A novel nomogram model to predict in-hospital mortality in patients with acute type A aortic dissection after surgery

Acute type A aortic dissection is a dangerous disease that threatens public health. In recent years, with the progress of medical technology, the mortality rate of patients after surgery has been gradually reduced, leading that previous prediction models may not be suitable for nowadays. Therefore, the present study aims to find new independent risk factors for predicting in-hospital mortality and construct a nomogram prediction model. Methods: The clinical data of 341 consecutive patients in our center from 2019 to 2023 were collected, and they were divided into two groups according to the death during hospitalization. The independent risk factors were analyzed by univariate and multivariate logistic regression, and the nomogram was constructed and verified based on these factors. Results: age, preoperative lower limb ischemia, preoperative activated partial thromboplastin time (APTT), preoperative platelet count, Cardiopulmonary bypass (CPB) time and postoperative acute kidney injury (AKI) independently predicted in-hospital mortality of patients with acute type A aortic dissection after surgery. The area under the receiver operating characteristic curve (AUC) for the nomogram was 0.844. The calibration curve and decision curve analysis verified that the model had good quality. Conclusion: The new nomogram model has a good ability to predict the in-hospital mortality of patients with acute type A aortic dissection after surgery.