Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

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.

The Role of Non-Invasive Multimodality Imaging in Chronic Coronary Syndrome: Anatomical and Functional Pathways

Coronary artery disease (CAD) is one of the major causes of mortality and morbidity worldwide, with a high socioeconomic impact. Currently, various guidelines and recommendations have been published about chronic coronary syndromes (CCS). According to the recent European Society of Cardiology guidelines on chronic coronary syndrome, a multimodal imaging approach is strongly recommended in the evaluation of patients with suspected CAD. Today, in the current practice, non-invasive imaging methods can assess coronary anatomy through coronary computed tomography angiography (CCTA) and/or inducible myocardial ischemia through functional stress testing (stress echocardiography, cardiac magnetic resonance imaging, single photon emission computed tomography-SPECT, or positron emission tomography-PET). However, recent trials (ISCHEMIA and REVIVED) have cast doubt on the previous conception of the management of patients with CCS, and nowadays it is essential to understand the limitations and strengths of each imaging method and, specifically, when to choose a functional approach focused on the ischemia versus a coronary anatomy-based one. Finally, the concept of a pathophysiology-driven treatment of these patients emerged as an important goal of multimodal imaging, integrating 'anatomical' and 'functional' information. The present review aims to provide an overview of non-invasive imaging modalities for the comprehensive management of CCS patients.