Associated factors with the mortality rate in patients with COVID-19 - decision trees vs. logistic regression

ABSTRACT

BACKGROUND: Given the global burden of COVID-19 mortality, this study intended to determine the factors affecting mortality in patients with COVID-19 using decision tree analysis and logistic regression model in Kermanshah province, 2020. METHODS: This cross-sectional study was conducted on 7799 patients with COVID-19 admitted to the hospitals of Kermanshah province. Data gathered from February 18 to July 9, 2020, were obtained from the vice-chancellor for the health of Kermanshah University of Medical Sciences. The performance of the models was compared according to the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. RESULTS: According to the decision tree model, the most important risk factors for death due to COVID-19 were age, body temperature, admission to intensive care unit (ICU), prior hospital visit within the last 14 days, and cardiovascular disease. Also, the multivariate logistic regression model showed that the variables of age [OR = 4.47, 95% CI (3.16 -6.32)], shortness of breath [OR = 1.42, 95% CI (1.0-2.01)], ICU admission [OR = 3.75, 95% CI (2.47-5.68)], abnormal chest X-ray [OR = 1.93, 95% CI (1.06-3.41)], liver disease [OR = 5.05, 95% CI (1.020-25.2)], body temperature [OR = 4.93, 95% CI (2.17-6.25)], and cardiovascular disease [OR = 2.15, 95% CI (1.27-3.06)] were significantly associated with the higher mortality of patients with COVID-19. The area under the ROC curve for the decision tree model and logistic regression was 0.77 and 0.75, respectively. CONCLUSIONS: Identifying risk factors for mortality in patients with COVID-19 can provide more effective interventions in the early stages of treatment and improve the medical approaches provided by the medical staff.