Frontiers in medicine · 2026
Research on a machine learning-based predictive model for postoperative neurological dysfunction in acute Stanford type A aortic dissection.
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Li Lun, Wang Ruiyi, Qin Lei, Jing Xiaoyong, Zhu Junming
Аннотация
INTRODUCTION: This study aimed to construct and validate a machine learning (ML) model integrating preoperative, intraoperative, and postoperative multimodal clinical data for individualized prediction of postoperative neurological dysfunction (ND) in patients with acute Stanford type A aortic dissection (ATAAD).
METHODS: A retrospective analysis was conducted on 1,228 ATAAD patients (Aortic Disease Center of Beijing Anzhen Hospital, January 2020-December 2023): 853 patients (January 2020-December 2022) for model training/internal validation (via 10-fold cross-validation) and 375 patients (January-December 2023) for external validation. The 853 patients were grouped into control (n = 616) and ND (n = 237), including 203 transient ND (TND) and 34 permanent ND (PND) groups. Data were analyzed using Mann-Whitney U, chi-square (χ 2), and Fisher's exact tests (p < 0.05). Four ML models (SVC-LK, Nu-SVC, AdaBoost, XGBoost) were built with perioperative data; SHapley Additive exPlanations (SHAP) selected 15 robust features from 49 initial ones. Model performance was assessed via ROC-AUC (10-fold cross-validation for training/internal validation, external validation for effectiveness), and the optimal model was identified using DeLong test (two-tailed p-values). A multidimensional analysis compared the optimal model with traditional logistic regression (LR).
RESULTS: The XGBoost model exhibited the best performance: AUC = 0.966 (internal validation) and AUC = 0.951 (external validation), outperforming LR and the other three ML models.
CONCLUSION: The XGBoost algorithm demonstrates superior efficacy in predicting postoperative ND in acute ATAAD patients, providing postoperative early warning, identifying high-risk patients, offering clinical guidance, and enabling timely intervention.
METHODS: A retrospective analysis was conducted on 1,228 ATAAD patients (Aortic Disease Center of Beijing Anzhen Hospital, January 2020-December 2023): 853 patients (January 2020-December 2022) for model training/internal validation (via 10-fold cross-validation) and 375 patients (January-December 2023) for external validation. The 853 patients were grouped into control (n = 616) and ND (n = 237), including 203 transient ND (TND) and 34 permanent ND (PND) groups. Data were analyzed using Mann-Whitney U, chi-square (χ 2), and Fisher's exact tests (p < 0.05). Four ML models (SVC-LK, Nu-SVC, AdaBoost, XGBoost) were built with perioperative data; SHapley Additive exPlanations (SHAP) selected 15 robust features from 49 initial ones. Model performance was assessed via ROC-AUC (10-fold cross-validation for training/internal validation, external validation for effectiveness), and the optimal model was identified using DeLong test (two-tailed p-values). A multidimensional analysis compared the optimal model with traditional logistic regression (LR).
RESULTS: The XGBoost model exhibited the best performance: AUC = 0.966 (internal validation) and AUC = 0.951 (external validation), outperforming LR and the other three ML models.
CONCLUSION: The XGBoost algorithm demonstrates superior efficacy in predicting postoperative ND in acute ATAAD patients, providing postoperative early warning, identifying high-risk patients, offering clinical guidance, and enabling timely intervention.