Titre :

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

Auteur(s) :

González-Cebrián, Alba [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Borràs-Ferrís, J. [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Ordovás-Baines, J. P. [Auteur]
Hermenegildo-Caudevilla, M. [Auteur]
Climente-Marti, M. [Auteur]
Tarazona, S. [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Palací-López, D. [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Sierra-Sánchez, J. F. [Auteur]
Hospital Jerez de la Frontera
Saez De La Fuente, J. [Auteur]
Hospital Universitario Ramón y Cajal [Madrid]
Ferrer, A. [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]

Titre de la revue :

PLOS ONE

Nom court de la revue :

PLoS One

Numéro :

17

Pagination :

e0274171

Date de publication :

2022-09-28

ISSN :

1932-6203

Discipline(s) HAL :

Chimie/Chimie théorique et/ou physique

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS

Collections :

• Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516

Date de dépôt :

2024-02-28T22:19:17Z
2024-03-13T12:52:36Z