AI-guided Prediction and Treatment of Cardiac Arrest

Study 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.

Primary Outcome

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.

Interventions

• DEVICE Machine learning-guided cardiac arrest prediction device