A scoring model based on clinical factors to predict postoperative moderate to severe acute respiratory distress syndrome in Stanford type A aortic dissection

Background: Postoperative acute respiratory distress syndrome (ARDS) after type A aortic dissection is common and has high mortality. However, it is not clear which patients are at high risk of ARDS and an early prediction model is deficient.

Methods: From May 2015 to December 2017, 594 acute Stanford type A aortic dissection (ATAAD) patients who underwent aortic surgery in Anzhen Hospital were enrolled in our study. We compared the early survival of MS-ARDS within 24 h by Kaplan-Meier curves and log-rank tests. The data were divided into a training set and a test set at a ratio of 7:3. We established two prediction models and tested their efficiency.

Results: The oxygenation index decreased significantly immediately and 24 h after TAAD surgery. A total of 363 patients (61.1%) suffered from moderate and severe hypoxemia within 4 h, and 243 patients (40.9%) suffered from MS-ARDS within 24 h after surgery. Patients with MS-ARDS had higher 30-day mortality than others (log-rank test: p-value <0.001). There were 30 variables associated with MS-ARDS after surgery. The XGboost model consisted of 30 variables. The logistic regression model (LRM) consisted of 11 variables. The mean accuracy of the XGBoost model was 70.7%, and that of the LRM was 80.0%. The AUCs of XGBoost and LRM were 0.764 and 0.797, respectively.

Conclusion: Postoperative MS-ARDS significantly increased early mortality after TAAD surgery. The LRM model has higher accuracy, and the XGBoost model has higher specificity.