Burton Timothy, Ramchandani Shyam, Bhavnani Sanjeev P, Khedraki Rola, Cohoon Travis J, Stuckey Thomas D, Steuter John A, Meine Frederick J, Bennett Brett A, Carroll William S, Lange Emmanuel, Fathieh Farhad, Khosousi Ali, Rabbat Mark, Sanders William E
CorVista Health (Analytics For Life Inc., d.b.a CorVista Health) Toronto, Toronto, ON, Canada.
Scripps Clinic Division of Cardiology, San Diego, CA, United States.
Front Cardiovasc Med. 2022 Sep 23;9:980625. doi: 10.3389/fcvm.2022.980625. eCollection 2022.
Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.
Patients presenting with cardiovascular symptoms ( = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.
The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.
This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.
左心室舒张末期压力（LVEDP）升高是左心室顺应性受损的结果，也是心肌功能障碍的一项重要指标。我们开发了一种算法，利用机电（EM）波形特征来预测LVEDP升高。我们研究了从这些EM波形中提取的选定特征的层次聚类，以识别重要的患者亚组并评估其可能的预后意义。
有心血管症状的患者（n = 396）接受了EM数据收集，并通过左心导管插入术直接测量LVEDP。LVEDP被分类为未升高（≤12 mmHg）或升高（≥25 mmHg）。从EM数据中提取对算法输出贡献最大的30个特征，并输入到无监督层次聚类算法中。将得到的树状图分为五个簇，并叠加患者元数据。
在聚类以及临床特征方面，LVEDP最高的簇（簇1）与LVEDP最低的簇（簇5）差异最大。与LVEDP升高患者比例最高的簇相比，最低的簇主要是LVEDP未升高、年龄较小、体重指数较低且患有严重冠状动脉疾病（CAD）比例较高的男性。LVEDP最高的簇（簇1）的下一个相邻簇（簇2）在所有簇中LVEDP第二低。簇2与簇1的主要区别在于从电数据中提取的特征，以及量化信号可预测性和变异性的特征。LVEDP最高的簇1中可预测性低且变异性高，而相邻的簇2则相反。
该分析基于波形特征识别出了LVEDP升高程度不同的患者亚组。一种对簇间变化和心肌功能障碍可能进展进行分层的方法可能包括区分簇的特征变化；具体而言，电信号可预测性降低和变异性增加。识别由LVEDP升高所证明的心肌功能障碍表型以及了解促进向左心室充盈压更高的簇转变的因素，可能有助于早期风险分层，并改善新型治疗干预措施的患者选择。
