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The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

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 p...

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Autores principales: Torres-Macho, Juan, Ryan, Pablo, Valencia, Jorge, Pérez-Butragueño, Mario, Jiménez, Eva, Fontán-Vela, Mario, Izquierdo-García, Elsa, Fernandez-Jimenez, Inés, Álvaro-Alonso, Elena, Lazaro, Andrea, Alvarado, Marta, Notario, Helena, Resino, Salvador, Velez-Serrano, Daniel, Meca, Alejandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598151/
https://www.ncbi.nlm.nih.gov/pubmed/32977606
http://dx.doi.org/10.3390/jcm9103066
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author Torres-Macho, Juan
Ryan, Pablo
Valencia, Jorge
Pérez-Butragueño, Mario
Jiménez, Eva
Fontán-Vela, Mario
Izquierdo-García, Elsa
Fernandez-Jimenez, Inés
Álvaro-Alonso, Elena
Lazaro, Andrea
Alvarado, Marta
Notario, Helena
Resino, Salvador
Velez-Serrano, Daniel
Meca, Alejandro
author_facet Torres-Macho, Juan
Ryan, Pablo
Valencia, Jorge
Pérez-Butragueño, Mario
Jiménez, Eva
Fontán-Vela, Mario
Izquierdo-García, Elsa
Fernandez-Jimenez, Inés
Álvaro-Alonso, Elena
Lazaro, Andrea
Alvarado, Marta
Notario, Helena
Resino, Salvador
Velez-Serrano, Daniel
Meca, Alejandro
author_sort Torres-Macho, Juan
collection PubMed
description 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.
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spelling pubmed-75981512020-10-31 The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19 Torres-Macho, Juan Ryan, Pablo Valencia, Jorge Pérez-Butragueño, Mario Jiménez, Eva Fontán-Vela, Mario Izquierdo-García, Elsa Fernandez-Jimenez, Inés Álvaro-Alonso, Elena Lazaro, Andrea Alvarado, Marta Notario, Helena Resino, Salvador Velez-Serrano, Daniel Meca, Alejandro J Clin Med Article 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. MDPI 2020-09-23 /pmc/articles/PMC7598151/ /pubmed/32977606 http://dx.doi.org/10.3390/jcm9103066 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Torres-Macho, Juan
Ryan, Pablo
Valencia, Jorge
Pérez-Butragueño, Mario
Jiménez, Eva
Fontán-Vela, Mario
Izquierdo-García, Elsa
Fernandez-Jimenez, Inés
Álvaro-Alonso, Elena
Lazaro, Andrea
Alvarado, Marta
Notario, Helena
Resino, Salvador
Velez-Serrano, Daniel
Meca, Alejandro
The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title_full The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title_fullStr The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title_full_unstemmed The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title_short The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19
title_sort pandemyc score. an easily applicable and interpretable model for predicting mortality associated with covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598151/
https://www.ncbi.nlm.nih.gov/pubmed/32977606
http://dx.doi.org/10.3390/jcm9103066
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