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                  <mods:namePart>Álvarez Mon, Melchor</mods:namePart>
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                  <mods:namePart>Ortega, Miguel A.</mods:namePart>
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                  <mods:namePart>Gasulla, Óscar</mods:namePart>
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               <mods:name>
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                  <mods:namePart>Fortuny Profitós, Jordi</mods:namePart>
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                  <mods:namePart>Mazaira-Font, Ferran</mods:namePart>
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                  <mods:namePart>Saurina, Pablo</mods:namePart>
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                  <mods:namePart>Monserrat, Jorge</mods:namePart>
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                  <mods:namePart>Plana, María N.</mods:namePart>
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                  <mods:namePart>Troncoso, Daniel</mods:namePart>
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                  <mods:namePart>Moreno, José Sanz</mods:namePart>
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                  <mods:namePart>Muñoz, Benjamin</mods:namePart>
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                  <mods:namePart>Arranz, Alberto</mods:namePart>
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                  <mods:namePart>Varona, José F.</mods:namePart>
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                  <mods:namePart>López Escobar, Alejandro</mods:namePart>
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                  <mods:namePart>Asunsolo, Angel</mods:namePart>
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                  <mods:dateIssued encoding="iso8601">2021-03-01T07:45:32Z2021-03-01T07:45:32Z2021-01-082021-02-11T11:05:37Z</mods:dateIssued>
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               <mods: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 (&lt;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.</mods:abstract>
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               <mods:accessCondition type="useAndReproduction">cc by (c) Álvarez Mon et al., 2021 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess</mods:accessCondition>
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                  <mods:topic>COVID-19</mods:topic>
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                  <mods:topic>Mortalitat</mods:topic>
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                  <mods:topic>COVID-19</mods:topic>
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                  <mods:topic>Mortality</mods:topic>
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                  <mods:title>A Predictive Model and Risk Factors for Case Fatality of COVID-19</mods:title>
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