2026-04-29T04:52:17Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/112432024-11-29T10:36:44Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_15322col_20.500.12105_19609col_20.500.12105_16978

00925njm 22002777a 4500 dc Torres-Macho, Juan author Ryan, Pablo author Valencia, Jorge author Pérez-Butragueño, Mario author Jiménez, Eva author Fontán-Vela, Mario author Izquierdo-García, Elsa author Fernandez-Jimenez, Inés author Álvaro-Alonso, Elena author Lazaro, Andrea author Alvarado, Marta author Notario, Helena author Resino, Salvador author Velez-Serrano, Daniel author Meca, Alejandro author 2020-09-23 This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization. J Clin Med . 2020 Sep 23;9(10):E3066. http://hdl.handle.net/20.500.12105/11243 32977606 10.3390/jcm9103066 2077-0383 Journal of clinical medicine The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.