Zsarnoczay Emese, Varga-Szemes Akos, Schoepf U Joseph, Rapaka Saikiran, Pinos Daniel, Aquino Gilberto J, Fink Nicola, Vecsey-Nagy Milan, Tremamunno Giuseppe, Kravchenko Dmitrij, Hagar Muhammad Taha, Amoroso Nicholas S, Steinberg Daniel H, Jacob Athira, O'Doherty Jim, Sharma Puneet, Maurovich-Horvat Pal, Emrich Tilman
Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary.
Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
J Cardiovasc Comput Tomogr. 2025 Mar-Apr;19(2):201-207. doi: 10.1016/j.jcct.2024.12.082. Epub 2025 Jan 9.
This study aimed to determine whether artificial intelligence (AI)-based automated assessment of left atrioventricular coupling index (LACI) can provide incremental value above other traditional risk factors for predicting mortality among patients with severe aortic stenosis (AS) undergoing coronary CT angiography (CCTA) before transcatheter aortic valve replacement (TAVR).
This retrospective study evaluated patients with severe AS who underwent CCTA examination before TAVR between September 2014 and December 2020. An AI-prototype software fully automatically calculated left atrial and left ventricular end-diastolic volumes and LACI was defined by the ratio between them. Uni- and multivariate Cox proportional hazard methods were used to identify the predictors of mortality in models adjusting for relevant significant parameters and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) score.
A total of 656 patients (77 years [IQR, 71-84 years]; 387 [59.0 %] male) were included in the final cohort. The all-cause mortality rate was 21.6 % over a median follow-up time of 24 (10-40) months. When adjusting for clinical confounders, LACI ≥43.7 % independently predicted mortality (adjusted HR, 1.52, [95 % CI: 1.03, 2.22]; p = 0.032). After adjusting for the STS-PROM score in a separate model, LACI ≥43.7 % remained an independent prognostic parameter (adjusted HR, 1.47, [95 % CI: 1.03-2.08]; p = 0.031). In a sub-analysis of patients with preserved left ventricular ejection fraction, LACI remained a significant predictor (adjusted HR, 1.72 [95 % CI: 1.02, 2.89]; p = 0.042).
AI-based fully automated assessment of LACI can be used independently to predict mortality in patients undergoing TAVR, including those with preserved LVEF.
本研究旨在确定基于人工智能（AI）的左房室耦合指数（LACI）自动评估能否在预测接受经导管主动脉瓣置换术（TAVR）前进行冠状动脉CT血管造影（CCTA）的严重主动脉瓣狭窄（AS）患者死亡率方面，提供超越其他传统危险因素的增量价值。
这项回顾性研究评估了2014年9月至2020年12月期间在TAVR前接受CCTA检查的严重AS患者。一个AI原型软件可完全自动计算左心房和左心室舒张末期容积，LACI由两者之比定义。采用单因素和多因素Cox比例风险方法，在调整相关显著参数和胸外科医师协会预测死亡率（STS-PROM）评分的模型中识别死亡率的预测因素。
最终队列共纳入656例患者（77岁[四分位间距，71 - 84岁]；387例[59.0%]为男性）。在中位随访时间24（10 - 40）个月内，全因死亡率为21.6%。在调整临床混杂因素后，LACI≥43.7%可独立预测死亡率（调整后HR，1.52，[95%CI：1.03，2.22]；p = 0.032）。在另一个单独模型中调整STS-PROM评分后，LACI≥43.7%仍然是一个独立的预后参数（调整后HR，1.47，[95%CI：1.03 - 2.08]；p = 0.031）。在左心室射血分数保留的患者亚组分析中，LACI仍然是一个显著的预测因素（调整后HR，1.72 [95%CI：1.02，2.89]；p = 0.042）。
基于AI的LACI全自动评估可独立用于预测接受TAVR的患者的死亡率，包括左心室射血分数保留的患者。
