Publication in JACC: Advances demonstrates that an algorithm combining the HeartBeam ECG device with patient risk factors and symptoms can accurately identify heart attack riskStudy advances the scientific foundation
  • Publication in JACC: Advances demonstrates that an algorithm combining the HeartBeam ECG device with patient risk factors and symptoms can accurately identify heart attack risk
  • Study advances the scientific foundation supporting HeartBeam's planned indication expansion into heart attack detection
  • Large market potential with over 20 million patients in the U.S. at risk of a heart attack

HeartBeam, Inc. (NASDAQ:BEAT), a medical technology company focused on transforming cardiac care by providing powerful cardiac insights, today announced a peer-reviewed article in JACC: Advances, a journal of the American College of Cardiology, demonstrating that a risk prediction algorithm incorporating the credit card-sized HeartBeam ECG device can accurately identify heart attack risk in patients presenting with chest pain.

More Details on Study Results

The proof-of-concept study evaluated whether a risk prediction algorithm could close that gap by combining three independent inputs into a single risk score: an ECG from the HeartBeam device, a patient's pre-existing cardiovascular risk factors, and a structured symptom assessment. Key findings include:

  • Prospective study enrolled 212 patients presenting to the emergency department with chest pain with 184 patients included in the final analysis.
  • When the algorithm used a single ECG reading from the HeartBeam device combined with the patient's risk factors and symptoms, it achieved an AUC of 86.5%. AUC or area under the curve is a key measure of performance.
  • When a personal, symptom-free baseline ECG previously recorded on the same HeartBeam device was available for comparison, AUC increased to 92.9%. This is an important finding, as physicians evaluating an ECG from the HeartBeam System will always have the patient's baseline ECG for comparison, potentially increasing the ability to detect a heart attack.
  • The algorithm's false-positive rate was significantly lower than the physician panel (19.8% for algorithm vs. 55.6% for physician panel, P=0.004), indicating fewer patients would be sent unnecessarily to the emergency department.

The authors concluded that integrating clinical risk factors, symptom characteristics, and ECG data from the HeartBeam System into a single algorithm "may enable clinically meaningful ACS (heart attack) risk stratification at the early stages of chest pain" and could help shorten the time between symptom onset and treatment.