Automated non-invasive quantification of myocardial work using 3D echocardiography and validation with invasive catheter measurements

Abstract Background Myocardial work is an emerging technique for the assessment of left ventricular (LV) performance. Using blood pressure as an afterload determinate offers further insight into LV mechanics than can be detected with load-sensitive parameters of ejection fraction or global longitudinal strain (GLS). Observational studies have signalled benefit in identifying vulnerable ventricles in cardiomyopathy, valvular, and coronary disease. While the reference standard for calculating myocardial work is through catheterisation, this is impractical for routine assessment. Currently, a vendor specific non-invasive index of myocardial work is commercially available, but requires subjective manual analysis and identification of valvular events. With increasing availability of 3D echocardiography (3DE) and automated analysis techniques, there is potential to derive more reproducible estimates of myocardial work. Purpose We sought to test whether global myocardial work could be accurately, non-invasively, and automatically estimated using 3DE, in combination with standard non-invasive clinical measurements. Methods Patients undergoing left heart catheterisation as part of routine care were prospectively recruited, and 3DE was performed within one hour of invasive LV pressure measurement. A validated deep learning model was used to automatically analyse the 3DE image volumes to produce GLS curves, from which a smaller number of features (principal components) were extracted. The invasive pressure traces were separated into cycles, which were each paired with the strain trace that had the minimal difference in R-R interval (and excluded if the relative R-R interval difference exceeded 25%). For each patient, an invasively-derived estimate of myocardial work was calculated as the area enclosed by the median pressure-strain (PS) loop. A multivariate regression model was used to relate a set of routine clinical parameters to the invasively-derived myocardial work, and subsequently assessed using leave-one-out cross-validation. Results A total of 131 out of 153 patients were included for analysis (2 excluded due to R-R interval mismatch, 20 excluded due to poor image or pressure trace quality), with basic demographics presented in Table I. The final parameter set consisted of 6 variables: systolic and diastolic brachial cuff pressures, peak GLS, R-R interval, and two strain curve principal components. When fit into a leave-one-out model, this resulted in a Pearson r-value of 0.86, demonstrating a very high correlation between predicted and invasively-derived myocardial work estimates, with a bias of 0 mmHg% and 95% limits of agreement (LOA) of ± 423 mmHg% (Figure 1). Conclusion This study demonstrates that fully automated and reliable estimates of myocardial work can be derived using 3DE and deep learning combined with routine clinical parameters. Automated and non-invasive approaches may facilitate increased clinical uptake of myocardial work assessment.