Suppr超能文献

心脏磁共振无创估测肺血管阻力。

Non-invasive estimation of pulmonary vascular resistance with cardiac magnetic resonance.

机构信息

The Zena and Michael A. Wiener Cardiovascular Institute and Marie-Josee and Henry R. Kravis Center for Cardiovascular Health, Mount Sinai School of Medicine, New York 10029, USA.

出版信息

Eur Heart J. 2011 Oct;32(19):2438-45. doi: 10.1093/eurheartj/ehr173. Epub 2011 May 30.

Abstract

AIM

To develop a cardiac magnetic resonance (CMR) method for non-invasive estimation of pulmonary vascular resistance (PVR).

METHODS AND RESULTS

The study comprised 100 consecutive patients with known or suspected pulmonary hypertension (PH; 53 ± 16 years, 73% women) who underwent same-day right heart catheterization (RHC) and CMR. Increased PVR was defined from RHC as >3 WU (n = 66, 66%). From CMR cine and phase-contrast images, right ventricular (RV) volumes and ejection fraction (RVEF), pulmonary artery (PA) flow velocities and areas, and cardiac output were quantified. The best statistical model to estimate PVR was obtained from a derivation cohort (n = 80) based on physiological plausibility and statistical criteria. Validity of the model was assessed in the remaining 20 patients (validation cohort). The CMR-derived model was: estimated PVR (in WU) = 19.38 - [4.62 × Ln PA average velocity (in cm/s)] - [0.08 × RVEF (in %)]. In the validation cohort, the correlation between invasively quantified and CMR-estimated PVR was 0.84 (P < 0.001). The mean bias between the RHC-derived and CMR-estimated PVR was -0.54 (agreement interval -6.02 to 4.94 WU). The CMR model correctly classified 18 (90%) of patients as having normal or increased PVR (area under the receiver operator characteristics curve 0.97; 95% confidence interval: 0.89-1.00). CONCLUSIONS Non-invasive estimation of PVR using CMR is feasible and may be valuable for PH diagnosis and/or follow-up.

摘要

目的

开发一种心脏磁共振(CMR)方法,用于无创估计肺血管阻力(PVR)。

方法和结果

本研究纳入了 100 例已知或疑似肺动脉高压(PH)的连续患者(53 ± 16 岁,73%为女性),这些患者在同一天接受了右心导管检查(RHC)和 CMR。从 RHC 中定义为>3 WU 的增加 PVR(n = 66,66%)。从 CMR 电影和相位对比图像中,定量了右心室(RV)容积和射血分数(RVEF)、肺动脉(PA)流速和面积以及心输出量。根据生理合理性和统计标准,从一个推导队列(n = 80)中获得了最佳的统计模型来估计 PVR。该模型的有效性在其余 20 名患者(验证队列)中进行了评估。CMR 衍生模型为:估计的 PVR(以 WU 计)= 19.38 - [4.62 × Ln PA 平均速度(以 cm/s 计)] - [0.08 × RVEF(以%计)]。在验证队列中,侵入性量化和 CMR 估计的 PVR 之间的相关性为 0.84(P < 0.001)。RHC 衍生和 CMR 估计的 PVR 之间的平均偏差为 -0.54(一致性区间为-6.02 至 4.94 WU)。CMR 模型正确分类了 18 名(90%)患者为正常或增加的 PVR(接收者操作特性曲线下面积为 0.97;95%置信区间:0.89-1.00)。

结论

使用 CMR 无创估计 PVR 是可行的,对于 PH 的诊断和/或随访可能具有价值。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验