Background

Stress testing helps diagnose heart failure with preserved ejection fraction (HFpEF), but there are no established criteria for quantifying left ventricular (LV) functional reserve. The aim of this study was to investigate whether comprehensive analysis of the timing and amplitude of LV long-axis myocardial motion and deformation throughout the cardiac cycle during rest and stress can provide more informative criteria than standard measurements.

Methods

Velocity, strain, and strain rate traces were measured from all 18 LV segments by echocardiographic myocardial velocity imaging at rest and during semisupine bicycle exercise in 100 subjects aged 69 ± 7 years, including patients with HFpEF and healthy, hypertensive, and breathless control subjects. A machine-learning algorithm, composed of an unsupervised statistical method and a supervised classifier, was used to model spatiotemporal patterns of the traces and compare the predicted labels with the clinical diagnoses.

Results

The learned strain rate parameters gave the highest accuracy for allocating subjects into the four groups (overall, 57%; for patients with HFpEF, 81%), and into two classes (asymptomatic vs symptomatic; area under the curve, 0.89; accuracy, 85%; sensitivity, 86%; specificity, 82%). Machine learning of strain rate, compared with standard measurements, gave the greatest improvement in accuracy for the two-class task (+23%, P < .0001), compared with +11% (P < .0001) using velocity and +4% (P < .05) using strain. Strain rate was also best at predicting 6-min walk distance as an independent reference criterion.

Conclusions

Machine learning of spatiotemporal variations of LV strain rate during rest and exercise could be used to identify patients with HFpEF and to provide an objective basis for diagnostic classification.

中文翻译：

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保留射血分数对心力衰竭的诊断：左心室变形时空变化的机器学习

背景

压力测试可通过保留射血分数（HFpEF）来帮助诊断心力衰竭，但尚无用于量化左心室（LV）功能储备的既定标准。这项研究的目的是调查在休息和压力期间整个心动周期中LV长轴心肌运动和变形的时间和幅度的综合分析是否可以提供比标准测量更多的信息标准。

方法

通过超声心动图心肌速度成像在100位69±7岁的受试者中（包括HFpEF以及健康，高血压和呼吸困难的受试者）在静息和半仰卧自行车运动期间通过超声心动图心肌速度成像测量了所有18个LV段的速度，应变和应变率曲线。使用由无监督统计方法和有监督分类器组成的机器学习算法对痕迹的时空模式进行建模，并将预测的标记与临床诊断进行比较。

结果

所学习的应变率参数为将受试者分配到四组（总体为57％； HFpEF患者为81％）和两类（无症状与对症；曲线下面积为0.89；准确度为85）提供了最高的准确性。 ％；敏感性86％；特异性82％）。与标准测量值相比，机器学习的应变率使两类任务的准确度有了最大的提高（+ 23％，P  <.0001），而 使用速度和+4时则为+ 11％（P <.0001） ％（P  <.05）使用应变。应变率也最适合预测6分钟的步行距离，并将其作为独立的参考标准。

结论

休息和运动期间左心室应变率时空变化的机器学习可用于识别HFpEF患者，并为诊断分类提供客观依据。