Application value of myocardial work technology by non-invasive echocardiography in evaluating left ventricular function in patients with chronic heart failure.

Abstract

Accurate evaluation of left ventricular (LV) systolic function is the premise for diagnosing and treating chronic heart failure. This study aimed to explore the incremental value of echocardiographic myocardial work in evaluating the LV systolic dysfunction in patients with chronic heart failure. A total of 206 participants were enrolled, including 155 patients with chronic heart failure and 51 healthy controls (HC). The chronic heart failure patients were divided into three groups according to LV ejection fraction (LVEF): Heart failure with preserved ejection fraction (HFpEF group, 54 cases, LVEF ≥50%), heart failure with mid-range ejection fraction (HFmrEF group, 50 cases, 40%≤ LVEF <50%), and heart failure with reduced ejection fraction (HFrEF group, 51 cases, LVEF <40%). Except for the conventional echocardiographic parameters, the left ventricular myocardial work parameters, including the global myocardial work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE), were calculated in the study participants. One-way analysis of variance test followed by Fisher's least significant difference (LSD) t-test were used to obtain parameters with significant differences, which were then fed into a machine learning model established for subsequent multi-classification of the four groups. The selected myocardial work parameters with high importance rankings resulting from the machine learning model were further compared with the traditional LVEF in the multi-classification of the four groups. All conventional echocardiographic parameters were significantly different between the HFmrEF and HFrEF groups, but only E/e', left atrium showed notable differences between the HFpEF and HC groups (P<0.05). All myocardial work parameters were markedly different between the four groups (P<0.05). LVEF and GWI were more important than the other parameters according to the multi-classification machine learning model. The multi-classification diagnostic performances of LVEF, GWI, and LVEF + GWI were 82%, 88%, and 98%, respectively, which confirmed that GWI + LVEF could complementarily improve the diagnosis accuracy in classifying the four groups, with a performance increase of approximately 10% than each individually. GWI can play a complementary role to LVEF in the early diagnosis of HFpEF patients from the HC group and improve the clinical evaluation accuracy in chronic heart failure patients. Echocardiographic myocardial work should be utilized along with conventional LVEF to evaluate the systolic function of chronic heart failure patients in clinical practice.