The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Current guidelines recommend a standardized compression point (the lower half of the sternum), yet recent research indicates that this position can compress the aortic valve in approximately 48.7% of patients, significantly reducing the chances of successful resuscitation.
This study will develop a deep learning model based on YOLO v8 to analyze real-time arterial pressure waveforms to identify proper aortic valve opening and closing. By identifying specific waveform features that humans cannot easily distinguish, the AI will guide rescuers to adjust the compression site-typically toward the left ventricle-to ensure optimal blood output. The project seeks to transform CPR from a standardized "one-size-fits-all" approach into a personalized, precision medicine intervention.
Adults aged 20 years or older.
Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.
Pregnant patients.
Patients with obvious signs of death.
Patients with a signed "Do Not Resuscitate" (DNR) order.
Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
The study population consists of adult patients experiencing non-traumatic out-of-hospital cardiac arrest who are transported to the Emergency Department of Far Eastern Memorial Hospital. This facility is the only medical center in New Taipei City and handles approximately 30% of the city's OHCA cases, ensuring a diverse and high-volume sample of patients receiving advanced life support.
Cohort
Prospective
Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App)
DeviceA deep learning application based on the YOLO v8 architecture that analyzes real-time arterial pressure waveforms from a femoral A-line. It identifies whether the current chest compression location is causing aortic valve compression (as confirmed by TEE) and provides immediate feedback to the resuscitation team.
AI-Guided Chest Compression Repositioning
Procedure/SurgeryWhen the AI application indicates aortic valve compression, the rescuer adjusts the mechanical chest compression (LUCAS) position. Based on literature and AI feedback, the adjustment typically involves moving the compression point downward and toward the left parasternal line to avoid the aortic valve and optimize left ventricular output.
Transesophageal Echocardiography (TEE)
Diagnostic testUsed as the "Gold Standard" throughout the study. TEE is performed during CPR to record the actual opening and closing of the aortic valve and the deformation of cardiac chambers, providing the labels for AI training and the verification for clinical testing.
AI Identification Accuracy of Aortic Valve Compression
The accuracy of the AI model in identifying whether the aortic valve is compressed or open during CPR, using Transesophageal Echocardiography (TEE) as the gold standard for verification.
Successful Avoidance of Aortic Valve Compression
The percentage of cases where the resuscitation team successfully adjusted the chest compression location to stop aortic valve compression based on AI app feedback, confirmed by TEE.
Time Consumed for Compression Adjustment
The time interval between the first arterial waveform detection and the completion of the chest compression repositioning.
Rate of Return of Spontaneous Circulation (ROSC)
Incidence of ROSC and sustained ROSC (maintained for $\\ge 20$ minutes), as well as survival rates to hospital admission and discharge.
Favorable Neurologic Outcome at Discharge
Assessment of neurological status using the Cerebral Performance Category (CPC 1-2) or Modified Rankin Scale (mRS 0-2).
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Far Eastern Memorial Hospital
Lead sponsor
National Health Research Institutes, Taiwan
Collaborator
