Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients
Por:
González-Cebrián A, Borràs-Ferrís J, Ordovás-Baines JP, Hermenegildo-Caudevilla M, Climente-Marti M, Tarazona S, Vitale R, Palací-López D, Sierra-Sánchez JF, Saez de la Fuente J and Ferrer A
Publicada:
22 sep 2022
Ahead of Print:
22 sep 2022
Categoría:
Multidisciplinary
Resumen:
The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.
Filiaciones:
González-Cebrián A:
Multivariate Statistical Engineering Group, Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, València, España
Borràs-Ferrís J:
Multivariate Statistical Engineering Group, Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, València, España
:
Pharmacy Service, Hospital Universitario Dr. Peset, València, España
:
Pharmacy Service, Hospital Universitario Dr. Peset, València, España
:
Pharmacy Service, Hospital Universitario Dr. Peset, València, España
Tarazona S:
Multivariate Statistical Engineering Group, Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, València, España
Vitale R:
Univ. Lille, CNRS, LASIRE - UMR 8516 - Laboratory of Advanced Spectroscopy for Interaction, Reactivity and Environmental Studies, Lille, France
Palací-López D:
Multivariate Statistical Engineering Group, Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, València, España
Sierra-Sánchez JF:
Pharmacy Service, Hospital Universitario Jerez de la Frontera, Área de Gestión Sanitaria Jerez-Costa Noroeste y Sierra de Cádiz, Jerez de la Frontera, España
Saez de la Fuente J:
Pharmacy Service, Hospital Universitario Ramón y Cajal, Madrid, España
Ferrer A:
Multivariate Statistical Engineering Group, Department of Applied Statistics and Operational Research and Quality, Universitat Politècnica de València, València, España
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