Background

Quantifying right ventricular (RV) function is important to describe the pathophysiology of in pulmonary hypertension (PH). Current phenotyping strategies in PH rely on few invasive hemodynamic parameters to quantify RV dysfunction severity. The aim of this study was to identify novel RV phenotypes using unsupervised clustering methods on advanced hemodynamic features of RV function.

Methods

Participants were identified from the University of Arizona Pulmonary Hypertension Registry (n=190). RV-pulmonary artery coupling (Ees/Ea), RV systolic (Ees) and diastolic function (Eed) was quantified from stored RV pressure waveforms. Consensus clustering analysis with bootstrapping was used to identify the optimal clustering method. Pearson correlation analysis was used to reduce collinearity between variables. RV cluster subphenotypes were characterized using clinical data and compared to pulmonary vascular resistance (PVR) quintiles.

Results

Five distinct RV clusters (C1-C5) with distinct RV subphenotypes were identified using k-medoids with a Pearson distance matrix. Clusters 1 and 2 both have low diastolic stiffness (Eed) and afterload (Ea) but RV-PA coupling (Ees/Ea) is decreased in C2. Intermediate cluster (C3) has a similar Ees/Ea as C2 but with higher PA pressure and afterload. Clusters C4 and C5 have increased Eed and Ea but C5 has a significant decrease in Ees/Ea. Cardiac output was high in C3 distinct from the other clusters. In the PVR quintiles, contractility increased and stroke volume decreased as a function of increased afterload. World Symposium PH classifications were distributed across clusters and PVR quintiles.

Conclusions

RV-centric phenotyping offers an opportunity for a more precise-medicine based management approach.

中文翻译：

---

用于识别肺动脉高压患者新的 RV 功能亚表型的高级血流动力学和聚类分析

背景

量化右心室 (RV) 功能对于描述肺动脉高压 (PH) 的病理生理学非常重要。当前 PH 的表型分析策略依赖于少量侵入性血流动力学参数来量化 RV 功能障碍的严重程度。本研究的目的是利用无监督聚类方法对 RV 功能的高级血流动力学特征识别新的 RV 表型。

方法

参与者是从亚利桑那大学肺动脉高压登记处确定的（n=190）。RV-肺动脉耦合 (Ees/Ea)、RV 收缩功能 (Ees) 和舒张功能 (Eed) 通过存储的 RV 压力波形进行量化。使用引导的一致性聚类分析来确定最佳聚类方法。使用皮尔逊相关分析来减少变量之间的共线性。使用临床数据对 RV 簇亚表型进行表征，并与肺血管阻力 (PVR) 五分位数进行比较。

结果

使用 k-medoids 和 Pearson 距离矩阵识别出具有不同 RV 亚表型的五个不同 RV 簇 (C1-C5)。簇 1 和簇 2 均具有较低的舒张期刚度 (Eed) 和后负荷 (Ea)，但 C2 中的 RV-PA 耦合 (Ees/Ea) 降低。中间簇 (C3) 具有与 C2 类似的 Ees/Ea，但具有更高的 PA 压力和后负载。簇 C4 和 C5 增加了 Eed 和 Ea，但 C5 的 Ees/Ea 显着降低。与其他簇不同，C3 的心输出量较高。在 PVR 五分位数中，随着后负荷的增加，收缩力增加，每搏输出量减少。世界研讨会 PH 分类分布在集群和 PVR 五分位数中。

结论

以 RV 为中心的表型分析为更精确的基于药物的管理方法提供了机会。