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Multinomial regression analysis of COVID-19 severity in patients of Ecuador

Abstract:

Introduction: Determining specific predictors of severity in the population allows us to identify high-risk groups on which to focus measures to prevent severe progression of COVID-19, reducing related complications. Objective: To analyze by multinomial regression the severity of COVID-19, according to smoking status, comorbidities, Body Mass Index, sex, and age, in patients of a hospital in Ecuador, during the second semester of 2022. Methods: The study was predictive, analytical, observational, cross-sectional and retrospective. The study population consisted of 214 patients diagnosed with COVID-19 confirmed by laboratory tests. A multinomial regression design with likelihood ratio tests was performed. Results: In the multinomial analysis performed in 214 patients hospitalized for COVID-19, the presence of previous comorbidity increased 5.9 times the probability of hospitalization (OR 5.90; 95 % CI 2.78-12.53) and 5 times the risk of ICU admission (OR 5.09; 95 % CI 2.45-10.57) compared to requiring mechanical ventilation. The final model explained 19.7 % of the variability in disease severity. Conclusions: Previous comorbidity represented the main factor independently associated with a higher severity of COVID-19 in hospitalized patients, increasing the risk of both hospitalization and ICU admission over the requirement of mechanical ventilation. Other covariates such as smoking, age, BMI or sex did not show a significant correlation with the level of severity achieved by the disease. Further studies are required to determine additional predictors of clinical progression of COVID-19.