jesus Millan; Gomez-Huelgas Ricardo; Castagna Rosa; Pollan Javier A.; Gomez-Varela David; Martin-Escalante Maria Dolores; Funke Nico; Canales Beltran Magdy Teresa; Klen Riku; Pugliese Florencia; Casas-Rojo Jose Manuel; Boietti Bruno; Rivas-Ruiz Francisco; Onieva-Garcia Maria angeles; Valdez Pascual Ruben; Pedrera-Jimenez Miguel; Anton-Santos Juan Miguel; Lalueza Blanco Antonio; Huespe Ivan A.; Titto Omonte Estela Edith; Garcia Barrio Noelia; Ramos-Rincon Jose Manuel; Leiding Benjamin
Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study
Young Pablo
Gross Artega Rosmery
Purohit Disha
Ramirez Juan Ignacio
Lumbreras Carlos
Nunez-Cortes Jesus Millan
Gomez-Huelgas Ricardo
Castagna Rosa
Pollan Javier A.
Gomez-Varela David
Martin-Escalante Maria Dolores
Funke Nico
Canales Beltran Magdy Teresa
Klen Riku
Pugliese Florencia
Casas-Rojo Jose Manuel
Boietti Bruno
Rivas-Ruiz Francisco
Onieva-Garcia Maria angeles
Valdez Pascual Ruben
Pedrera-Jimenez Miguel
Anton-Santos Juan Miguel
Lalueza Blanco Antonio
Huespe Ivan A.
Titto Omonte Estela Edith
Garcia Barrio Noelia
Ramos-Rincon Jose Manuel
Leiding Benjamin
eLIFE SCIENCES PUBL LTD
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022081154763
https://urn.fi/URN:NBN:fi-fe2022081154763
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]