Tremamunno Giuseppe, Vecsey-Nagy Milan, Schoepf U Joseph, Zsarnoczay Emese, Aquino Gilberto J, Kravchenko Dmitrij, Laghi Andrea, Jacob Athira, Sharma Puneet, Rapaka Saikiran, O'Doherty Jim, Suranyi Pal Spruill, Kabakus Ismail Mikdat, Amoroso Nicholas S, Steinberg Daniel H, Emrich Tilman, Varga-Szemes Akos
Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Sant'Andrea University, Hospital Via di Grottarossa, 1035-1039 00189 Rome, Italy (G.T., A.L.).
Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Dr, Charleston, South Carolina 29425, USA (G.T., M.V.N., U.J.S., E.Z., G.J.A., D.K., J.O., P.S.S., I.M.K., T.E., A.V.S.); Heart and Vascular Center, Semmelweis University, Varosmajor utca 68, Budapest 1122, Hungary (M.V.N.).
Acad Radiol. 2025 Feb;32(2):702-711. doi: 10.1016/j.acra.2024.09.046. Epub 2024 Oct 10.
Coronary CT angiography (CCTA) is mandatory before transcatheter aortic valve replacement (TAVR). Our objective was to evaluate the efficacy of artificial intelligence (AI)-powered software in automatically analyzing cardiac parameters from pre-procedural CCTA to predict major adverse cardiovascular events (MACE) in TAVR patients.
Patients undergoing pre-TAVR CCTA were retrospectively included. AI software automatically extracted 34 morphologic and volumetric cardiac parameters characterizing the ventricles, atria, myocardium, and epicardial adipose tissue. Clinical information and outcomes were recorded from institutional database. Cox regression analysis identified predictors of MACE, including non-fatal myocardial infarction, heart failure hospitalization, unstable angina, and cardiac death. Model performance was evaluated with Harrell's C-index, and nested models were compared using the likelihood ratio test. Manual analysis of 170 patients assessed agreement with automated measurements.
Among the 648 enrolled patients (77 ± 9.3 years, 58.9% men), 116 (17.9%) experienced MACE within a median follow-up of 24 months (interquartile range 10-40). After adjusting for clinical parameters, only left ventricle long axis shortening (LV-LAS) was an independent predictor of MACE (hazard ratio [HR], 1.05 [95% confidence interval, 1.05-1.11]; p = 0.04), with significantly improved C-index (0.620 vs. 0.633; p < 0.001). When adjusted for the Society of Thoracic Surgeons Predicted Risk of Mortality score, LV-LAS was also predictive of MACE (HR, 1.08 [95%CI, 1.03-1.13]; p = 0.002), while improving model performance (C-index: 0.557 vs. 0.598; p < 0.001). All parameters showed good or excellent agreement with manual measurements.
Automated AI-based comprehensive cardiac assessment enables pre-TAVR MACE prediction, with LV-LAS outperforming all other parameters.
在经导管主动脉瓣置换术（TAVR）之前，冠状动脉CT血管造影（CCTA）是必不可少的。我们的目的是评估人工智能（AI）驱动的软件在自动分析术前CCTA的心脏参数以预测TAVR患者主要不良心血管事件（MACE）方面的疗效。
回顾性纳入接受TAVR术前CCTA的患者。AI软件自动提取了34个形态学和容积性心脏参数，这些参数表征了心室、心房、心肌和心外膜脂肪组织。从机构数据库中记录临床信息和结果。Cox回归分析确定了MACE的预测因素，包括非致命性心肌梗死、心力衰竭住院、不稳定型心绞痛和心源性死亡。使用Harrell氏C指数评估模型性能，并使用似然比检验比较嵌套模型。对170例患者进行手动分析，评估与自动测量结果的一致性。
在648例入选患者（77±9.3岁，58.9%为男性）中，116例（17.9%）在中位随访24个月（四分位间距10 - 4）期间发生了MACE。在调整临床参数后，只有左心室长轴缩短（LV-LAS）是MACE的独立预测因素（风险比[HR]，1.05[95%置信区间，1.05 - 1.11]；p = 0.04），C指数显著提高（0.620对0.633；p < 0.001）。在调整胸外科医师协会预测死亡风险评分后，LV-LAS也可预测MACE（HR，1.08[95%CI，1.03 - 1.13]；p = 0.002），同时改善了模型性能（C指数：0.557对0.598；p < 0.001）。所有参数与手动测量结果均显示出良好或极好的一致性。
基于AI的自动综合心脏评估能够预测TAVR术前的MACE，其中LV-LAS的表现优于所有其他参数。
