Diagnosis of Heart Failure With Preserved Ejection Fraction: Machine Learning of Spatiotemporal Variations in Left Ventricular Deformation.

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.