Cardiovascular Institute, Banner University Medical Center, Phoenix, United States.
Cardiovascular Institute, Banner University Medical Center, Phoenix, United States.
J Cardiovasc Comput Tomogr. 2020 Nov-Dec;14(6):495-499. doi: 10.1016/j.jcct.2020.04.007. Epub 2020 Apr 19.
There is limited data identifying patients at risk for significant mitral regurgitation (MR) after transcatheter mitral valve replacement (TMVR). We hypothesized that software modeling based on computed tomography angiography (CTA) can predict the risk of moderate or severe MR after TMVR.
58 consecutive patients underwent TMVR at two institutions, including 31 valve-in-valve, 16 valve-in-ring, and 11 valve-in-mitral annular calcification. 12 (20%) patients developed moderate or severe MR due to paravalvular leak (PVL).
The software model correctly predicted 8 (67%) patients with significant PVL, resulting in sensitivity of 67%, specificity 96%, positive predictive value 89%, and negative predictive value 86%. There was excellent agreement between CTA readers using software modeling to predict PVL (kappa 0.92; p < 0.01). On univariate analysis, CTA predictors of moderate or severe PVL included presence of a gap between the virtual valve and mitral annulus on the software model (OR 48; p < 0.01), mitral annular area (OR 1.02; p 0.01), and % valve oversizing (OR 0.9; p 0.01). On multivariate analysis, only presence of a gap on the software model remained significant (OR 36.8; p < 0.01).
Software modeling using pre-procedural CTA is a straightforward method for predicting the risk of moderate and severe MR due to PVL after TMVR.
经导管二尖瓣置换术(TMVR)后发生中重度二尖瓣反流(MR)的高危患者数据有限。我们假设基于计算机断层扫描血管造影(CTA)的软件建模可以预测 TMVR 后中重度 MR 的风险。
两家机构共 58 例连续患者接受 TMVR,其中包括 31 例瓣中瓣、16 例瓣环内和 11 例二尖瓣瓣环钙化。12 例(20%)患者因瓣周漏(PVL)发生中重度 MR。
软件模型正确预测了 8 例(67%)存在严重 PVL 的患者,其敏感性为 67%,特异性为 96%,阳性预测值为 89%,阴性预测值为 86%。使用软件模型预测 PVL 的 CTA 阅读者之间具有极好的一致性(kappa 0.92;p<0.01)。单因素分析中,中度或重度 PVL 的 CTA 预测因子包括软件模型上虚拟瓣环与二尖瓣环之间存在间隙(OR 48;p<0.01)、二尖瓣环面积(OR 1.02;p<0.01)和瓣膜过度扩张百分比(OR 0.9;p<0.01)。多因素分析中,仅软件模型上存在间隙具有统计学意义(OR 36.8;p<0.01)。
使用术前 CTA 的软件建模是一种预测 TMVR 后因 PVL 导致中重度 MR 的风险的简便方法。