Development of a predictive nomogram for postoperative acute respiratory distress syndrome in Stanford type A aortic dissection patients: A retrospective study.

Abstract

Introduction and objectives: This retrospective study aimed to develop a nomogram to predict the risk of postoperative acute respiratory distress syndrome (ARDS) in patients with Stanford type A acute aortic dissection.

Methods: The study included patients who underwent surgical repair for Stanford type A acute aortic dissection between January 2020 and December 2023. Demographic data, surgical details, intraoperative information, and postoperative outcomes were collected. Univariate logistic regression was used for preliminary predictor screening, and a multivariate logistic regression model was constructed and presented as a nomogram. The nomogram's performance was evaluated using the area under the receiver operating characteristics (ROC) curve calibration plots, and decision curve analysis (DCA). Internal validation was performed using bootstrap resampling.

Results: The study included 142 patients, 41 (28.873%) of whom developed ARDS postoperatively. Multivariate logistic regression identified body mass index (BMI), postoperative procalcitonin (PCT), cardiopulmonary bypass (CPB) time, and low albumin as independent risk factors for postoperative ARDS in type A acute aortic dissection patients. These factors were used to develop the nomogram, which demonstrated good predictive performance with an area under the ROC curve of 0.809 (95% confidence interval: 0.721-0.881). The nomogram was successfully validated by calibration plots and DCA.

Conclusions: BMI, PCT, CPB time, and low albumin are independent risk factors for postoperative ARDS in type A acute aortic dissection patients. The constructed nomogram provides an effective tool for predicting the risk of ARDS, aiding in the prevention and management of this complication in patients undergoing aortic surgery.