AI-guided Prediction and Treatment of Cardiac Arrest
This study is testing a new device called the Machine learning-guided cardiac arrest prediction device. This device uses heart tracings (ECG) to predict when someone might have another cardiac arrest (rearrest) after being successfully revived, and why it's happening. The goal is to help emergency medical services (EMS) providers predict and treat rearrests faster, which could improve survival. This study is looking for adult EMS providers for a simulation part of the trial, and adult patients who have had an out-of-hospital cardiac arrest. The study will measure how acceptable the device is to use and how quickly it can lead to treatment benefits. The current status of the study is unclear, and it plans to enroll 68 participants.
- Study design
- This is an interventional study, meaning participants will receive a specific intervention. It plans to enroll 68 participants, including both EMS providers and patients.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- The study measures implementation acceptability immediately after a simulation session (within 5 hours of enrollment) and time to treatment benefit up to 2 hours from enrollment.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
AI-guided Prediction and Treatment of Cardiac Arrest
At a glance
Conditions
Where it's being run
1 sites across 1 statesWho to contact
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What this trial measures
- Mean Implementation Acceptability ScoreAssessed once immediately after completion of the simulation session (within 5 hours of enrollment).
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.
- Calculated time to treatment benefitFrom subject enrollment up to 2 hours
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).