AI tool aims to predict deadly repeat cardiac arrests in real time

NCT ID NCT07452016

Not yet recruiting Knowledge-focused Sponsor: MetroHealth Medical Center Source: ClinicalTrials.gov ↗

First seen Jun 26, 2026 · Last updated Jun 27, 2026 · Updated 1 time

Summary

This study tests a machine learning device that uses heart tracings (ECG) to predict if a person will have another cardiac arrest after being revived. Researchers will first see if EMS providers find the device useful in simulated scenarios, then test its accuracy in 68 real patients. The goal is to help emergency teams treat rearrests faster and improve survival.

What this could mean

Our plain-language read of the trial. This is informational only — not medical advice or a prediction.

Active substance: machine learning-guided cardiac arrest prediction device
What this could lead to: If successful, this device could help EMS providers predict and treat recurrent cardiac arrest faster, potentially improving survival rates.
What could go wrong: This is an early feasibility study with only 68 participants, testing accuracy and usability, not yet proven to save lives. The device may not work as well in real-world conditions.

This is an AI summary of the original study and may miss details. Read our disclaimer.

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Conditions

The condition(s) this trial relates to.

cardiac arrest Death, Sudden, Cardiac sudden cardiac arrest ventricular fibrillation ventricular tachycardia

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

SUDDEN CARDIAC ARREST

Contacts and locations

Locations

The MetroHealth System

Cleveland, Ohio, 44109, United States

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