ABSTRACT
Introduction: The novel coronavirus disease 2019 is a major health concern worldwide. The objective was to develop a Bayesian model to predict critical outcomes in patients with COVID-19. Methods: Sensitivity and specificity were obtained from previous meta-analysis. Using the IVC-COV2 index as pretest probability, likelihood ratios were integrated in a Fagan nomogram for posttest probabilities, generating IVC-COV2 + NEWS and CURB-65 scores values. Absolute and Relative Diagnostic Gains (ADG, RDG) were calculated. Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and based on mortality divided into high (71.97%) Intermediate (26.11%) and low (1.91%) risk groups. Integrating the IVC-COV2 intermediate + NEWS≥5 and CURB-65 >2 score models found that the Number Needed to Diagnose demonstrated a slight improvement for the CURB-65 model [2.00 (2) vs 2.71(3)]. When comparing diagnostic gains, no statistical differences were found on the IVC-CoV2 NEWS model compared to the CURB-65 model in both LR+ (P=0.62) and LR- (P=0.95). Conclusion: This mathematical model proposed that the combination of a IVC-COV2 Intermediate score plus NEWS or CURB-65 scores yields superior results and a greater predictive value for severity of illness. To our knowledge this is the first population-based/mathematical model for COVID-19 Critical Care decision making.