AI-Guided Prediction Device for Cardiac Arrest
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial tests a new device that uses machine learning to predict if someone who has just survived a cardiac arrest will experience another one. Sudden cardiac arrest poses a serious problem, and predicting a recurring event (rearrest) could help emergency services save more lives. Initially, the trial will assess how well emergency medical service (EMS) providers can use the machine learning-guided cardiac arrest prediction device during practice scenarios. Subsequently, it will evaluate whether the device can accurately predict another cardiac arrest in real-life situations without influencing the care provided. The trial seeks adult EMS providers and patients who have been revived from cardiac arrest. As an unphased trial, this study offers a unique opportunity to contribute to groundbreaking research that could enhance emergency response and save lives.
Do I have to stop taking my current medications for the trial?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that this AI-guided prediction device is safe for use in cardiac arrest scenarios?
Research has shown that machine learning tools have been safely used in many settings. One study demonstrated that AI-guided alerts reduced cardiac arrests and hospital deaths without requiring extra steps or causing harm. This suggests the technology can be used safely and effectively in real-life situations. Another study developed a machine learning model to predict cardiac arrests in emergency rooms, successfully identifying risks without negative effects.
These studies indicate that the machine learning approach, like the device under testing, is generally well-tolerated and does not lead to harmful outcomes. While this specific trial's device is still under study, existing evidence suggests that similar tools have been safe for use in healthcare settings.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores a new way to predict cardiac arrest using a machine learning-guided device. Unlike traditional methods that rely on standard ECG monitoring and clinical judgment, this device processes data to forecast potential cardiac arrests more accurately. By integrating advanced algorithms, the device could improve the speed and precision of interventions during emergencies, potentially saving more lives. This trial aims to uncover how effectively the device can predict recurrent cardiac arrests and improve emergency response strategies.
What evidence suggests that this device is effective for predicting cardiac arrest?
Research has shown that machine learning tools can help predict heart attacks. In this trial, Emergency Medical Service Providers will test a machine learning-guided cardiac arrest prediction device during high-fidelity cardiac arrest simulations. One study found that devices using artificial intelligence (AI) were linked to fewer heart attacks and lower death rates in hospitals. Another study demonstrated that these devices could accurately predict if a heart attack might recur after a patient has been successfully revived. Meanwhile, patients experiencing cardiac arrest in this trial will receive standard care, with the device running in the background to assess its predictive accuracy offline. These findings suggest that AI can improve outcomes for heart attack patients by providing timely alerts and interventions.12678
Are You a Good Fit for This Trial?
This trial is for adults (18+) who have been revived after a cardiac arrest outside the hospital, as well as EMS providers involved in their care. It excludes children, prisoners, those with do-not-resuscitate orders, people not resuscitated by EMS, and non-English-speaking providers.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Simulation Phase
Emergency Medical Service Providers participate in high fidelity cardiac arrest simulations to test the machine learning guided prediction device
Observational Phase
Patients who experience cardiac arrest are observed while a machine learning guided prediction device runs in the background
Follow-up
Participants are monitored for safety and effectiveness after the observational phase
What Are the Treatments Tested in This Trial?
Interventions
- Machine learning-guided cardiac arrest prediction device
Trial Overview
The study tests a new device that uses AI to analyze heart rhythms and predict if someone might have another cardiac arrest. First, it checks if EMS providers can use the device in simulations; then it measures how accurately the device predicts repeat arrests in real patients.
How Is the Trial Designed?
2
Treatment groups
Experimental Treatment
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.
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.
Find a Clinic Near You
Who Is Running the Clinical Trial?
MetroHealth Medical Center
Lead Sponsor
National Center for Advancing Translational Sciences (NCATS)
Collaborator
Citations
Clinical Effectiveness of an Artificial Intelligence-Based ... - PMC
Conclusions: AI-SaMD-guided alerts were associated with reductions in cardiac arrest and in-hospital mortality without requiring additional ...
AI-guided Prediction and Treatment of Cardiac Arrest
A machine learning-guided cardiac arrest prediction device will be used to predict recurrence of cardiac arrest after initially successful resuscitation. It ...
Development and validation of machine learning-based ...
We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 72 h.
Predicting In-Hospital Cardiac Arrest Using Machine Learning ...
This scoping review aims to synthesize and critically evaluate the quality and quantity of clinical features and machine learning (ML) models ...
5.
purdue.edu
purdue.edu/hhs/nur/dnp/student-project-examples/L%20Moffat-DNP%20Executive%20Summary.docxExecutive Summary
This paper described a machine learning approach to predict cardiac arrest in hospitalized critical care adults within a vast publicly available dataset. Our ...
Evaluation of machine learning models for personalized ...
Machine learning identified 2.5% of non-tMCS patients likely to survive if treated with tMCS. In 23 (RF model) and 31 (XGBoost model) patients, ...
Development of a machine-learning algorithm to predict in ...
In this study, we developed a machine-learning (ML) model to predict the occurrence of IHCA in the ED of patients arriving via EMS. Our research ...
From prediction to precision: Artificial intelligence and ...
Moreover, AI has shown promise in predicting cardiac arrests and dangerous arrhythmias through continuous ECG monitoring and evaluating post-arrest outcomes ...
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