AI-guided Prediction and Treatment of Cardiac Arrest
ConditionSudden Cardiac Arrest
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorMetroHealth Medical Center
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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)
Disqualifiers
Non-English-speaking providers
Providers who do not care for cardiac arrest patients
Prisoners
Pediatric patients under age of 18
Trial design
Design model
Parallel
Treatments tested in this trial
Machine learning-guided cardiac arrest prediction device
DeviceA 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.
Treatment groups
68 Participants
are divided into 2 treatment groupsGroup A: Emergency Medical Service ProvidersExperimental treatment 1 intervention
Group B: Patients who experience cardiac arrest cared for by EMSOther 1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
Assessed once immediately after completion of the simulation session (within 5 hours of enrollment).2
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).
Time frame
From subject enrollment up to 2 hoursSecondary outcomes
1
Accuracy of ML-guided rearrest predication
Standard accuracy measures of device predication will performance in the phase 2 observational clinical trial. Machine learning prediction of rearrest occurrence and rhythm type will be compared to the actual observed rearrest occurrence and rhythm type. Accuracy will be defined as the percentage of correct predictions (True Positives + True Negatives) over the total dataset and shown in a confusion matrix.
Time frame
From subject enrollment up to 2 hours2
Time to machine learning guided prediction
Time to machine learning prediction of rearrest will be performed in the observational clinical trial of the ML-guided prediction device. Time to prediction will be measured (in seconds) from the time of EMS arrival to time the machine learning prediction device predicts the first rearrest with confidence greater than or equal to 70%.
Time frame
From subject enrollment up to 2 hours3
Time to device deployment in simulated cardiac arrest
Time to machine learning device deployment will be measured in cardiac arrest simulations. Time to deploy the device in simulated cardiac arrest patients will be measured (in seconds) from the time of simulated EMS arrival to time the machine learning prediction device is deployed and begins to collect data.
Time frame
Assessed once immediately after completion of the simulation session (within 5 hours of enrollment).Sponsors and contacts
Click on the lead sponsor to view all of their trials.
MetroHealth Medical Center
Lead sponsor
National Center for Advancing Translational Sciences (NCATS)
Collaborator
Source ClinicalTrials.gov
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