Predictors of Mortality in Hospitalized Patients with COVID-19: A One-Year Case-Control Study (preprint)

ABSTRACT

Objective: To determine the associated factors with mortality, in addition to age and sex, in a high-complexity hospital in Bogota, Colombia, during the first year of the pandemic. Design: A case-control study. Setting: High-complexity center above 2,640 meters above sea level (masl) in Colombia. Methods: A case-control study was conducted on 564 patients admitted to the hospital with confirmed COVID-19. Deceased patients (n 282) and a control group (n 282), matched by age, sex, and month of admission, were included. Clinical and paraclinical variables were retrospectively obtained by systematic revision of clinical records. Multiple imputations by chained equation (MICE) were implemented to account for missing variables. Classification and regression trees (CART) were estimated to evaluate the interaction of associated factors on admission and their role in predicting mortality during hospitalization. Results: Most of the patients included were males in the seventh decade of life. Most of the admissions occurred between July and August 2021. Surprisingly, recovered patients reported heterogeneous symptomatology, whereas deceased patients were most likely to present respiratory distress, dyspnea, and seizures on admission. In addition, the latter group exhibited a higher burden of comorbidities and alterations in laboratory parameters. After the imputation of datasets, CART analysis estimated 14 clinical profiles based on respiratory distress, LDH, dyspnea, hemoglobin, D-dimer, ferritin, blood urea nitrogen, C-reactive protein, PaO2/FiO2, dysgeusia, total bilirubin, platelets, and gastroesophageal reflux disease. The accuracy model for prediction was 85.6% (P < 0.0001). Conclusion: Multivariate analysis yielded a reliable model to predict mortality in COVID-19. This analysis revealed new interactions between clinical and paraclinical features in addition to age and sex. Furthermore, this predictive model could offer new clues for the personalized management of this condition in clinical settings. Keywords SARS-CoV-2, COVID-19, Mortality, Predictors, Risk Factors