Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation: the TIME-CARE Project
Trial ID: NCT06538155
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2025-01-27
Completion date: 2026-08-31
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)
Conditions: Out-Of-Hospital Cardiac Arrest; Cardiac Arrest
Interventions: Wearable device; Cardiopulmonary resuscitation; CT scan, TEE exam, or chest X ray
Locations: IRCCS Ospedale San Raffaele; AOU Policlinico Federico II; Azienda Ospedaliera Universitaria Vanvitelli
Countries: Italy; Italy; Italy
Enrollment: 500
Age range: 18 Years - None
Sex: ALL

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.

Eligibility criteria:
AIM 1: PREDICTION AND RECOGNITION OF CARDIAC ARREST AIM 1.1: PREDICT A MAJOR CARDIOVASCULAR EVENT

Inclusion criteria:

* Age 18-70 years;
* Being a healthy volunteer (i.e., an individual with no history of cardiovascular events willing to contribute to the project) or a patient (survivors and non-survivors) who experienced major cardiovascular events (i.e., myocardial infarction or cardiac arrest);
* Users of a smartwatch or smartphone that continuously and automatically collect health data;
* Informed consent.

Exclusion criteria:

* Impossibility to access/export data;
* User did not wear the wearable device for periods longer than 24 hours;
* User did not wear the wearable device in the 4 weeks preceding the event.

AIM 1.2 CARDIAC ARREST DETECTION FROM VIDEOS No patient involved.

AIM 2: TECHNOLOGIES TO INCREASE CPR AND DEFIBRILLATION USE BEFORE AMBULANCE ARRIVAL No patient involved.

AIM 3: PATIENT-TAILORED RESUSCITATION AIM 3.1: CLINICAL STUDY IN PATIENTS WHO RECEIVED CPR

Inclusion criteria:

* Adults (≥ 18 years);
* Patients suffering a non-traumatic cardiac arrest treated with chest compressions (both survivors and non-survivors);
* Received a TEE, chest x-ray, or chest CT scan as the standard clinical assessment following cardiac arrest;
* Informed consent.

Exclusion criteria:

\- Patients with severe thorax/mediastinal deformity.

AIM 3.2 CLINICAL STUDY IN PATIENTS WHO RECEIVED A CHEST CT SCAN

Inclusion criteria:

* Adults (≥ 18 years);
* Received a chest CT scan for any reasons;
* Informed consent.

Exclusion criteria:

\- Patients with severe thorax/mediastinal deformity.

AIM 3.3 MACHINE LEARNING (ML) ALGORITHM No patient involved.

Outcomes:
Survival