Noninvasive prediction of pulmonary artery pressure and vascular resistance by using cardiac magnetic resonance indices
Noninvasive prediction of pulmonary artery pressure and vascular resistance by using cardiac magnetic resonance indices
复制标题
利用心脏磁共振指数无创预测肺动脉压和血管阻力
DOI：
10.1016/j.ijcard.2016.10.068
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发表时间：
2017-01-15
影响因子：
3.5
通讯作者：
Zhang, Ningnannan
中科院分区：
文献类型：
--
作者：
Zhang, Zhang;Wang, Meng;Zhang, Ningnannan
背景：心脏磁共振（CMR）有望能够在初次右心导管插入术（RHC）诊断后对肺动脉高压（PAH）患者进行反复随访，以无创方式提供频繁的心脏形态和功能评估。本研究旨在利用无创CMR指数建立并验证平均肺动脉压（mPAP）和肺血管阻力（PVR）的预测模型。方法：PAH患者的推导队列（N=25）和验证队列（N=25）均在一周内接受CMR和RHC。使用快速电影和相衬序列来计算 CMR 指数，包括心室质量指数 (VMI)、室间隔曲率比 (CR) 和肺动脉正流量 (Q(P))。金标准 mPAP (mPAP(RHC)) 和 PVR (PVRRHC) 是根据 RHC 测量的。 mPAP 使用来自衍生队列的 CMR 指数 (mPAP(CMR)) 计算。采用多元线性回归进行分析。结果：预测mPAP的方程为mPAP(CMR)=28.837VMI -26.479CR - 0.201Q(P)+57.021。然后将该方程应用于验证队列以验证预测方程的准确性。 mPAPCMR 与 mPAP(RHC) 呈线性相关，即 mPAP(RHC) = 0.8055mPAP(CMR) + 7.9056 (r(2) = 0.6470，p < 0.001)。此外，从 CMR (PVRCMR) 计算的 PVR 也与推导队列 (r(2) = 0.4092，p < 0.001) 和验证队列 (r(2) = 0.3480，p < 0.001) 中的 PVRRHC 相关。结论：mPAP(CMR) 和 PVRCMR 技术的应用可能会提供一种无创方法来评估PAH 患者随访期间的血流动力学以及右心室功能评估。 (C) 2016 Elsevier Ireland Ltd. 保留所有权利。
Background: Cardiac magnetic resonance (CMR) has promise of being able to provide frequent cardiac morphology and function evaluations noninvasively for repeated follow-ups of pulmonary arterial hypertension (PAH) patients after the initial right heart catheterization (RHC) diagnosis. By using the noninvasive CMR indices, the present study aimed to formulate and validate a prediction model of mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance (PVR).Methods: Both Derivation Cohort (N=25) and Validation Cohort (N=25) of PAH patients underwent CMR and RHC within one week. Fast cine and phase-contrast sequences were used to calculate CMR indices, including ventricular mass index (VMI), interventricular septum curvature ratio (CR), and positive pulmonary arterial flow (Q(P)). The gold standard mPAP (mPAP(RHC)) and PVR (PVRRHC) were measured from RHC. mPAP was calculated using CMR indices (mPAP(CMR)) from the Derivation Cohort. Multiple linear regression was applied for analysis.Results: The equation predicting mPAP was mPAP(CMR)=28.837VMI -26.479CR - 0.201Q(P) + 57.021. The equation was then applied to the Validation Cohort to verify the accuracy of the prediction equation. mPAPCMR was correlated linearly with mPAP(RHC) as mPAP(RHC) = 0.8055mPAP(CMR) + 7.9056 (r(2) = 0.6470, p < 0.001). Moreover, PVR calculated from CMR (PVRCMR) was also correlated with the PVRRHC in both the Derivation Cohort (r(2) = 0.4092, p < 0.001) and the Validation Cohort (r(2) = 0.3480, p < 0.001).Conclusion: The application of the mPAP(CMR) and PVRCMR technique could potentially provide a noninvasive method to evaluate the hemodynamics for PAH patients during follow-ups as well as right ventricle function assessment. (C) 2016 Elsevier Ireland Ltd. All rights reserved.