Lachmann Mark, Fortmeier Vera, Stolz Lukas, Tokodi Márton, Kovács Attila, Hesse Amelie, Leipert Antonia, Rippen Elena, Alvarez Covarrubias Héctor Alfonso, von Scheidt Moritz, Tervooren Jule, Roski Ferdinand, Fett Michelle, Gerçek Muhammed, Schuster Tibor, Harmsen Gerhard, Yuasa Shinsuke, Mayr N Patrick, Kastrati Adnan, Schunkert Heribert, Joner Michael, Xhepa Erion, Laugwitz Karl-Ludwig, Hausleiter Jörg, Rudolph Volker, Trenkwalder Teresa
First Department of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany (M.L., A.H., E.R., J.T., K.-L.L.).
DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance (M.L., A.H., E.R., M.v.S., A. Kastrati, H.S., M.J., E.X., K.-L.L., J.H., T.T.).
Circ Cardiovasc Imaging. 2025 Jan;18(1):e017005. doi: 10.1161/CIRCIMAGING.124.017005. Epub 2025 Jan 21.
Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricuspid annular plane systolic excursion. Recently, a deep learning model has been proposed that accurately predicts RV ejection fraction (RVEF) from 2-dimensional echocardiographic videos, with similar diagnostic accuracy as 3-dimensional imaging. This study aimed to evaluate the prognostic value of the deep learning-predicted RVEF values in patients with severe MR undergoing TEER.
This multicenter registry study analyzed the associations between the predicted RVEF values and 1-year mortality in patients with severe MR undergoing TEER. To predict RVEF, 2-dimensional apical 4-chamber view videos from preprocedural transthoracic echocardiographic studies were exported and processed by a rigorously validated deep learning model.
Good-quality 2-dimensional apical 4-chamber view videos could be retrieved for 1154 patients undergoing TEER between 2017 and 2023. Survival at 1 year after TEER was 84.7%. The predicted RVEF values ranged from 26.6% to 64.0% and correlated only modestly with tricuspid annular plane systolic excursion (Pearson =0.33; <0.001). Importantly, predicted RVEF was superior to tricuspid annular plane systolic excursion levels in predicting 1-year mortality after TEER (area under the curve, 0.687 versus 0.625; =0.029). Furthermore, Kaplan-Meier survival analysis revealed that patients with reduced RV function (n=723; defined as a predicted RVEF of <45%) had significantly worse 1-year survival rates than patients with preserved RV function (n=431; defined as a predicted RVEF of ≥45%; 80.3% [95% CI, 77.4%-83.3%] versus 92.1% [95% CI, 89.5%-94.7%]; hazard ratio for 1-year mortality, 2.67 [95% CI, 1.82-3.90]; <0.001).
Deep learning-enabled assessment of RV function using standard 2-dimensional echocardiographic videos can refine the prognostication of patients with severe MR undergoing TEER. Thus, it can be used to screen for patients with RV dysfunction who might benefit from intensified follow-up care.
右心室（RV）功能在接受经导管缘对缘修复（TEER）的严重二尖瓣反流（MR）患者中具有明确的预后作用，通常使用超声心动图测量的三尖瓣环平面收缩期位移进行评估。最近，有人提出了一种深度学习模型，该模型可以从二维超声心动图视频中准确预测右心室射血分数（RVEF），诊断准确性与三维成像相似。本研究旨在评估深度学习预测的RVEF值在接受TEER的严重MR患者中的预后价值。
这项多中心注册研究分析了接受TEER的严重MR患者中预测的RVEF值与1年死亡率之间的关联。为了预测RVEF，将术前经胸超声心动图研究中的二维心尖四腔视图视频导出，并由经过严格验证的深度学习模型进行处理。
在2017年至2023年期间，可为1154例接受TEER的患者检索到高质量的二维心尖四腔视图视频。TEER术后1年的生存率为84.7%。预测的RVEF值范围为26.6%至64.0%，与三尖瓣环平面收缩期位移的相关性仅为中等程度（Pearson相关系数=0.33；P<0.001）。重要的是，在预测TEER术后1年死亡率方面，预测的RVEF优于三尖瓣环平面收缩期位移水平（曲线下面积分别为0.687和0.625；P=0.029）。此外，Kaplan-Meier生存分析显示，右心室功能降低的患者（n=723；定义为预测的RVEF<45%）的1年生存率明显低于右心室功能保留的患者（n=431；定义为预测的RVEF≥45%；80.3%[95%CI，77.4%-83.3%]对92.1%[95%CI，89.5%-94.7%]；1年死亡率的风险比为2.67[95%CI，1.82-3.90]；P<0.001）。
使用标准二维超声心动图视频通过深度学习评估右心室功能可以优化接受TEER的严重MR患者的预后评估。因此，它可用于筛查可能受益于强化随访护理的右心室功能障碍患者。
