APACHE II scoring system to predict mortality of covid-19 patients: a cross-sectional study

Abstract

Background: The coronavirus disease-2019 (COVID-19) pandemic has significantly impacted global health, resulting in millions of reported cases and deaths worldwide. The Acute Physiologic and Chronic Health Evaluation (APACHE) II is a commonly used assessment system for determining the severity of illness in critically unwell patients. This study investigates the feasibility of using the APACHE II scoring system as a mortality predictor for COVID-19. Materials and Methods: In this cross-sectional study, we evaluated 499 confirmed patients with COVID-19 before COVID-19 vaccination, all of whom received standard treatment. Patients with confirmed COVID-19 by polymerase chain reaction (PCR) test were enrolled in this study, and their demographic data, chest computed tomographic (CT) findings, APACHE II score, need for mechanical ventilation, length of in-hospital stay, and outcome (discharge or death) were collected in a checklist. Results: The mean age of the patients was 62.24 ± 18.48 years, composed of 52.5% male and 47.5% female. Respiratory complaints, such as cough, dyspnea, and chest pain, were observed in 56.9% of patients, and 8.8% presented with palpitations, decreased blood pressure (BP), and cardiac arrest. Among these patients, 96.3% were discharged, and 3.6% died. Non-survived patients had significantly higher APACHE II scores than survived patients (12.3 vs. 17.7, P = 0.007). The ROC curve analysis revealed an APACHE II cutoff of 18.5 for predicting mortality in patients with COVID-19, with sensitivity and specificity values of 63% and 84%, respectively. Conclusion: The APACHE II scoring system can be valuable in the emergency department for prioritizing patients based on their severity of illness.

Keywords

• apache ii
• covid-19
• emergency department
• prediction