{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T05:20:37Z","timestamp":1787721637560,"version":"build-2784847793"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To construct and validate a risk prediction model of in-hospital mortality using machine learning (ML) algorithm in a retrospective cohort of acute type A aortic dissection (ATAAD) patients undergoing surgical treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patients with ATAAD undergoing surgical treatment between January 2014 and December 2022 were enrolled to predict in-hospital mortality. To address class imbalance and overfitting, we developed a robust Random Forest (RF)-based classification framework using a nested stratified 5-fold cross-validation (NCV). This was a single-center, retrospective study with internal validation only; no external validation was performed. Performance was evaluated via ROC-AUC, Precision-Recall Area Under the Curve (PR-AUC), sensitivity, brier score and calibration metrics, with Shapley Additive exPlanations (SHAP) utilized for feature interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 639 ATAAD patients were included in the analytical cohort, with an in-hospital mortality rate of 5.6% (36\/639). The calibrated full RF model (50 preoperative clinical variables) achieved an ROC-AUC of 0.666, PR-AUC of 0.145, brier score of 0.051, and calibration slope of 0.836, with a sensitivity of 0.694 at an optimized threshold. A parsimonious 15-feature model maintained robust performance (ROC-AUC: 0.752, PR-AUC: 0.207, brier score: 0.050 and calibration slope: 0.934). SHAP analysis identified Creatine Kinase-MB, Myoglobin, and Fibrinogen Concentration as the top mortality predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed and internally validated an explainable RF model to predict in-hospital mortality after ATAAD surgery. Given the low positive predictive value and high negative predictive value, the model is best regarded as a promising preliminary rule-out\/triage tool that requires multicenter external validation before clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1872110","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T04:45:23Z","timestamp":1787719523000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and validation of explainable machine learning models to predict in-hospital mortality for patients with acute type A aortic dissection surgery"],"prefix":"10.3389","volume":"13","author":[{"given":"Keyan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Surgery, The Second Hospital of Jilin University, Changchun","place":["Jilin, 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