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Title: A Predictive Model and Risk Factors for Case Fatality of COVID-19
Author: Álvarez Mon, Melchor
Ortega, Miguel A.
Gasulla, Óscar
Fortuny Profitós, Jordi
Mazaira Font, Ferran A.
Saurina, Pablo
Monserrat, Jorge
Plana, María N.
Troncoso, Daniel
Moreno, José Sanz
Muñoz, Benjamin
Arranz, Alberto
Varona, Jose F.
López Escobar, Alejandro
Asúnsolo del Barco, Angel
Keywords: COVID-19
Issue Date: 8-Jan-2021
Publisher: MDPI
Abstract: This study aimed to create an individualized analysis model of the risk of intensive care unit (ICU) admission or death for coronavirus disease 2019 (COVID-19) patients as a tool for the rapid clinical management of hospitalized patients in order to achieve a resilience of medical resources. This is an observational, analytical, retrospective cohort study with longitudinal follow-up. Data were collected from the medical records of 3489 patients diagnosed with COVID-19 using RT-qPCR in the period of highest community transmission recorded in Europe to date: February-June 2020. The study was carried out in in two health areas of hospital care in the Madrid region: the central area of the Madrid capital (Hospitales de Madrid del Grupo HM Hospitales (CH-HM), n = 1931) and the metropolitan area of Madrid (Hospital Universitario Príncipe de Asturias (MH-HUPA) n = 1558). By using a regression model, we observed how the different patient variables had unequal importance. Among all the analyzed variables, basal oxygen saturation was found to have the highest relative importance with a value of 20.3%, followed by age (17.7%), lymphocyte/leukocyte ratio (14.4%), CRP value (12.5%), comorbidities (12.5%), and leukocyte count (8.9%). Three levels of risk of ICU/death were established: low-risk level (<5%), medium-risk level (5-20%), and high-risk level (>20%). At the high-risk level, 13% needed ICU admission, 29% died, and 37% had an ICU-death outcome. This predictive model allowed us to individualize the risk for worse outcome for hospitalized patients affected by COVID-19.
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It is part of: Journal of Personalized Medicine, 2021, vol. 11, num. 1
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Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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