Construction of a prediction model for the risk of hospital⁃acquired infections after Stanford type A aortic dissection surgery

ObjectiveTo construct a Nomogram prediction model for the risk of postoperative hospital⁃acquired infection in patients with Stanford type A aortic dissection,so as to enable the early identification of high⁃risk individuals.MethodsClinical data were retrospectively collected from 412 patients with Stanford type A aortic dissection who underwent surgical treatment at Qilu Hospital of Shandong University from January 2019 to December 2023.LASSO regression and multivariate logistic regression analyses were employed to identify risk factors for postoperative hospital⁃acquired infection.A Nomogram prediction model for hospital⁃acquired infection risk was subsequently constructed.The model's performance was evaluated using the concordance index and bootstrap⁃corrected C⁃index,the area under the receiver operating characteristic curve(AUC),calibration curves,decision curve analysis, and clinical impact curve analysis.ResultsPostoperative hospital-acquired infections occurred in 61 of the 412 patients(14.81%).Logistic regression analysis revealed that pleural effusion,indwelling nasogastric tube,length of ICU stay,duration of mechanical ventilation,and hypoalbuminemia were significant risk factors for postoperative hospital⁃acquired infection.The Nomogram yielded an AUC of 0.837(95%CI 0.773⁃0.901),with a C⁃index of 0.837 and a bootstrap⁃corrected C⁃index of 0.829.The calibration curve demonstrated good agreement between predicted probabilities and actual observations. Decision curve analysis and clinical impact curve analysis indicated favorable clinical utility and net benefit.ConclusionsPleural effusion,indwelling nasogastric tube,length of ICU stay,duration of mechanical ventilation,and hypoalbuminemia are significant predictors of postoperative hospital⁃acquired infection in patients undergoing surgery for Stanford type A aortic dissection.The Nomogram prediction model based on these variables could help clinical workers identify high⁃risk patients early,and take timely control measures to reduce infection risk.