AI-guided Prediction and Treatment of Cardiac Arrest

Part of paid clinical trials in Cleveland, Ohio.

Sponsor
MetroHealth Medical Center
Study ID
NCT07452016
Status
Not Yet Recruiting

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Conditions

  • Sudden Cardiac Arrest

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • Machine learning-guided cardiac arrest prediction device — DEVICE
    A machine learning-guided cardiac arrest prediction device will be used to predict recurrence of cardiac arrest after initially successful resuscitation. It will also predict if the recurrent cardiac arrest is caused by ventricular fibrillation/tachycardia or pulseless electrical activity.

Study Details

Sudden cardiac arrest is a major health problem, and most people don't survive. One big reason is that even if resuscitation is successful, people commonly have recurrent cardiac arrests (rearrest). Right now, it is not possible to accurately predict a rearrest or prevent it. The investigators have developed a machine learning device that uses the heart tracing (ECG) to predict when and why a rearrest occurs. The investigators plan to test if it will accurately and effectively help EMS providers predict rearrest and provide timely treatment to increase survival after cardiac arrest. To determine if this machine learning device will work in the real world, the investigators need to find out if there are barriers to using it, and whether EMS providers will think it is useful and will help them improve the care of patients who have a cardiac arrest. The investigators will first test the device in live simulated cardiac arrest scenarios to see if the providers can use it and if they find the device potentially valuable in taking care of patients. In a second study, the investigators will test how accurate the device is in predicting if a cardiac arrest will happen again in patients who have just been brought back to life after a cardiac arrest. EMS providers will attach the device, but it will only work in the background. EMS will take care of patients as they normally would, without using or knowing what the device says. To see if the device is accurate at predicting another cardiac arrest, the investigators will analyze the results offline, and compare what the device says to what actually happens to the patient. By comparing what the device predicts to what actually happens, the investigators can see how well it predicts another cardiac arrest and estimate how it might improve treatment of patients.

Key Dates

Start date
Aug 31, 2026
Status verified
Mar 2026
Primary completion
Jun 30, 2029
Completion
Jun 30, 2029

Study Design

Enrollment
68 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH

Arms

  • Experimental: Emergency Medical Service Providers
    Emergency Medical Service Providers will experience high fidelity cardiac arrest simulations and test the barriers and facilitators to using a machine learning guided prediction device in simulated cardiac arrest patients.
  • Other: Patients who experience cardiac arrest cared for by EMS
    Patients who experience cardiac arrest will receive normal standard of care treatments. A machine learning guided prediction device will run in the background and also receive the normally acquired ECG data. Offline, the accuracy of the device to predict recurrent cardiac arrest and the type of rearrest which occurs after successful return of spontaneous circulation will be determined.

Primary Outcome Measure

Mean Implementation Acceptability Score [ Time Frame: Assessed once immediately after completion of the simulation session (within 5 hours of enrollment). ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
The MetroHealth SystemClevelandOhio44109
Lance Wilson Attending Physician and Professor, Emergency Medicine, MD
[email protected]
216-978-6274
Julie Nichols Research Coordinator, RN
[email protected]
(216) 957-6488
Jeremiah Escajeda, MD (SUB_INVESTIGATOR)
Thomas Noeller, MD (SUB_INVESTIGATOR)
Joseph Piktel, MD (SUB_INVESTIGATOR)

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By research site
The MetroHealth System· Cleveland, OH

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