MULTIMODAL STRATEGIES FOR EARLY DIAGNOSIS AND RISK STRATIFICATION OF ACUTE CORONARY SYNDROME FROM PREHOSPITAL CARE TO THE EMERGENCY DEPARTMENT: A SYSTEMATIC REVIEW

Abstract Background: Early identification and risk stratification of acute coronary syndrome (ACS) in the prehospital–emergency department (ED) pathway is challenging, particularly for non-ST-segment elevation presentations. Multimodal strategies that combine clinical assessment, ECG interpretation, and cardiac biomarkers, and more recently AI-supported ECG, improve early decision-making and resource use. Methods: We performed a PMC-focused systematic search for original studies evaluating multimodal approaches for suspected ACS from EMS to the ED. Eligible studies incorporated at least two complementary elements (risk score, ECG, troponin; or AI-ECG integrated with clinical evaluation). Randomized trials, prospective cohorts, and validation studies were included. Results: Data clustered into two domains. In the ED, structured pathways integrating risk scores with troponin testing supported rapid rule-out for selected low-risk patients. The HEART framework and the HEART Pathway provided consistent models combining clinical variables, ECG, and serial troponin to inform early discharge and reduce unnecessary testing in appropriate populations. EDACS-ADP and T-MACS offered alternative integrated strategies using score-based or biomarker-driven models with comparable intent to accelerate safe disposition for low-risk presentations. Prehospital studies showed that EMS application of chest pain risk tools is feasible, and the addition of point-of-care troponin in defined low-risk profiles reduce downstream utilization without compromising short-term safety. Emerging AI-ECG models further enhance early NSTE-ACS risk assessment before ED arrival. Conclusion: Integrated EMS–ED pathways combining clinical risk tools, ECG, and troponin appear most supported by current data, with AI-ECG as a promising adjunct.