Machine learning models for predicting postoperative paraplegia in acute type A aortic dissection patients.

Background: Acute type A aortic dissection (ATAAD) is an extremely life-threatening cardiovascular emergency characterized by a critical clinical course and high mortality rate. Once the diagnosis is confirmed, emergency surgical intervention is required to save the patient's life. Postoperative paraplegia is a relatively rare but severe surgical complication, typically manifesting as transient or permanent loss of sensory and motor functions in both lower extremities after surgery. It severely impairs patients' survival and prognosis, imposing a heavy burden on both patients' families and the healthcare system. This study aimed to explore the risk factors for postoperative paraplegia in patients with ATAAD and to develop and validate a risk prediction model. Methods: A retrospective analysis was performed on the clinical data of ATAAD patients who were admitted to Beijing Anzhen Hospital and underwent surgical treatment between August 2018 and July 2022. Postoperative paraplegia was defined as the primary outcome endpoint. Patients were divided into a training set (70%) and a validation set (30%) using stratified sampling. Least absolute shrinkage and selection operator (Lasso) regression was applied to the training set to screen for key variables influencing the primary outcome. Seven machine learning algorithms were used to construct risk prediction models. The predictive performance of these models was validated using confusion matrix metrics, including the area under the receiver operating characteristic curve (AUC). The optimal prediction model was selected, and Shapley Additive exPlanations (SHAP) analysis was conducted to interpret the model. Results: Among the 572 patients included in this study, 22 (3.84%) developed paraplegia. Comprehensive evaluation of confusion matrix metrics showed that the Neural Networks model had the best AUC, lower Brier score, and higher F1 score and Kappa value. According to the SHAP analysis results, the risk factors most strongly associated with postoperative paraplegia were: pancreatic lipase level, left subclavian artery involvement, Sun's procedure, age, pancreatic amylase level, hemoglobin level, and secondary surgery. Conclusions: Among the seven machine learning models for predicting postoperative paraplegia in ATAAD patients, the Neural Networks model demonstrated the best predictive performance.