Artificial intelligence calculated global longitudinal strain and left ventricular ejection fraction predicts cardiac events and all-cause mortality in patients with chest pain

Background: Assessment of left ventricular ejection fraction (LVEF) and myocardial deformation with global longitudinal strain (GLS) has shown promise in predicting adverse cardiovascular events. The aim of this study was to evaluate whether artificial intelligence (AI) calculated LVEF and GLS is associated with major adverse cardiac events (MACE) and all-cause mortality in patients presenting with chest pain.Methods: We studied 296 patients presenting with chest pain, who underwent transthoracic echocardiography (TTE). Clinical data, downstream clinical investigations and patient outcomes were collected. Resting TTE images underwent AI contouring for automated calculation of LVEF and GLS with Ultromics EchoGo Core 2.0. Regression analysis was performed to identify clinical and AI calculated parameters associated with MACE and all-cause mortality.Results: During a median follow-up period of 7.8 years (IQR 6.4, 8.8), MACE occurred in 34 (11.5%) patients and all-cause mortality in 60 (20%) patients. AI calculated LVEF (Odds Ratio [OR] .96; 95% CI .93-.99 and .96; 95% CI .93-.99) and GLS (1.11; 95% CI 1.01-1.21 and 1.08; 95% CI 1.00-1.16) were independently associated with MACE and all-cause mortality, respectively. According to Cox proportional hazards, a LVEF < 50% was associated with a 3.7 times MACE and 2.8 times all-cause mortality hazard rate compared to those with a LVEF >= 50%. Those with a GLS >= 15% was associated with a 2.5 times MACE and 2.3 times all-cause mortality hazard rate compared to those with a GLS <= 15.Conclusion: AI calculated resting LVEF and GLS is independently associated with MACE and all-cause mortality in high CVD risk patients. These results may have significant clinical implications through improved risk stratification of patients with chest pain, accelerated workflow of labour-intensive technical measures, and reduced healthcare costs.