A TMAD-based rapid assessment model for left ventricular systolic function in patients with coronary artery disease: a multicenter prospective development and validation study.

Assessing left ventricular (LV) systolic function is critical for diagnosis, treatment, and prognosis. Current methods like LV ejection fraction (LVEF) and global longitudinal strain (GLS) are time-consuming and image-quality-dependent. This study aimed to evaluate the prognostic value of Tissue Motion Annular Displacement (TMAD) in patients with CAD and develop a stable, reproducible LVEF estimation model for rapid cardiac dysfunction screening. In this prospective, multicentre study we characterised LVEF and TMAD, calculated AUROCs to predict MACE and reduced systolic function, and performed Cox modelling for MACE risk. A rapid LVEF evaluation method was developed using the best-fit model and applied in CAD patients. TMAD, especially nTMADmid, showed significant correlations with GLS (r = 0.850, P < 0.001) and LVEF (r = 0.608, P < 0.001). Moreover, TMAD demonstrated high diagnostic efficacy for identifying MACE (AUC = 0.880, P<0.001) and reduced LV systolic function (AUC = 0.903, P = 0.008). The multivariate Cox proportional hazards model revealed that TMAD was the only independent risk factor for MACE. Power equations (highest F-value) were selected to build the model, showing minimal bias between observed and estimated LVEF across validation sets. Furthermore, TMAD exhibited high intra- and inter-observer reproducibility, was independent of view selection and image quality, and required significantly shorter time-consuming than both LVEF and GLS. TMAD is a valuable tool for assessing LV systolic function and providing incremental prognostic information. The TMAD-based predictive model enables rapid LVEF prediction and rapid detection of myocardial damage. Its simplicity and independence from image quality make it particularly useful in challenging imaging scenarios.