Artificial Intelligence Improves Prediction of Major Adverse Cardiovascular Events in Patients Undergoing Transcatheter Aortic Valve Replacement Planning CT.

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-10-09 DOI:10.1016/j.acra.2024.09.046

Giuseppe Tremamunno, Milan Vecsey-Nagy, U Joseph Schoepf, Emese Zsarnoczay, Gilberto J Aquino, Dmitrij Kravchenko, Andrea Laghi, Athira Jacob, Puneet Sharma, Saikiran Rapaka, Jim O'Doherty, Pal Spruill Suranyi, Ismail Mikdat Kabakus, Nicholas S Amoroso, Daniel H Steinberg, Tilman Emrich, Akos Varga-Szemes

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Abstract

Rationale and objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusion: Automated AI-based comprehensive cardiac assessment enables pre-TAVR MACE prediction, with LV-LAS outperforming all other parameters.

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人工智能提高了经导管主动脉瓣置换术规划 CT 患者主要不良心血管事件的预测能力。

理由和目的：经导管主动脉瓣置换术（TAVR）前必须进行冠状动脉 CT 血管造影（CCTA）。我们的目的是评估人工智能（AI）驱动的软件自动分析术前 CCTA 心脏参数以预测 TAVR 患者主要不良心血管事件（MACE）的效果：回顾性纳入接受TAVR术前CCTA检查的患者。人工智能软件自动提取了心室、心房、心肌和心外膜脂肪组织的 34 个形态和容积心脏参数。机构数据库记录了临床信息和结果。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。使用哈雷尔 C 指数评估模型性能，使用似然比检验比较嵌套模型。对 170 名患者进行的人工分析评估了与自动测量的一致性：在 648 名入选患者（77 ± 9.3 岁，58.9% 为男性）中，116 人（17.9%）在中位随访 24 个月（四分位间范围 10-40 个月）内发生 MACE。调整临床参数后，只有左心室长轴缩短率（LV-LAS）是MACE的独立预测因素（危险比[HR]，1.05[95%置信区间，1.05-1.11]；P = 0.04），C指数显著改善（0.620 vs. 0.633；P 结论：基于人工智能的自动综合心脏评估能够预测TAVR前的MACE，其中LV-LAS优于所有其他参数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.