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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...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7598151 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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