HeartSciences’ MyoVista® Technology Used to Develop AI-ECG Algorithm to Identify Patients at High-Risk of Major Adverse Cardiovascular Events

Rhea-AI Summary

Heart Test Laboratories, Inc., operating as HeartSciences (NASDAQ: HSCS; HSCSW), announced a study validating its MyoVista® technology in cardiac risk stratification. The independent research highlighted its AI-ECG algorithm's ability to predict major adverse cardiovascular events (MACE) effectively. The study involved 518 patients over 38 months and showed a significantly higher probability of identifying high-risk patients (21%) compared to low-risk (3%). Results align closely with traditional echo-based models, indicating strong potential for AI in clinical settings.

Positive

• AI-ECG model shows 21% identification of high-risk MACE patients vs 3% for low-risk (P<0.001).
• MyoVista® technology has comparable predictive utility to traditional echocardiography models (21% vs 5%; P<0.001).
• The study suggests cost-effective strategies for predicting cardiovascular risks and improving health outcomes.

Negative

• None.

Independent study demonstrates novel AI-ECG algorithm could provide a cost-effective strategy for improving cardiac risk stratification

Southlake, Texas, Nov. 10, 2022 (GLOBE NEWSWIRE) -- Heart Test Laboratories, Inc. d/b/a HeartSciences (NASDAQ: HSCS; HSCSW) (“HeartSciences” or the “Company”), a medical technology company focused on saving lives by making an ECG (also known as an EKG) a far more valuable screening tool through the use of AI, today announced that an independent study utilizing its MyoVista® proprietary technology was featured in Advocate Aurora Health’s Journal of Patient-Centered Research and Reviews, an open access, peer-reviewed medical journal devoted to advancing patient centered care practices, health outcomes, and patient experiences.

The publication concluded that the MyoVista® technology ECG-derived machine learning model “provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high-risk of major adverse cardiovascular events (MACE).” 

Key study highlights:

•  Patients (n=518) from West Virginia University served as the validation set and were followed up over a three-year period (38-months) for clinical outcomes including MACE rehospitalization and cardiac death. 
•  Results demonstrate the potential of AI-ECG algorithms for rapid decision-making in an office-based setting to evaluate and monitor the progress of the patient and justify appropriate downstream referral for additional tests like echocardiography or other interventions.
•  The Deep Neural Network (DNN) AI-ECG model demonstrated significantly increased probability for identifying patients at high-risk compared to low risk of MACE (21% vs 3%; P<0.001)
•  The AI-ECG model results were similar to an echo-based model (21% vs 5%; P<0.001), suggesting comparable utility.
•  Data used to develop the AI-ECG algorithm was collected from a multi-center trial, which included 727 patients from the Icahn School of Medicine at Mount Sinai, The University of California Los Angeles and Windsor Cardiac Center, Windsor Ontario.

“This independent study demonstrates the opportunity that AI-ECG algorithms could bring to improving health outcomes. I believe the solution to unnecessary cardiac deaths will come from low-cost, front-line screening using AI-ECGs. Imagine the day where you can go to your primary care physician and a simple 20-second ECG test shows not only whether you have early-stage heart disease, but also whether you are at high-risk of a major adverse cardiovascular event in the next three years,” stated Andrew Simpson, CEO of HeartSciences. “There are millions of ECGs conducted every week and HeartSciences is at the leading edge of commercialization in the field of adding new clinical indications to the ECG.”

The study trained the AI-ECG model against an echo-derived model for predicting MACE events combining multiple parameters for identifying patient phenogroups at risk for MACE. Nine echo parameters were included for determining patient risk: ejection fraction; left ventricular mass index; left atrial volume index; early diastolic transmitral flow velocity (E); late diastolic transmitral flow velocity (A); E/A ratio; early diastolic relaxation velocity (e’); E/e’ ratio; and tricuspid regurgitation peak velocity. 

The final AI-ECG model included 51 ECG features, of which the majority were MyoVista® CWT frequency features (n=26, 51%), followed by traditional ECG features derived from the MyoVista® (n=23, 45%) and clinical features were age and past medical history of coronary artery disease (n=2, 4%).

