A prediction model for after postoperative outcome in SARS-CoV-2 patients: a retrospective observation study

Introduction. Coronavirus infection can complicate the perioperative course of any surgical intervention, posing an acute problem in surgical patients with COVID-19. At the same time, the risk factors and their contribution to the adverse outcome remain obscure.Objectives. This study aims to identify risk factors for postoperative death in patients diagnosed with SARS-CoV-2.Materials and methods. The study offers a retrospective analysis of data from 1029 patients at the Krasnodar Regional Clinical Hospital № 2, which had been converted into a COVID-19 treatment facility.Results. A total of 421 (41 %) patients underwent high-risk surgery. Mortality in the study cohort reached 21.2 %. Factors such as the ASA baseline physical status, age, surgery duration and the degree of lung damage seen on CT scans (CT-3 and CT-4) serve as independent predictors of death. Using these parameters makes it possible to predict perioperative mortality with high accuracy (AUROC = 0.814).Conclusion. The study examined risk factors for poor outcomes in surgery patients with COVID-19 and developed a model to predict death in this group of patients. The frequency of adverse outcomes after surgical treatment of patients with SARS-CоV-2 was relatively high, the predictors of death being advanced age, baseline physical status, surgery severity and duration, as well as the volume of lung damage seen on CT scans. The developed model allows accurate prediction of an unfavourable outcome.