Abnormal aortic flow profiles persist after aortic valve replacement in the majority of patients with aortic valve disease: how model-based personalized therapy planning could improve results. A pilot study approach
Abnormal aortic flow profiles persist after aortic valve replacement in the majority of patients with aortic valve disease: how model-based personalized therapy planning could improve results. A pilot study approach
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DOI：
10.1093/ejcts/ezz149
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发表时间：
2020-01-01
影响因子：
3.4
通讯作者：
Goubergrits, Leonid
中科院分区：
文献类型：
--
作者：
Nordmeyer, Sarah;Hellmeier, Florian;Goubergrits, Leonid
目的：已知主动脉中复杂的血流分布有助于血管扩张。我们研究了主动脉瓣疾病患者在手术主动脉瓣置换术（AVR）前后的主动脉血流特征。方法：34例主动脉瓣病变患者（生物瓣膜27例，机械瓣膜7例）在AVR前后行4维速度编码磁共振成像。7名健康志愿者作为对照。离心率（ES）和复杂血流评分（CFS）由主动脉血流剖面的螺旋度、涡度和离心率来确定。4例患者采用基于模型的治疗方案，通过个性化调整瓣膜的大小、旋转、成角以及主动脉直径来改善人工术后血流剖面。结果：主动脉瓣病变患者比对照组表现出更复杂的血流[中位ES 2.5（四分位间距（IQR） 2.3-2.7） vs 1.0 (IQR 1.0-1.0), P < 0.001，中位CFS 4.7 (IQR 4.3-4.8) vs 1.0 (IQR 1.0-2.0), P < 0.001]。手术后，整个患者队列的血流复杂性降低，但与对照组相比仍显着升高[中位ES 2.3 (IQR 1.9-2.3) vs 1.0 (IQR 1.0-1.0), P < 0.001，中位CFS 3.8 (IQR 3.0-4.3) vs 1.0 (IQR 1.0-2.0), P < 0.001]。机械AVR术后患者的血流复杂性大幅下降，与对照组无差异[中位ES 1.0 (IQR 1.0-2.3) vs中位CFS 1.0 (IQR 1.0-1.0), P = 0.46，中位CFS 1.0 (IQR 1.0-3.3) vs中位CFS 1.0 (IQR 1.0-2.0), P = 0.71]。在所有4例选定病例（生物学，n = 2；机械，n = 2）中，与现有的术后发现相比，基于模型的治疗计划降低了血流剖面的计算机复杂性[中位ES为1.7 (IQR 1.4-1.7) vs 2.3 (IQR 2.3-2.3)；CFS 1.7 (IQR 1.4-2.5) vs 3.8 (IQR 3.3-4.3)]。结论：主动脉异常血流谱在AVR术后更常持续存在。基于模型的治疗计划可能具有优化治疗以获得最佳个体结果的潜力。
OBJECTIVES: Complex blood flow profiles in the aorta are known to contribute to vessel dilatation. We studied flow profiles in the aorta in patients with aortic valve disease before and after surgical aortic valve replacement (AVR).METHODS: Thirty-four patients with aortic valve disease underwent 4-dimensional velocity-encoded magnetic resonance imaging before and after AVR (biological valve = 27, mechanical valve = 7). Seven healthy volunteers served as controls. Eccentricity (ES) and complex flow scores (CFS) were determined from the degree of helicity, vorticity and eccentricity of flow profiles in the aorta. Model-based therapy planning was used in 4 cases to improve in silico postoperative flow profiles by personalized adjustment of size, rotation and angulation of the valve as well as aorta diameter.RESULTS: Patients with aortic valve disease showed more complex flow than controls [median ES 2.5 (interquartile range (IQR) 2.3-2.7) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 4.7 (IQR 4.3-4.8) vs 1.0 (IQR 1.0-2.0), P < 0.001]. After surgery, flow complexity in the total patient cohort was reduced, but remained significantly higher compared to controls [median ES 2.3 (IQR 1.9-2.3) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 3.8 (IQR 3.0-4.3) vs 1.0 (IQR 1.0-2.0), P < 0.001]. In patients after mechanical AVR, flow complexity fell substantially and showed no difference from controls [median ES 1.0 (IQR 1.0-2.3) vs 1.0 (IQR 1.0-1.0), P = 0.46, median CFS 1.0 (IQR 1.0-3.3) vs 1.0 (IQR 1.0-2.0), P = 0.71]. In all 4 selected cases (biological, n = 2; mechanical, n = 2), model-based therapy planning reduced in silico complexity of flow profiles compared to the existing post-surgical findings [median ES 1.7 (IQR 1.4-1.7) vs 2.3 (IQR 2.3-2.3); CFS 1.7 (IQR 1.4-2.5) vs 3.8 (IQR 3.3-4.3)].CONCLUSIONS: Abnormal flow profiles in the aorta more frequently persist after surgical AVR. Model-based therapy planning might have the potential to optimize treatment for best possible individual outcome.