%0 Journal Article
%A Álvarez Mon, Melchor
%A Ortega, Miguel A.
%A Gasulla, Óscar
%A Fortuny Profitós, Jordi
%A Mazaira Font, Ferran A.
%A Saurina, Pablo
%A Monserrat, Jorge
%A Plana, María N.
%A Troncoso, Daniel
%A Sanz Moreno, José
%A Muñoz, Benjamin
%A Arranz, Alberto
%A Varona, Jose F.
%A Lopez Escobar, Alejandro
%A Asúnsolo del Barco, Angel
%T A Predictive Model and Risk Factors for Case Fatality of COVID-19
%D 2021
%@ 2075-4426
%U https://hdl.handle.net/20.500.14352/7047
%X 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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