Prognostic model for death from COVID-19

• Main Authors: Alexis Álvarez Aliaga (Author), Julio César González Aguilera (Author), Alexis Suárez Quesada (Author), Andrés José Quesada Vázquez (Author), Liannys Lidia Naranjo Flores (Author)
• Format: Book
• Published: Universidad de Ciencias Médicas de La Habana, 2024-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

Introduction: Tools have been used to predict the risk of COVID-19, but none have evaluated their external validity. Objective: To evaluate the capacity of a model, based on prognostic factors, to predict the risk of dying from COVID-19. Material and Methods: A cohort analytical study was carried out on patients with COVID-19 at the "Carlos Manuel de Céspedes" provincial general hospital in the Bayamo municipality, province of Granma, from January 1st, 2020 to March 31st, 2023. Confirmed COVID-19 patients were selected. Death was defined as the dependent variable and sociodemographic variables were considered as independent variables for evaluation, including: toxic habits, comorbidity, biomarkers, phenotypes, and prognostic scales. Results: The Cox proportional regression model demonstrated that the factors associated with the prognosis of death were: classifying phenotype 5 (HR= 6.41; 95% CI= 1.49 to 13.44; p= 0.015); history of arterial hypertension (HR= 2.01; 95% CI= 1.34 to 2.98; p= 0.001); and the RALE scale at 4 or more points (HR= 1.94; 95% CI=1, 47 to 7.90; p= 0.047). The discriminative capacity of the model (C statistic = 0.891) and its calibration were adequate (X2 = 5.384; p = 0.637). Conclusions: The predictive capacity and calibration of the model were adequate. The model can be used as a clinical and epidemiological surveillance instrument, by staging the risk of dying in the most vulnerable subjects.

Item Description

1729-519X