Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest.
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 255
- 试验地点
- 1
- 主要终点
- AI Identification Accuracy of Aortic Valve Compression
研究概览
简要总结
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.
详细描述
This three-year prospective study is designed to develop and clinically validate an "AI-Enhanced Arterial Waveform Monitor" to guide precision CPR.
- 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.
- 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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults aged 20 years or older.
- •Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.
- •Cardiac arrest caused by non-traumatic factors.
排除标准
- •Pregnant patients.
- •Patients with obvious signs of death.
- •Patients with a signed "Do Not Resuscitate" (DNR) order.
- •Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
- •Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA).
- •Cardiac arrest caused by massive hemorrhage, aortic emergencies, tension pneumothorax, cardiac tamponade, or pulmonary embolism.
- •History of severe aortic valve disease or previous aortic valve surgery.
- •Patients for whom TEE or femoral arterial catheterization is contraindicated.
- •Situations where the medical team is unable to perform TEE or femoral arterial catheterization during CPR.
研究组 & 干预措施
OHCA Patients Receiving AI-Enhanced Resuscitation.
Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3).
干预措施: Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App) (Device)
OHCA Patients Receiving AI-Enhanced Resuscitation.
Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3).
干预措施: AI-Guided Chest Compression Repositioning (Procedure)
OHCA Patients Receiving AI-Enhanced Resuscitation.
Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3).
干预措施: Transesophageal Echocardiography (TEE) (Diagnostic Test)
结局指标
主要结局
AI Identification Accuracy of Aortic Valve Compression
时间窗: 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.
次要结局
- Successful Avoidance of Aortic Valve Compression(During the clinical feasibility assessment (Year 3).)
- Time Consumed for Compression Adjustment(During the clinical feasibility assessment (Year 3).)
- 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).)
- Favorable Neurologic Outcome at Discharge(At the time of hospital discharge (up to approximately 30 days).)
- Chest Compression Fraction (CCF)(During the clinical feasibility assessment (Year 3).)
研究者
Sheng-En Chu
physician
Far Eastern Memorial Hospital