Heart failure classifications via non-invasive pressure volume loops from echocardiography.

Background: Left ventricular pressure-volume (LV-PV) loops provide comprehensive characterization of cardiovascular system in both health and disease, which are the essential element of the hemodynamic evaluation of heart failure (HF). This study attempts to achieve more detailed HF classifications by non-invasive LV-PV loops from echocardiography and analyzes contribution of parameters to HF classifications.

Methods: Firstly, non-invasive PV loops are established by time-varying elastance model where LV volume curves were extracted from apical-four-chambers view of echocardiographic videos. Then, 16 parameters related to cardiac structure and functions are automatically acquired from PV loops. Next, we applied six machine learning (ML) methods to divide four categories. On this premise, we choose the best performing classifier among machine learning approaches for feature ranking. Finally, we compare the contributions of different parameters to HF classifications.

Results: By the experimental, the PV loops were successfully acquired in 1076 cases. When single left ventricular ejection fraction (LVEF) is used for HF classifications, the accuracy of the model is 91.67%. When added parameters extracted from ML-derived LV-PV loops, the classification accuracy is 96.57%, which improved by 5.1%. Especially, our parameters have a great improvement in the classification of non-HF controls and heart failure with preserved ejection fraction (HFpEF).

Conclusions: We successfully presented the classification of HF by machine derived non-invasive LV-PV loops, which has the potential to improve the diagnosis and management of heart failure in clinic. Moreover, ventriculo-arterial (VA) coupling and ventricular efficiency were demonstrated important factors for ML-based HF classification model besides LVEF.