The purpose of this study was to evaluate the effect of real-time audio ventilation feedback on the survival of patients with an out-of-hospital cardiac arrest (OHCA) during advanced cardiac life support (ACLS) performed by paramedics. This research was a prospective randomized controlled study performed in Busan, South Korea, from July 2022 to December 2022. This study included 121 patients, ages 19 and up, who were transferred to the study site, excluding 91 patients who did not receive CPR under a doctor’s direction as well as those who had a ’(DNR)’ order among 212 adult CA patients. OHCA patients’ clinical prognosis was compared by being randomly assigned to either a general manual defibrillator (NVF) group (N = 58) or a manual defibrillator with an audio ventilation feedback (AVF) group (N = 63). To verify the primary outcome, the cerebral performance category (CPC), return of spontaneous consciousness (ROSC), 30h survival, and survival discharge were compared. Multivariate logistic regression was conducted to analyze the association between the audio-feedback manual defibrillator (AVF) and the ROSC of OHCA patients. This study analyzed 121 patients among 212 OHCA patients. The ROSC (AVF group: 32 {26.4%} vs. NVF group: 21 {17.3%}), 24 h survival (AVF group: 24 {19.8%} vs. NVF group: 11 {9.0%}), and survival discharge (AVF group: 12 {9.9%} vs. NVF group: 6 {4.9%}) were higher in the AVF group than the NVF group. However, upon analyzing CPC scores in the surviving patients between the two groups, there was no significant difference (AVF group: 4.1 ± 1.23 vs. NVF group:4.7 ± 1.23, p = 1.232). Multivariate logistic regression analysis showed that the use of AVF was associated with a higher ROSC (odds ratio {OR}, 0.46; 95% confidence interval {CI}, 0.23–0.73; p < 0.01) and higher survival at 30 h (OR, 0.63; 95% CI, 0.41–0.98; p = 0.01).

Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, n = 763) or AI-output disclosure days (AI-days, n = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive–related operational impacts.

Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, N = 763) or AI output disclosure days (AI-days, N = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive-related operational impacts.