Heart failure classifications via non‐invasive pressure volume loops from echocardiography

Author:

Liu Xiaoli1ORCID,Chen Xu2,Xia Shaoyan3,Yang Feifei4ORCID,Zhu Haogang5,He Kunlun6

Affiliation:

1. The School of Biological Science and Medical Engineering Beihang University Beijing China

2. Department of Cardiology The Second Medical Center of Chinese PLA General Hospital Beijing China

3. The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

4. Department of Cardiology The Fourth Medical Center of Chinese PLA General Hospital Beijing China

5. The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Zhongguancun Laboratory Beijing China

6. Medical Big Data Research Center Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine Chinese PLA General Hospital Beijing China

Abstract

AbstractBackgroundLeft 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.MethodsFirstly, 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.ResultsBy 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).ConclusionsWe 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.

Funder

National Basic Research Program of China

National Natural Science Foundation of China

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging

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