{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T06:13:59Z","timestamp":1770963239549,"version":"3.50.1"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Anzhen Hospital High Level Research Funding","award":["2024AZC1001"],"award-info":[{"award-number":["2024AZC1001"]}]},{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2023ZD0504400"],"award-info":[{"award-number":["2023ZD0504400"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective analysis was conducted on 1,228 ATAAD patients (Aortic Disease Center of Beijing Anzhen Hospital, January 2020\u2013December 2023): 853 patients (January 2020\u2013December 2022) for model training\/internal validation (via 10-fold cross-validation) and 375 patients (January\u2013December 2023) for external validation. The 853 patients were grouped into control (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f616) and ND (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f237), including 203 transient ND (TND) and 34 permanent ND (PND) groups. Data were analyzed using Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      , chi-square (\n                      <jats:italic>\u03c7<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ), and Fisher\u2019s exact tests (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). 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\n                      <jats:italic>p<\/jats:italic>\n                      -values). A multidimensional analysis compared the optimal model with traditional logistic regression (LR).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The XGBoost model exhibited the best performance: AUC\u202f=\u202f0.966 (internal validation) and AUC\u202f=\u202f0.951 (external validation), outperforming LR and the other three ML models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1716649","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:22:45Z","timestamp":1770960165000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on a machine learning-based predictive model for postoperative neurological dysfunction in acute Stanford type A aortic dissection"],"prefix":"10.3389","volume":"13","author":[{"given":"Lun","family":"Li","sequence":"first","affiliation":[{"name":"Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Beijing Lab for Cardiovascular Precision Medicine","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiyi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Statistics, University of International Business and Economics","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Statistics, University of International Business and Economics","place":["Beijing, China"]},{"name":"Dong Fureng Institute of Economic and Social Development, Dong Fureng Institute of Economic and Social Development, Wuhan University","place":["Wuhan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyong","family":"Jing","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Beijing Lab for Cardiovascular Precision Medicine","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University","place":["Beijing, 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