• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用商业可用软件建模预测经导管二尖瓣置换术后瓣周漏。

Predicting paravalvular leak after transcatheter mitral valve replacement using commercially available software modeling.

机构信息

Cardiovascular Institute, Banner University Medical Center, Phoenix, United States.

Cardiovascular Institute, Banner University Medical Center, Phoenix, United States.

出版信息

J Cardiovasc Comput Tomogr. 2020 Nov-Dec;14(6):495-499. doi: 10.1016/j.jcct.2020.04.007. Epub 2020 Apr 19.

DOI:10.1016/j.jcct.2020.04.007
PMID:32409265
Abstract

BACKGROUND

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.

METHODS

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).

RESULTS

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).

CONCLUSIONS

Software modeling using pre-procedural CTA is a straightforward method for predicting the risk of moderate and severe MR due to PVL after TMVR.

摘要

背景

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。

方法

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。

结果

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)。

结论

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。

相似文献

1
Predicting paravalvular leak after transcatheter mitral valve replacement using commercially available software modeling.使用商业可用软件建模预测经导管二尖瓣置换术后瓣周漏。
J Cardiovasc Comput Tomogr. 2020 Nov-Dec;14(6):495-499. doi: 10.1016/j.jcct.2020.04.007. Epub 2020 Apr 19.
2
Software Modeling to Predict Paravalvular Leak Following Transcatheter Mitral Valve Replacement.预测经导管二尖瓣置换术后瓣周漏的软件建模
JACC Cardiovasc Interv. 2018 Oct 22;11(20):e167-e169. doi: 10.1016/j.jcin.2018.07.054. Epub 2018 Sep 26.
3
Left Ventricular Remodeling After Transcatheter Mitral Valve Replacement With Tendyne: New Insights From Computed Tomography.经导管二尖瓣置换术后 Tendyne 对左心室重构的影响:来自计算机断层扫描的新见解。
JACC Cardiovasc Interv. 2020 Sep 14;13(17):2038-2048. doi: 10.1016/j.jcin.2020.06.009. Epub 2020 Aug 28.
4
Three-dimensional prototyping for procedural simulation of transcatheter mitral valve replacement in patients with mitral annular calcification.经皮二尖瓣置换术治疗二尖瓣环钙化患者的程序模拟的三维成型。
Catheter Cardiovasc Interv. 2018 Dec 1;92(7):E537-E549. doi: 10.1002/ccd.27488. Epub 2018 Jan 23.
5
Acute fulminant hemolysis after transcatheter mitral valve replacement for mitral annular calcification.二尖瓣环钙化经导管二尖瓣置换术后急性暴发性溶血
Catheter Cardiovasc Interv. 2020 Sep 1;96(3):706-711. doi: 10.1002/ccd.28944. Epub 2020 May 6.
6
Predictors of Left Ventricular Outflow Tract Obstruction After Transcatheter Mitral Valve Replacement.经导管二尖瓣置换术后左心室流出道梗阻的预测因素。
JACC Cardiovasc Interv. 2019 Jan 28;12(2):182-193. doi: 10.1016/j.jcin.2018.12.001.
7
Validating a prediction modeling tool for left ventricular outflow tract (LVOT) obstruction after transcatheter mitral valve replacement (TMVR).验证经导管二尖瓣置换术(TMVR)后左心室流出道(LVOT)梗阻的预测建模工具。
Catheter Cardiovasc Interv. 2018 Aug 1;92(2):379-387. doi: 10.1002/ccd.27447. Epub 2017 Dec 11.
8
Paravalvular leak repair after balloon-expandable transcatheter mitral valve implantation in mitral annular calcification: Early experience and lessons learned.经皮球囊扩张式经导管二尖瓣置换术后瓣周漏修复:早期经验与教训。
Catheter Cardiovasc Interv. 2019 Nov 1;94(5):764-772. doi: 10.1002/ccd.28131. Epub 2019 Feb 9.
9
Transcatheter Mitral Valve Replacement in Patients With Previous Aortic Valve Replacement.经导管二尖瓣置换术治疗既往主动脉瓣置换术后患者。
Circ Cardiovasc Interv. 2018 Oct;11(10):e006412. doi: 10.1161/CIRCINTERVENTIONS.118.006412.
10
Multimodality imaging guidance for percutaneous paravalvular leak closure: Insights from the multi-centre FFPP register.多模态影像引导经皮瓣周漏封堵术:多中心 FFPP 注册研究的见解。
Arch Cardiovasc Dis. 2018 Jun-Jul;111(6-7):421-431. doi: 10.1016/j.acvd.2018.05.001. Epub 2018 Jun 22.

引用本文的文献

1
[Role of computed tomography in transcatheter coronary and structural heart disease interventions].计算机断层扫描在经导管冠状动脉及结构性心脏病介入治疗中的作用
REC Interv Cardiol. 2024 May 9;6(3):201-212. doi: 10.24875/RECIC.M24000460. eCollection 2024 Jul-Sep.
2
Clinical Impact of Computational Heart Valve Models.计算心脏瓣膜模型的临床影响
Materials (Basel). 2022 May 5;15(9):3302. doi: 10.3390/ma15093302.
3
The Journal of Cardiovascular Computed Tomography: 2020 Year in review.心血管计算机断层摄影杂志:2020 年回顾。
J Cardiovasc Comput Tomogr. 2021 Mar-Apr;15(2):180-189. doi: 10.1016/j.jcct.2021.02.004. Epub 2021 Feb 22.
4
Highlights of the 15th annual scientific meeting of the Society of Cardiovascular Computed Tomography.第十五届心血管计算机断层成像学会年度科学会议要点。
J Cardiovasc Comput Tomogr. 2020 Nov-Dec;14(6):466-470. doi: 10.1016/j.jcct.2020.09.008. Epub 2020 Oct 1.