Development and evaluation of a machine learning-based in-hospital COvid-19 disease outcome predictor (CODOP): a multicontinental retrospective study
- University of Turku, Finland
- Max Planck Institute of Experimental Medicine, Germany
- University of Malaga, Spain
- General University Hospital of Alicante, Spain
- Gregorio Marañón University Hospital, Spain
- Hospital Universitario 12 De Octubre, Spain
- Hospital Costa del Sol, Spain
- Hospital Británico of Buenos Aires, Argentina
- Hospital Santa Cruz - Caja Petrolera de Salud, Bolivia
- Hospital of San Juan de Dios, Bolivia
- Hospital Honduras Medical Centre, Honduras
- Hospital Velez Sarsfield, Argentina
- Hospital Italiano de Buenos Aires, Argentina
- Clausthal University of Technology, Germany
- Max Planck Institute for Experimental Medicine, Germany
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 nine 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.
Data availability
The raw patient data used in this study are not freely available due to legal restrictions of the ethical committees of the different hospitals. However, they can be accessed upon request to the Scientific Committees of these organisms. An exception to this is the patient data from the USA cohort, which has been published elsewhere. All the model's numerical data necessary to generate all figures can be found in the submitted source data tables. Furthermore, all supplementary tables can be found in Supplementary File 1.
Article and author information
Author details
Juan Miguel Antón-Santos
Jose Manuel Ramos-Rincon
David Gómez-Varela
Funding
Max Planck Society (Publication cost)
- David Gómez-Varela
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Klén et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Development and evaluation of a machine learning-based in-hospital COvid-19 disease outcome predictor (CODOP): a multicontinental retrospective study
eLife 11:e75985.
https://doi.org/10.7554/eLife.75985
Further reading
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- Epidemiology and Global Health
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