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

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.