AI-guided Prediction and Treatment of Cardiac Arrest

NCT07452016 | Not Yet Recruiting

• 2 other identifiers: STUDY00001031UM1TR004528
• Study Type: interventional
• Target: 68
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Sudden Cardiac Arrest

Keywords

cardiac arrest; sudden cardiac death; machine learning; rearrest; ventricular fibrillation; ventricular tachycardia; pulseless electrical activity

Outcome Measures

Primary Outcomes (2)

• Mean Implementation Acceptability Score

  Mean score on a 20-item post-simulation survey adapted from the Consolidated Framework for Implementation Research (CFIR). Each item is rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The composite score is calculated as the mean of all items (range 1-5), with higher scores indicating greater perceived implementation acceptability.

  Assessed once immediately after completion of the simulation session (within 5 hours of enrollment).

• Calculated time to treatment benefit

  Determination of estimated change in time to treatments for cardiac arrest patients from the observational clinical trial of the ML-guided prediction device. Time to treatment will be measured (in seconds) from time to EMS arrival at scene to treatment time for the first rearrest is rendered. This will be compared to calculated time to treatment, measured from EMS arrival to machine learning prediction (in seconds).

  From subject enrollment up to 2 hours

Secondary Outcomes (3)

• Accuracy of ML-guided rearrest predication

  From subject enrollment up to 2 hours

• Time to machine learning guided prediction

  From subject enrollment up to 2 hours

• Time to device deployment in simulated cardiac arrest

  Assessed once immediately after completion of the simulation session (within 5 hours of enrollment).

Study Arms (2)

Emergency Medical Service Providers

EXPERIMENTAL

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.

Device: Machine learning-guided cardiac arrest prediction device

Patients who experience cardiac arrest cared for by EMS

OTHER

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.

Device: Machine learning-guided cardiac arrest prediction device

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.

Emergency Medical Service Providers; Patients who experience cardiac arrest cared for by EMS

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adult (18 years of age or older) EMS providers (Simulation trial)
• Adult (18 years of age or older) patients have attempted resuscitation from out-of-hospital SCA of any etiology (Clinical trail)

You may not qualify if:

• Non-English-speaking providers
• Providers who do not care for cardiac arrest patients
• Prisoners
• Pediatric patients under age of 18
• DNR/DNI
• No resuscitation attempted (declared deceased in field by EMS)

Sponsors & Collaborators

• MetroHealth Medical Center: lead
• National Center for Advancing Translational Sciences (NCATS): collaborator

Study Sites (1)

The MetroHealth System

Cleveland, Ohio, 44109, United States

Location

MeSH Terms

Conditions

Death, Sudden, Cardiac; Heart Arrest; Ventricular Fibrillation; Tachycardia, Ventricular

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Death, Sudden; Death; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Arrhythmias, Cardiac; Tachycardia; Cardiac Conduction System Disease

Central Study Contacts

Lance Wilson, MD

CONTACT

12169786274; lwilson@metrohealth.org

Julie Nichols Research Coordinator, RN

CONTACT

(216) 957-6488; jnichols@metrohealth.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Emergency Medicine

Study Record Dates

• First Submitted: February 17, 2026
• First Posted: March 5, 2026
• Study Start (Estimated): August 1, 2026
• Primary Completion (Estimated): June 1, 2029
• Study Completion (Estimated): June 1, 2029
• Last Updated: March 16, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP
• Time Frame: After completion of study and study reporting, and an additional 2 years.

Locations

US (1)