Identified Factors in COVID-19 Patients in Predicting Mortality

Abstract

ABSTRACT Background: The coronavirus disease 2019 (COVID-19) pandemic has led to a significant increase in global mortality rates. Numerous studies have been conducted to identify the factors associated with mortality in COVID-19 cases. In these studies, overall mortality was evaluated in patients, and no distinction was made as ward or intensive care mortality. Aim: This study aims to determine mortality-related factors in patients who died while in the ward. This could enable us to review the indications for intensive care hospitalization in possible pandemics. Materials and Method: This retrospective study was conducted on a cohort of 237 patients who applied to our institution between January 2020 and December 2021 with the diagnosis of COVID-19. Demographic characteristics, length of stay, type of admission (emergency ward or outpatient clinic), presence of comorbidities, thoracic computerized tomography (CT) findings, and laboratory findings were extracted from the hospital database. The demographic and laboratory results of both deceased and recovered patients were compared. Results: While many demographic and laboratory findings were statistically significant in the initial analysis, multiple logistic regression analysis showed that decreased albumin levels (adjusted OR = 0.23, 95% CI = 0.09 – 0.57), increased troponin (adjusted OR = 1.03, 95% CI = 1.02 – 1.05), and procalcitonin (adjusted OR = 3.46, 95% CI = 1.04 – 11.47) levels and higher partial thromboplastin time (PTT) (adjusted OR = 1.18, 95% CI = 1.09 – 1.28) values, presence of diabetes mellitus (DM) in patients (adjusted OR = 2.18, 95% CI = 1.01 – 4.69, P = 0.047), and admission to hospital from the emergency department (adjusted OR = 5.15, 95% CI = 1.45 – 18.27, P = 0.011) were significantly associated with mortality when adjusted for age. When a predictive model is constructed with these variables, this model predicted mortality statistically significant (AUC = 0.904, 95% CI = 0.856 – 0.938, P < 0.001), with a sensitivity of 77.2% (95% CI, 67.8 – 85), a specificity of 91.2% (95% CI, 85.1 – 95.4), a positive predictive value (PPV) of 86.7% (95% CI, 72 – 85.3), and an negative predictive value (NPV) of 84.4% (95% CI, 79.4 – 89.6). Conclusion: In this study, we may predict mortality among COVID-19-diagnosed patients admitted to the ward via this model which has the potential to provide guidance for reconsidering the indications for intensive care unit (ICU) admission.