Role of left ventricle deformation analysis in stress echocardiography for significant coronary artery disease detection: A diagnostic study meta-analysis

Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice

We used segmental strain analysis to evaluate whether intrinsic (diet-induced obesity [DIO]) and extrinsic (unpredictable chronic mild stress [UCMS]) stressors can alter deformational patterns of the left ventricle. Six-week-old male C57BL/6J mice were randomized into the lean or obese group (n = 24/group). Mice underwent 12 wk of DIO with a high-fat diet (HFD). At 18 wk, lean and obese mice were further randomized into UCMS and non-UCMS groups (UCMS, 7 h/d, 5 d/wk, for 8 wk). Echocardiography was performed at baseline (6 wk), post-HFD (18 wk) and post-UCMS (26 wk). Machine learning was applied to the DIO and UCMS groups. There was robust predictive accuracy (area under the receiver operating characteristic curve [AUC] = 0.921) when comparing obese with lean mice, with radial strain changes in the lateral (-64%, p ≤ 0.001) and anterior free (-53%, p < 0.001) walls being most informative. The ability to predict mice that underwent UCMS, irrespective of diet, was assessed (AUC = 0.886), revealing longitudinal strain rate of the anterior midwall and radial strain of the posterior septal wall as the top features. The wall segments indicate a predilection for changes in deformation patterns to the free wall (DIO) and septal wall (UCMS), indicating disease-specific alterations to the myocardium.

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

AIMS

To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection.

METHODS AND RESULTS

SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all P < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and -17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69-0.87) to 0.83 (0.74-0.91) or 0.84 (0.75-0.92), respectively.

CONCLUSION

AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.

Speckle Tracking Stress Echocardiography on Treadmill in Assessment of the Functional Significance of the Degree of Coronary Artery Disease

Aim        To determine diagnostic capabilities of left ventricular (LV) global longitudinal systolic strain (GLSS) in stress echocardiography (stress-EchoCG) with a treadmill test for diagnosing the functional significance of the degree of coronary stenosis.Material and methods        The study included 121 patients (73 men aged 68.3±7.7 years) with suspected or previously diagnosed ischemic heart disease (IHD). Speckle-tracking stress-EchCG (method of tracking speckles on two-dimensional gray-scale ultrasonic images) with a treadmill test and coronarography was performed for all patients. The patients were divided into 3 groups based on the severity of coronary artery (CA) stenosis according to the Gensini scale.Results   LV GLSS at rest did not significantly differ between the study groups. After the exercise, LV GLSS was significantly lower in patients with pronounced CA stenosis than in patients without or with moderate CA stenosis (15.9±4.6 % vs. 20.6±3.7 % (p&lt;0.001) and 19.6±3.0 % (p=0.003), respectively). Postexercise LV GLSS &lt;16.9% suggested a pronounced CA stenosis with a sensitivity of 80% and a specificity of 70% (area under the curve, AUC, 0.76±0.06 at 95 % confidence interval, CI, 0.63-0.89; р&lt;0.001). In the patient group without CA stenosis, LV GLSS showed a significant increase after completion of the exercise (from 19.1±3.1 to 20.6±3.7; p=0.04).Conclusion            Evaluation of LV GLSS and its dynamics in stress-EchoCG with a treadmill test may be promising in patients with IHD, since in most patients with pronounced CA stenosis, LV GLSS is reduced at baseline and further reduces in response to exercise. In patients without CA stenosis, LV GLSS increases after completing the exercise.

Sustainability and Versatility of the ABCDE Protocol for Stress Echocardiography

For the past 40 years, the methodology for stress echocardiography (SE) has remained basically unchanged. It is based on two-dimensional, black and white imaging, and is used to detect regional wall motion abnormalities (RWMA) in patients with known or suspected coronary artery disease (CAD). In the last five years much has changed and RWMA is not enough on its own to stratify patient risk and dictate therapy. Patients arriving at SE labs often have comorbidities and are undergoing full anti-ischemic therapy. The SE positivity rate based on RWMA fell from 70% in the eighties to 10% in the last decade. The understanding of CAD pathophysiology has shifted from a regional hydraulic disease to a systemic biologic disease. The conventional view of CAD encouraged the use of coronary anatomic imaging for diagnosis and the oculo-stenotic reflex for the deployment of therapy. This has led to a clinical oversimplification that ignores the lessons of pathophysiology and epidemiology, and in fact, CAD is not synonymous with ischemic heart disease. Patients with CAD may also have other vulnerabilities such as coronary plaque (step A of ABCDE-SE), alveolar-capillary membrane and pulmonary congestion (step B), preload and contractile reserve (step C), coronary microcirculation (step D) and cardiac autonomic balance (step E). The SE methodology based on two-dimensional echocardiography is now integrated with lung ultrasound (step B for B-lines), volumetric echocardiography (step C), color- and pulsed-wave Doppler (step D) and non-imaging electrocardiogram-based heart rate assessment (step E). In addition, qualitative assessment based on the naked eye has now become more quantitative, has been improved by contrast and based on cardiac strain and artificial intelligence. ABCDE-SE is now ready for large scale multicenter testing in the SE2030 study.