Effect of APACHE-II and the Age-Adjusted CHARLSON Comorbidity Index at Predicting Mortality in Patients with COVID-19

Abstract

The COVID-19 pandemic disproportionately affects patients with comorbidities. Comprehensive comorbidity assessment is important in establishing the risk stratification of patients with COVID-19 after hospital admission. In this study, our aim is to investigate the effectiveness of Acute Physiology and Chronic Health Assessment II (APACHE-II) and Age Adjusted Charlson Comorbidity Index (ACCI) in predicting mortality in COVID-19 patients admitted to the Intensive Care Unit (ICU). Patients aged >18 years who were admitted to the intensive care unit with the diagnosis of COVID-19 pneumonia in the Health Sciences University Bursa Yüksek İhtisas Training and Training Hospital between July 2021 and September 2021 were included in the study. The medical records of the patients were then scanned into the hospital automation system. Demographics, comorbidities, clinical features, laboratory parameters, APACHE-II score, treatments, and outcomes were recorded in a standard form. ACCI score was calculated from the data and recorded. The 276 patients analyzed were divided into two groups as surviving (n=129) and developing mortality (n=147). The mortality rate was 58.93%, mostly male (58%), median age 65 years, ACCI score 1 (IQR.3) and APACHE-II score 2 (IQR.8). There was no difference between the groups in terms of age, gender distribution and APACHI-II score (P= 0.519, P= 0.927, P= 0.364, respectively). The groups did not differ in terms of comorbidity except for chronic renal failure (CRF), and CRF was significantly higher in patients who developed mortality (P= 0.037). The ACCI score was found to be higher in patients who developed mortality (P= 0.034). Death risk; Those with an ACCI score of >2 were 2.26 times higher than those with an ACCI score of ≤2 (P= 0.021). The APACHI-II score did not differ between the groups in terms of mortality (P= 0.380). As a result, high ACCI score was found to be effective in predicting mortality. It could potentially be used to identify at-risk patients infected with COVID-19 and to predict their clinical status.