“The extracted frequency parameters provided via MyoVista®’s CWT technology represented the majority of the statistically significant data variables used to develop the algorithm using AI-based variable reduction techniques,” stated Mark Hilz, COO of HeartSciences. “We believe this clearly demonstrates the value of our signal processing technology and its ability to identify additional meaningful information from the electrical signal of the heart.” 

The deep neural network (DNN) model showed robust classification of patients with areas under the receiver operating characteristic curves (AUC) of 0.84 (95% CI: 0.80–0.87). The ECG-predicted model demonstrated an increased probability for MACE in high-risk compared to low-risk patients (21% vs 3%; P<0.001).  The results were similar to the echo-trained model (21% vs 5%; P<0.001), suggesting a comparable utility of the MyoVista® technology compared to echo to identify patients at risk of MACE events.

As reported in the publication, cardiovascular disease is the leading cause of morbidity and mortality globally, resulting in estimated health care costs of more than $200 billion in the United States annually. Effective, economical, and personalized prevention and risk-stratification strategies are imperative to mitigate this burden. 

To read the full publication, please visit: https://institutionalrepository.aah.org/cgi/viewcontent.cgi?article=1893&context=jpcrr

¹Source: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1

About HeartSciences
Heart Test Laboratories, Inc. d/b/a HeartSciences is a medical technology company focused on applying innovative AI-based technology to an ECG (also known as an EKG) to expand and improve an ECG’s clinical usefulness. Millions of ECGs are performed every week and the Company’s objective is to improve healthcare by making an ECG a far more valuable cardiac screening tool, particularly in frontline or point-of-care clinical settings. HeartSciences’ first product candidate for FDA clearance, the MyoVista® wavECG, or the MyoVista®, is a resting 12-lead ECG that is also designed to provide diagnostic information related to cardiac dysfunction which has traditionally only been available through the use of cardiac imaging. The MyoVista® also provides conventional ECG information in the same test. The business model, which involves the use of the MyoVista® device and consumables for each test, is expected to be “razor-razorblade” as the electrodes used with the MyoVista® are proprietary to HeartSciences, and new electrodes are required for every test performed.
For more information, please visit: https://www.heartsciences.com. Twitter: @HeartSciences
Safe Harbor Statement
This announcement contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. These forward-looking statements are made under the “safe harbor” provisions of the Private Securities Litigation Reform Act of 1995 and are relating to the Company's future financial and operating performance. All statements, other than statements of historical facts, included herein are "forward-looking statements" including, among other things, statements about HeartSciences’ beliefs and expectations. These statements are based on current expectations, assumptions and uncertainties involving judgments about, among other things, future economic, competitive and market conditions and future business decisions, all of which are difficult or impossible to predict accurately and many of which are beyond the Company's control. The expectations reflected in these forward-looking statements involve significant assumptions, risks and uncertainties, and these expectations may prove to be incorrect. Investors should not place undue reliance on these forward-looking statements, which speak only as of the date of this press release. Potential risks and uncertainties include, but are not limited to, risks discussed in HeartSciences’ filings with the U.S. Securities and Exchange Commission at www.sec.gov. Other than as required under the securities laws, the Company does not assume a duty to update these forward-looking statements.
For investor and media inquiries, please contact:
Investor Relations:
Crescendo Communications, LLC
Phone: (212) 671-1021
Email: HSCS@crescendo-ir.com

Company:
Gene Gephart
Phone: +1-737-414-9213 (US)
Email: investorrelations@heartsciences.com

FAQ

What are the key findings of the HeartSciences study on cardiac risk?

The study found that the MyoVista® AI-ECG algorithm can identify high-risk patients for major adverse cardiovascular events with a 21% probability compared to 3% for low-risk patients.

How does MyoVista® compare to traditional echocardiography?

MyoVista® technology demonstrated similar predictive capabilities as traditional echocardiography models in identifying high-risk patients for cardiovascular events.

What is the significance of this independent study for HeartSciences?

The study provides validation for HeartSciences' AI-ECG technology, potentially enhancing its commercial appeal by demonstrating effective cost-saving strategies in cardiac risk prediction.

How many patients were involved in the HeartSciences study?

The study involved 518 patients from West Virginia University, followed over a three-year period.

What type of model was compared to the MyoVista® AI-ECG in the study?

The study compared the AI-ECG model with an echo-derived model for predicting major adverse cardiovascular events.