Out-Of-Hospital Cardiac ArrestTIME-CARENCT06538155
Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation
An observational study of Out-Of-Hospital Cardiac Arrest and Cardiac Arrest, run by Università Vita-Salute San Raffaele at 3 sites in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Out-Of-Hospital Cardiac Arrest, Cardiac Arrest
- What is being tested?
- Wearable device, Cardiopulmonary resuscitation and CT scan, TEE exam, or chest X ray
- Type of treatment
- Medical device
- How many people
- 500 participants
- Where
- 3 sitesItaly
- Study dates
- 27 Jan 2025to 31 Aug 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: * Healthy volunteers (every adult individual with no history of cardiovascular events willing to contribute to the project) and patients who experienced major cardiovascular events (i.e., myocardial infarction or cardiac arrest). Both groups must have worn a wearable device or used a smartphone able to collect healthcare data and biosignals. * Patients ≥18 years resuscitated after cardiac arrest or during ongoing cardiopulmonary resuscitation (CPR). * Patients ≥ 18 years who received a chest CT scan for any reasons.
Who may be able to join
- 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.
- Adults (≥ 18 years);
- Patients suffering a non-traumatic cardiac arrest treated with chest compressions (both survivors and non-survivors);
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- Received a TEE, chest x-ray, or chest CT scan as the standard clinical assessment following cardiac arrest;
- Informed consent.
- Adults (≥ 18 years);
- Received a chest CT scan for any reasons;
- Informed consent.
Who cannot join
- 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
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- Patients with severe thorax/mediastinal deformity.
- AIM 3.2 CLINICAL STUDY IN PATIENTS WHO RECEIVED A CHEST CT SCAN
- Patients with severe thorax/mediastinal deformity.
- AIM 3.3 MACHINE LEARNING (ML) ALGORITHM No patient involved.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Italy3 sites, 3 recruiting
- Recruiting
IRCCS Ospedale San Raffaele
Milan, 20132
Tommaso Squizzato, MD, 0226438296 ext. +39, squizzato.tommaso@hsr.it
- Not yet recruiting
AOU Policlinico Federico II
Naples, 80100
- Not yet recruiting
Azienda Ospedaliera Universitaria Vanvitelli
Naples, 80138
Maria Caterina Pace, MD
About this study
Official title: Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation: the TIME-CARE Project
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.
Read more
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.
Read the detailed description
Out-of-hospital cardiac arrest (OHCA) annually affects 275000 individuals in Europe. In Italy alone, 50000 persons suffer from OHCA each year and only 9% survives1. Half of the survivors are left with severe brain damage. Prompt cardiopulmonary resuscitation (CPR) and defibrillation before ambulance arrival by bystanders can improve outcomes. However, in many cases, CPR only starts when the ambulance arrives. Additionally, half of all OHCAs occur in isolation, meaning that recognition, emergency calls, and lifesaving maneuvers are delayed. Chest compressions and defibrillation are critical for survival, but they are frequently inadequate or not patient-tailored. Current CPR guidelines recommend a uniform approach to chest compressions and defibrillation for all patients, which fails to account for individual differences. To address these unmet medical needs, we will develop artificial intelligence algorithms, smartphone apps, and novel devices. Starting with proof-of-concept approaches that we have already conceived, we will work to improve recognition of OHCA and provide timely and effective interventions. Our goal is to create tools and technologies that can help reduce the burden of OHCA and improve outcomes.
First, we aim to develop an artificial intelligence algorithm that can predict (minutes, hours, or days in advance) major cardiovascular events, such as myocardial infarction or cardiac arrest. To achieve this, we will collect biosignals recorded by wearables to train and validate the algorithm to identify individuals who are at risk of a major cardiovascular event. Alerted individuals will seek emergency medical care and receive treatments before a cardiac arrest occurs. We also aim to recognize OHCAs that occur in isolation from videos of surveillance cameras.
Second, to increase the rate of CPR and defibrillation provided before ambulance arrival, we will develop a smartphone app that will geolocate and alert nearby citizens to act as first responders. The app will also provide guidance on quickly retrieving a defibrillator.
Third, to determine the optimal compressions and defibrillation position on the chest, we will acquire scans of chest computer tomography and transesophageal echocardiography in elective patients and in victims of cardiac arrest. This will allow to determine optimal compression and defibrillator pads position, understanding the effects on outcomes of different cardiac structures compressed, and developing a modern sensor to estimate the optimal compression and defibrillator pads position on the chest.
Through experimental, simulation and observational studies and a multidisciplinary team of clinicians, researchers and engineers, we will develop the proof-of-concept of such technologies, evaluate their patentability, design an exploitation plan, and test efficacy in preventing and anticipating recognition of OHCA, reducing time to CPR and defibrillation, and offering patient-tailored CPR and defibrillation. Our underlying hypothesis is that developing novel methods and technologies to enhance each link in the chain of survival (preventative measures, early recognition, timely initiation of CPR and defibrillation, and high-quality advanced resuscitation) will significantly anticipate lifesaving treatments and reduce the global mortality and disability caused by OHCA.
