A Unified Mechanism for the Water Hammer Pulse and Pulsus Bisferiens in Severe Aortic Regurgitation: Insights from Wave Intensity Analysis.
Julio A Chirinos, Scott R Akers, Jan A Vierendeels...
https://pubmed.ncbi.nlm.nih.gov/29576810Actively Recruiting
Age: 20Years +
All Genders
ID07431710
Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest
Led by Far Eastern Memorial Hospital · Updated on 2026-02-24
255
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
Sponsors
F
Far Eastern Memorial Hospital
Lead Sponsor
N
National Health Research Institutes, Taiwan
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are exploring a new way to improve survival for adults who experience out-of-hospital cardiac arrest (OHCA). This study focuses on using artificial intelligence (AI) to guide chest compressions during cardiopulmonary resuscitation (CPR). Currently, chest compressions follow a standard location, but this may compress the aortic valve in nearly half of patients, reducing the chance of successful resuscitation. The study aims to develop an AI tool that analyzes arterial pressure waveforms in real time to identify the best compression site, shifting CPR towards a more personalized approach. The study involves developing and validating an AI-Enhanced Arterial Waveform Monitor, based on advanced deep learning techniques, including YOLO v8. The project has five phases: collecting data from 150 patients, creating algorithms to detect compression waveforms, training the AI to recognize aortic valve compression using patient data, clinically testing the AI on 75 additional patients, and finally assessing the AI's use as a real-time support app in 30 clinical cases. When the AI detects valve compression, rescuers adjust the chest compression position, usually downward and to the left, to improve blood flow. Participants are adults aged 20 or older with non-traumatic OHCA receiving advanced life support. The study collects detailed physiological data, including arterial pressure and transesophageal echocardiography (TEE) images, to train and test the AI. Outcomes measured include how accurately the AI identifies valve compression, success at repositioning compressions, time taken for adjustments, return of spontaneous circulation (ROSC), neurologic outcome at discharge, and chest compression metrics. The study spans about three years, with data collection, AI development, clinical testing, and real-world feasibility assessment phases.
CONDITIONS
Brief Title
The AIR-CPR Study: AI-Guided Chest Compressions
Who Can Participate
Age: 20Years +
All Genders
Eligibility Criteria
You may qualify if you...
- Adults aged 20 years or older
- Patients with out-of-hospital cardiac arrest (OHCA) undergoing cardiopulmonary resuscitation (CPR) in the emergency department
- Cardiac arrest caused by non-traumatic factors
You will not qualify if you...
- 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
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Monitoring
Duration - Up to approximately 30 days from emergency department resuscitation
Participants are monitored during cardiopulmonary resuscitation (CPR) with continuous arterial pressure waveform data collection and transesophageal echocardiography (TEE) to provide gold standard verification for AI model training and validation.
Continuous monitoring during CPR and emergency care
Implementation
Duration - During emergency department resuscitation period
The AI-Enhanced Arterial Waveform Monitor (AIR-CPR App) analyzes arterial pressure waveforms in real-time to guide rescuers on chest compression positioning to avoid aortic valve compression and optimize cardiac output during resuscitation efforts.
Real-time use during resuscitation events
Follow-up
Duration - Up to approximately 30 days after resuscitation
Participants are followed for outcomes including return of spontaneous circulation (ROSC), survival to discharge, and neurological status up to hospital discharge.
Approximately 1 to 2 visits during hospital stay
Trial Site Locations
Total: 1 location
1
Far Eastern Memorinal Hospital
New Taipei City, Banqiao, Taiwan, 220
Actively Recruiting
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Research Team
S
Sheng-En Chu, physician
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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