2024-03-28T09:42:58Zhttp://repisalud.isciii.es/oai/requestoai:repisalud.isciii.es:20.500.12105/112432023-12-18T19:01:40Zcom_20.500.12105_15322com_20.500.12105_2051com_20.500.12105_2060com_20.500.12105_2052col_20.500.12105_16978col_20.500.12105_2061
Repisalud
author
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
2020-10-28T13:27:42Z
2020-10-28T13:27:42Z
2020-09-23
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
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.
eng
The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.
journal article
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