Predicting and preventing all forms of cardiac arrest in hospitals with CodeRhythm™ AI software.

In-hospital cardiac arrest is a leading cause of death in the UK, impacting \>15k patients per year. Only one-in-five victims ultimately leave the hospital alive.

Cardiac arrest can often be prevented by making timely changes to a patient's medical care before the heart stops beating, but such changes can only be made if physicians are alerted that a patient is in danger with enough time to act. When cardiac arrest does occur, patients are much more likely to survive when physicians act right away. Delays of mere minutes can dramatically lower a patient's odds of surviving.

Current ECG monitoring technology only identifies cardiac arrest after it occurs, providing no help for prevention and making rapid response difficult. Future predictive technology that alerts nurses and physicians when a patient develops a high-risk of imminent cardiac arrest could enable life-saving preventive care and faster response when cardiac arrest nonetheless occurs. Transformative is building this technology.

Using advanced AI techniques originally developed at the CERN Large Hadron Collider, and having amassed the world's largest database of hospital continuous ECG monitoring data, Transformative has built a proprietary machine learning algorithm---CodeRhythm(tm)---that can predict cardiac arrests caused by a shockable rhythm. Based on initial climnical testing, CodeRhythm has a 97% accuracy at predicting shockable IHCA up to 8 hours in advance. Having proven the ability to accurately predict cardiac arrest due to shockable rhythms, Transformative now aims, in this proposed project, to improve CodeRhythm so that it can also predict non-shockable cardiac arrest.

With Innovate-UK support and in collaboration with Barts NHS Trust, a 15-month project, will deliver a proof-of-concept model that predicts all types of cardiac arrest. If successful, the approach will offer the first reliable predictive monitoring software for all forms of cardiac arrest, which could improve care for high-risk patients and more than double survival rates.