Fully automated left ventricular ejection fraction and global longitudinal strain predicts obstructive coronary artery disease in patients undergoing stress echocardiography: a multi-centre study. (14th October 2021) - O'Driscoll, J; Hawkes, W; Beqiri, A; Mumith, A; Parker, A; Upton, R; McCourt, A; Woodward, W; Dockerill, C; Heitner, S; Yadava, M; Kaul, S; et al.
Abstract: Background: Assessment of LVEF and myocardial deformation with GLS has shown promise in predicting CAD, which may add prognostic information for patients undergoing SE. However, selection bias precludes an accurate assessment of routine clinical SE workflow due to the exclusion of poor image quality and contrast enhanced studies. We hypothesise that an artificial intelligence (AI) pipeline capable of fully automated contouring of the left ventricle and GLS analysis of both non-contrast and contrast SE images is feasible and can predict CAD. Purpose: The aim of this study was to evaluate the prediction of obstructive coronary artery disease (CAD) from fully automated left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) measures in a large multicentre population of patients undergoing stress echocardiography (SE). Methods: 500 patients from five medical centres undergoing SE for the clinical evaluation of ischaemic heart disease were included in this study. LVEF and GLS was automatically calculated using AI in non-contrast and contrast images at rest and peak stress. The primary endpoint was CAD assessed using invasive coronary angiography. Results: Patients with significant CAD demonstrated significantly reduced LVEF and GLS at rest and peak stress (all p<0.001) compared to those without CAD. Of the 130 patients who exhibited myocardial ischaemia at peak stress, patients without significant CAD (37%) had significantly reduced LVEF and GLS when compared to those who did. Multivariate analysis demonstrated that a peak LVEF (0.93; 95% CI 0.9–0.96) and peak GLS (1.15; 95% CI 1.07–1.24) were significant independent predictors of CAD. The addition of automated LVEF and GLS to basic models significantly improved the C statistic from 0.78 to 0.83 and 0.85 (both p<0.001), respectively. Conclusions: Fully automated LVEF and GLS in non-contrast and contrast SE images is feasible and independently augment the prediction of obstructive CAD above and beyond traditional SE indexes. Funding Acknowledgement: Type of funding sources: None.