Scoring system for mortality prediction of in-hospital COVID-19 patients in resource-limited settings: a single center cohort study during Delta and Omicron waves

Abstract

  Objective: Coronavirus Disease 2019 (COVID-19)-related mortality includes several risk variables that are country-specific in nature. The development of a scoring system is necessary regarding the appearance of novel virus variants. The objective of this research is to develop a prognostic score for COVID-19 patients in resource-constrained settings.   Methods: This study used a retrospective and prospective cohort design to identify variables that influence COVID-19 patients' in-hospital mortality. The receiver operating characteristic (ROC) curve analysis was utilized to determine the laboratory variables cut-off. Cox regression analysis was undertaken to determine the exact variables influencing the survival of COVID-19 patients. A scoring system was created using the best model based on the Hosmer-Lemeshow test (calibration) and the area under the curve (AUC) (discrimination ability).   Results: Based on calibration and discrimination testing, model 2 (immune disorders, unconsciousness, cerebrovascular disease, onset, and oxygen saturation) was rated as the most advantageous model. Model 2 (without age adjustment) had a superior AUC than model 2A (with age). Cut-off was determined at 2, and calculated for onset ≥7 days (AUC=0.816, 95% CI: 0.742,0.890) and <7 days (AUC=0.850, 95% CI: 0.784,0.916). There was no difference in scoring system utilization for subjects recruited during Delta or Omicron waves (P=0.527).   Conclusion: The model (cut-off value ≥2) which incorporated age ≥65 years, immune disorders, decreased consciousness, increased respiratory rate, and oxygen saturation <95% is the best model in our study to predict COVID-19 patient mortality.