The AI-based app 'ECG Buddy™' demonstrated AUC-ROC scores of 0.772 for mechanical circulatory support and 0.759 for ad-hoc PCI, indicating its effectiveness in decision-making.
Does the AI-based app 'ECG Buddy' accurately predict the need for mechanical circulatory support and ad-hoc PCI in patients with suspected acute coronary syndrome?
An AI-powered smartphone app analyzing 12-lead ECG images demonstrated moderate predictive ability for determining the need for mechanical circulatory support and ad-hoc PCI in patients with suspected acute coronary syndrome.
Absolute Event Rate: 0% vs 0%
Abstract Background The integration of artificial intelligence (AI) and 12-lead ECGs is an essential topic for digital cardiology, and evidence is growing. In recent years, there has been a smattering of smartphone apps that capture 12-lead ECGs. The AI-based app "ECG Buddy™" extracts ECG rhythms and digital biomarkers from 12-lead ECGs. Purpose The purpose of this study is to evaluate the effectiveness of this app using 12-lead ECG imaging in determining emergency intervention strategies for coronary artery disease. Methods Cross-sectional study of patients with suspected ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI)/unstable angina pectoris (UAP) who underwent emergency coronary angiography (eCAG) between 1/2024 and 12/2024 at St. Marianna University Hospital (Kawasaki, Japan). The application included ECG rhythm and 10 digital biomarkers, including "Critical Condition" score and "Acute Coronary Syndrome (ACS)" score (0–100 points each), which can estimate coronary artery occlusion and cardiac ischemic damage were extracted. Results 158 of 207 patients met the inclusion criteria (exclusions: out-of-hospital cardiac arrest, congenital heart disease, history of cardiac surgery, pacemaker rhythm, ECG not taken 48 hours before CAG, heart rate = 150/min or 40/min). At the time of the pre-eCAG diagnosis, there were 58 and 100 STEMI and NSTEMI/UAP patients, respectively. The mean age was 73 ± 13 years and 61% were male. 102 patients underwent ad-hoc percutaneous coronary intervention (PCI) and 31 patients required mechanical circulatory support (MCS). The AUC-ROC (receiver operating characteristic) of the app-calculated "critical" score for MCS use was 0.772 (95% CI: 0.682-0.862, p 0.001) (Figure 1). The AUC-ROC of the app-calculated "ACS" score for ad-hoc PCI was 0.759 (95% CI: 0.686-0.831, p 0.001) (Figure 2). Conclusion The app-calculated "critical" score and "ACS" score may be useful in determining the use of mechanical circulatory support and distinguishing the need for ad-hoc PCI.Figure 1and 2
Higuma et al. (Thu,) reported a other. The AI-based app 'ECG Buddy™' demonstrated AUC-ROC scores of 0.772 for mechanical circulatory support and 0.759 for ad-hoc PCI, indicating its effectiveness in decision-making.