Person:
Vélez Serrano, Daniel

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First Name
Daniel
Last Name
Vélez Serrano
URI
https://hdl.handle.net/20.500.14352/75713
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Matemáticas
Department
Estadística e Investigación Operativa
Area
Estadística e Investigación Operativa
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Author: Alvarado, Marta × End date: 2023 ×

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    The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
    (Journal of Clinical Medicine, 2020) Torres Macho, Juan; Ryan Múrua, Pablo; Valencia, Jorge; Pérez-Butragueño, Mario; Jiménez González De Buitrago, Eva; Fontán-Vela, Mario; Izquierdo García, Elsa; Fernandez-Jimenez, Inés; Álvaro-Alonso, Elena; Lazaro, Andrea; Alvarado, Marta; Notario, Helena; Resino, Salvador; Vélez Serrano, Daniel; Meca, Alejandro
    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. Methods: 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. Results. 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. Conclusions: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.