Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation
NCT Number: NCT06538155
Acronym: TIME-CARE
Status: NOT_YET_RECRUITING
Study Type: OBSERVATIONAL
Conditions: Out-Of-Hospital Cardiac Arrest|Cardiac Arrest
Interventions: DEVICE: Wearable device|OTHER: Cardiopulmonary resuscitation|OTHER: CT scan, TEE exam, or chest X ray
Sponsor: Università Vita-Salute San Raffaele
Collaborators: IRCCS Ospedale San Raffaele|Politecnico di Milano|Azienda Ospedaliera Universitaria "Luigi Vanvitelli" (AOV)|Azienda Ospedaliera Universitaria Federico II (AOU Federico II)
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 500
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-08-31
Completion Date: 2026-08-31

Summary:
Out-of-hospital cardiac arrest (OHCA) affects 275,000 people in Europe every year. In Italy alone, 50,000 people experience OHCA annually, with only 9% surviving. Half of the survivors suffer severe brain damage. Immediate CPR and defibrillation by bystanders before the ambulance arrives can save lives, but often, CPR starts only when the ambulance gets there. Additionally, half of all OHCAs occur when the person is alone, causing delays in recognizing the emergency, calling for help, and starting lifesaving actions. Effective chest compressions and defibrillation are crucial but are often not done correctly or are not customized for each patient. Current guidelines recommend the same approach for everyone, which doesn't consider individual needs.

To tackle these issues, we plan to develop artificial intelligence (AI) algorithms, smartphone apps, and new devices. Our main goal is to create tools and technologies to improve the recognition of OHCA and provide timely and effective interventions, ultimately reducing the impact of OHCA and improving survival rates.

First, we aim to create an AI algorithm that can predict major cardiovascular events like heart attacks or cardiac arrests minutes, hours, or days before they happen. We will collect data from wearable devices to train and validate this algorithm, helping us identify individuals at risk. By alerting these individuals, they can seek emergency care and receive treatment before a cardiac arrest occurs. We will also work on recognizing OHCA cases from surveillance camera footage when they happen to people who are alone.

Second, to increase the rate of CPR and defibrillation before ambulances arrive, we will develop a smartphone app that geolocates and alerts nearby citizens to act as first responders. The app will guide them on how to quickly find a defibrillator and use it.

Third, to find the best spots on the chest for compressions and defibrillation, we will study chest scans from CTs and echocardiograms in both elective patients and cardiac arrest victims. This will help us understand the effects of compressing different heart structures and develop a sensor to determine the optimal positions for compressions and defibrillator pads.

Our multidisciplinary team of clinicians, researchers, and engineers will conduct experimental, simulation, and observational studies to develop these technologies, evaluate their potential for patents, design a plan for their use, and test their effectiveness in preventing and recognizing OHCA. We believe that by improving each step in the chain of survival-preventing cardiac events, early recognition, timely CPR and defibrillation, and high-quality advanced resuscitation-we can significantly improve treatment times and reduce the global death and disability rates caused by OHCA.

Primary Outcome Measures: Survival, Hospital discharge (4 weeks for hospital admission)