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

ABSTRACT: 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

SUBMITTER: Torres-Macho J 

PROVIDER: S-EPMC7598151 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature