Predictive Value of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Acute Physiology and Chronic Health Evaluation (APACHE II), and New Early Warning Signs (NEWS-2) Scores Estimate Mortality of COVID-19 Patients Requiring Intensive Care Unit (ICU)

Introduction: Various mortality predictive score models for coronavirus disease-2019 (COVID-19) have been deliberated. We studied how sequential organ failure assessment (SOFA), quick sequential organ failure assessment (qSOFA), acute physiology and chronic health evaluation (APACHE II), and new early warning signs (NEWS-2) scores estimate mortality in COVID-19 patients. Method: We conducted a prospective cohort study of 53 patients with moderate-to-severe COVID-19. We calculated qSOFA, SOFA, APACHE II, and NEWS-2 on initial admission and re-evaluated on day 5. We performed logistic regression analysis to differentiate the predictors of qSOFA, SOFA, APACHE II, and NEWS-2 scores on mortality. Result: qSOFA, SOFA, APACHE II, and NEWS-2 scores on day 5 exhibited a difference between survivors and nonsurvivors (p <0.05), also between ICU and non-ICU admission (p <0.05). The initial NEWS-2 revealed a higher AUC value than the qSOFA, APACHE II, and SOFA score in estimating mortality (0.867; 0.83; 0.822; 0.794). In ICU, APACHE II score revealed a higher AUC value than the SOFA, NEWS-2, and qSOFA score (0.853; 0.832; 0.813; 0.809). Concurrently, evaluation on day 5 showed that qSOFA AUC had higher scores than the NEWS-2, APACHE II, and SOFA (0.979; 0.965; 0.939; 0.933) in predicting mortality, while SOFA and APACHE II AUC were higher in ICU admission than NEWS-2 and qSOFA (0.968; 0.964; 0.939; 0.934). According to the cutoff score, APACHE II on day 5 revealed the highest sensitivity and specificity in predicting the mortality (sensitivity 95.7%, specificity 86.7%). Conclusion: All scores signify good predictive values on COVID-19 patients mortality following the evaluation on the day 5. Nonetheless, APACHE-II appears to be the best at predicting mortality and ICU admission rate.