HeartSciences Announces Publication of Clinical Study Results
in the Journal of the American College of Cardiology (JACC)
Study demonstrates promising new capabilities for MyoVista® ECG device in detecting Abnormal Cardiac Function
The article titled “Prediction of Abnormal Myocardial Relaxation from Signal Processed Surface ECG” presents the results from the investigator-initiated clinical study that focused on evaluating the feasibility of MyoVista® Wavelet ECG (wavECGTM) as a diagnostic tool for predicting myocardial relaxation abnormalities. Abnormal relaxation is an early feature of many types of heart disease and a key characteristic of left ventricular diastolic dysfunction (LVDD). It is typically detected using echocardiographic imaging. LVDD is a strong predictor of cardiovascular and all-cause mortality.1 Ischemia, hypertension, diabetes, valvular disease and reduced systolic function are all associated with LVDD.1, 2
Results from the feasibility study demonstrate MyoVista patented technology can detect myocardial relaxation abnormalities associated with LVDD. The study results demonstrated 80% sensitivity and 84% specificity with an area under the curve of 91% for the prediction of low (e’), an echocardiographic parameter widely used in determination of LVDD. Prediction of low (e’) also correctly identified 23 out of 28 study subjects (82%) with significant underlying coronary artery disease. Additionally, MyoVista wavECG prediction of relaxation abnormalities also allowed recognition of subjects with more advanced stages of DD and concurrent CAD with significantly more incremental value compared with clinical variables and conventional ECG information. The feasibility study was performed using machine learning analysis often described as artificial intelligence or AI. The study included a limited number of patients as well as other limitations and future studies were recommended to address these limitations.
Conclusions of the trial suggest a potential role for the MyoVista wavECG Device as a screening tool for patients at risk for LVDD that would benefit from echocardiographic evaluations.
Partho Sengupta, MD, Professor, Chief of Cardiology and Chair of Cardiac Innovation, WVU Heart & Vascular Institute and Principal Investigator in the study, commented “These data are extremely encouraging as they suggest a potential role of signal processed ECG in early cardiac disease detection. It is quite remarkable that MyoVista demonstrated a high diagnostic precision in detecting a state of cardiac muscle dysfunction only previously detectable using cardiac ultrasound techniques. This can eventually help in appropriate cardiac testing and reduce overall healthcare costs”
“These positive results demonstrate that MyoVista wavECG Technology, which includes our patented signal processing methods combined with artificial intelligence, can lead to enhanced capabilities and completely new uses for electrocardiography-based (ECG) devices.” stated Mark Hilz, President and CEO of HeartSciences.
Andrew Simpson, Chairman of HeartSciences, stated “This study of MyoVista wavelet ECG technology is a significant step towards enhancing the most commonly used low-cost front-line tool, the 12-lead resting ECG, with new capabilities that can provide more effective risk stratification related to the early detection of heart disease”.
A total of 188 subjects referred from outpatient clinics to the Icahn School of Medicine, Mount Sinai Hospital for coronary computed tomography (CT) angiography also undertook in the same visit, comprehensive two-dimensional echocardiography (including tissue Doppler) that included assessment of LVDD. Subjects with arrhythmias, unstable angina, previous cardiac surgery, a pacemaker, chest deformity, or an inability to express well-defined mitral annular velocities due to severe mitral annular calcifications were excluded. Additional analysis was conducted related to a comparison cohort that were evaluated at WVU, Heart and Vascular Institute which further validated MyoVista wavECG healthy patient characteristics as well as age and population distribution information.
1. LV Diastolic Dysfunction and Prognosis, Dalane W. Kitzman, et al., Circulation, 2012 February 14: 125(6): 743-745. doi:10.1161.CIRCULATIONAHA.111.086843.
2. Diastolic Dysfunction and Diastolic Heart Failure: Diagnostic, Prognostic and Therapeutic Aspects, Maurizio Galderisi, Cardiovascular Ultrasound, 2005, 3-9 doi:10.1186/1476-7120-3-9
Find the original article at http://www.onlinejacc.org/content/71/15/1650
PREDICTION OF ABNORMAL MYOCARDIAL RELAXATION FROM SIGNAL PROCESSED SURFACE ECG
Sengupta PP, Kularni H, Narula J
J Am Coll Cardiol. 2018;71(15)1650-1660
This study examined the benefits of using signal-processed surface electrocardiography (spECG) to diagnose abnormal cardiac muscle relaxation.
Materials and Methods
The authors used signal-processing techniques to magnify small changes on the surface ECG frequency spectrum related to the development of abnormal myocardial relaxation. The study used a 12-lead electrocardiogram on 188 patients who had been referred for coronary computed tomography. A method similar to Fourier analysis was used to deconstruct the ECG signals.
Some of the key points discussed included:
• Doppler imaging showed that 70% (133 of 188 subjects) had abnormal myocardial relaxation.
• Using a 12-lead spECG, an area under the curve of 91% was shown for predicting abnormal myocardial relaxation (80% sensitivity, 84% specificity).
• The prediction of low early diastolic relaxation velocity (e’) also allowed for the identification of significant underlying coronary artery disease in 82% of the cases. Integrated discrimination and net reclassification were also shown to be better for spECG compared to clinical features and conventional ECG.
• Among the findings, the authors noted that by adding the random forest classifier-based prediction of low e’ the prediction was improved as indicated by a 19% improvement in the area under the curve, an improvement of integrated discrimination index by 0.42 (p<0.001), and accurate reclassification of more than 80% of low e’ and normal subjects.
• There was also a 16% improvement of the area under the curve after adding Glasgow risk categorization, with integrated discrimination index by 0.40, and correct reclassification of more than 80%.
• Because the incidence of LVDD is high, as it can’t reliably be diagnosed by physical examination, and because of the limitations of a standard ECG, it is important that a physician carefully identify patients who should have an echocardiogram.