Out-of-hospital Cardiac Arrest (OHCA), Cardiopulmonary Resuscitation (CPR), Aortic Valve Compression, Precision Resuscitation
Conditions
Keywords
Artificial Intelligence, Transesophageal Echocardiography, Arterial Pressure Waveform, Chest Compression Location, Personalized Resuscitation, Hemodynamic Monitoring
Brief summary
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.
Detailed description
This three-year prospective study is designed to develop and clinically validate an "AI-Enhanced Arterial Waveform Monitor" to guide precision CPR. 1. Research Hypothesis and Objectives The study tests the hypothesis that AI can accurately predict aortic valve compression (confirmed by Transesophageal Echocardiography, TEE) by analyzing arterial pressure waveforms, thereby allowing rescuers to find the optimal compression site that avoids the aortic valve and maximizes cardiac output. 2. Implementation Phases The project is divided into five distinct stages: Case Preparation: Enrollment of 150 OHCA patients to collect synchronized TEE video and arterial pressure data. Arterial Waveform Detection Model: Development of an algorithm to automatically segment continuous pressure signals into single-compression waveform samples. Compression Region Detection Model: Training a YOLO v8-based model integrated with patient physiological data (age, sex, medical history) to distinguish between "compressed" and "non-compressed" aortic valve states. Clinical External Testing: Enrolling an additional 75 patients to verify model accuracy against TEE "gold standard" findings. Feasibility Assessment: Deploying the model as a "Resuscitation Support App" in 30 real-world clinical cases to evaluate its real-time guidance capability, speed, and impact on patient outcomes. 3. Technical Methodology Data Extraction: Using binarization and interpolation curve fitting to extract high-quality numerical data directly from physiological monitor screens. AI Architecture: Utilizing an improved YOLO v8 framework combined with an Attention-based architecture and Fully-connected neural networks to incorporate complex patient heterogeneities. Clinical Intervention: When the AI identifies aortic valve compression, rescuers will be prompted to adjust the compression location (typically downward and to the left) until the valve is no longer obstructed. 4. Outcome Measures The study will evaluate the Identification Success Rate (AI vs. TEE), Avoidance Success Rate (successful repositioning), and traditional resuscitation metrics including ROSC, survival to discharge, and favorable neurologic outcomes.
Interventions
DEVICEDevice: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App)
A 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.
PROCEDUREAI-Guided Chest Compression Repositioning
When 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.
DIAGNOSTIC_TESTTransesophageal Echocardiography (TEE)
Used 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.
Sponsors
Far Eastern Memorial Hospital
National Health Research Institutes, Taiwan
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Adults aged 20 years or older. 2. Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department. Cardiac arrest caused by non-traumatic factors.
Exclusion criteria
1. Pregnant patients. 2. Patients with obvious signs of death. 3. Patients with a signed "Do Not Resuscitate" (DNR) order. 4. Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR). 5. Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA). 6. Cardiac arrest caused by massive hemorrhage, aortic emergencies, tension pneumothorax, cardiac tamponade, or pulmonary embolism. 7. History of severe aortic valve disease or previous aortic valve surgery. 8. Patients for whom TEE or femoral arterial catheterization is contraindicated. 9. Situations where the medical team is unable to perform TEE or femoral arterial catheterization during CPR.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AI Identification Accuracy of Aortic Valve Compression | Collected during the clinical testing phase and feasibility assessment (Years 2 and 3). | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Successful Avoidance of Aortic Valve Compression | During the clinical feasibility assessment (Year 3). | 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 | During the clinical feasibility assessment (Year 3). | The time interval between the first arterial waveform detection and the completion of the chest compression repositioning. |
| Rate of Return of Spontaneous Circulation (ROSC) | From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days). | 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 | At the time of hospital discharge (up to approximately 30 days). | Assessment of neurological status using the Cerebral Performance Category (CPC 1-2) or Modified Rankin Scale (mRS 0-2). |
| Chest Compression Fraction (CCF) | During the clinical feasibility assessment (Year 3). | The proportion of total resuscitation time during which chest compressions were performed, ensuring that AI-guided adjustments do not negatively impact the continuity of compressions. |
Countries
Taiwan
Contacts
CONTACTSheng-En Chu, physician
Outcome results
None listed