- Conditions
- Out-Of-Hospital Cardiac Arrest, Cardiac Arrest
- Being tested
- Wearable device, Cardiopulmonary resuscitation, CT scan, TEE exam, or chest X ray
- Keywords
- out-of-hospital cardiac arrest, cardiac arrest, cardiopulmonary resuscitation, extracorporeal cardiopulmonary resuscitation
- Medical subject headings
- Out-of-Hospital Cardiac Arrest, Heart Arrest
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Wearable deviceDevice
Wearable devices that are preferentially Food and Drug Administration (FDA) and/or Conformité Européenne (CE) marked
Cardiopulmonary resuscitationOther
Cardiopulmonary resuscitation
CT scan, TEE exam, or chest X rayOther
Chest CT scan, transesophageal echocardiogram (TEE) scans, or chest X ray
| Study group | Role | What they receive |
|---|---|---|
| Wearable device users | Healthy volunteers (every adult individual with no history of cardiovascular events willing to contribute to the project) and patients who experienced major cardiovascular events (i.e., myocardial infarction or cardiac arrest). Both groups must have worn a wearable device or used a smartphone able to collect healthcare data and biosignals. Receives: Device: Wearable device | |
| Patients with cardiac arrest | Adults resuscitated after cardiac arrest or during ongoing cardiopulmonary resuscitation (CPR). Receives: Other: Cardiopulmonary resuscitation; Other: CT scan, TEE exam, or chest X ray | |
| Patients who received a CT scan | Adults who received a chest CT scan for any reasons. Receives: Other: CT scan, TEE exam, or chest X ray |
What is being measured
The primary outcome is the main question the study is designed to answer.
Survival
Measured: Hospital discharge (4 weeks for hospital admission)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Aug 2024
- Study start27 Jan 2025
- Primary completion31 Jul 2026 (estimated)
- Study completion31 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Out-Of-Hospital Cardiac ArrestStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Università Vita-Salute San Raffaele
- Sponsor type
- University, hospital or other
- Collaborators
- IRCCS Ospedale San Raffaele, Politecnico di Milano, Azienda Ospedaliera Universitaria "Luigi Vanvitelli" (AOV), Azienda Ospedaliera Universitaria Federico II (AOU Federico II)
- Responsible party
- Principal investigator, Giovanni Landoni, Università Vita-Salute San Raffaele
- Investigators
- Alberto Zangrillo, MD, Principal investigator, IRCCS Ospedale San Raffaele
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06538155
- Sponsor's protocol ID
- TIME-CARE 264-2024
- Other IDs
- PNRR-POC-2023-123771
- Acronym
- TIME-CARE
- Status last verified
- Aug 2025
- First submitted
- 31 Jul 2024
- Last updated
- 6 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation: the TIME-CARE Project
- ClinicalTrials.gov ID
- NCT06538155
- Acronym / sponsor ID
- TIME-CARE / TIME-CARE 264-2024
- Conditions
- Out-Of-Hospital Cardiac Arrest, Cardiac Arrest
- Keywords
- out-of-hospital cardiac arrest, cardiac arrest, cardiopulmonary resuscitation, extracorporeal cardiopulmonary resuscitation
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Università Vita-Salute San RaffaeleUniversity, hospital or other
- Collaborators
- IRCCS Ospedale San Raffaele, Politecnico di Milano, Azienda Ospedaliera Universitaria "Luigi Vanvitelli" (AOV), Azienda Ospedaliera Universitaria Federico II (AOU Federico II)
- Study start
- 27 Jan 2025 (actual)
- Primary completion
- 31 Jul 2026 (estimated)
- Study completion
- 31 Aug 2026 (estimated)
- First posted
- 5 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Out-of-hospital cardiac arrest (OHCA) annually affects 275000 individuals in Europe. In Italy alone, 50000 persons suffer from OHCA each year and only 9% survives1. Half of the survivors are left with severe brain damage. Prompt cardiopulmonary resuscitation (CPR) and defibrillation before ambulance arrival by bystanders can improve outcomes. However, in many cases, CPR only starts when the ambulance arrives. Additionally, half of all OHCAs occur in isolation, meaning that recognition, emergency calls, and lifesaving maneuvers are delayed. Chest compressions and defibrillation are critical for survival, but they are frequently inadequate or not patient-tailored. Current CPR guidelines recommend a uniform approach to chest compressions and defibrillation for all patients, which fails to account for individual differences. To address these unmet medical needs, we will develop artificial intelligence algorithms, smartphone apps, and novel devices. Starting with proof-of-concept approaches that we have already conceived, we will work to improve recognition of OHCA and provide timely and effective interventions. Our goal is to create tools and technologies that can help reduce the burden of OHCA and improve outcomes.
First, we aim to develop an artificial intelligence algorithm that can predict (minutes, hours, or days in advance) major cardiovascular events, such as myocardial infarction or cardiac arrest. To achieve this, we will collect biosignals recorded by wearables to train and validate the algorithm to identify individuals who are at risk of a major cardiovascular event. Alerted individuals will seek emergency medical care and receive treatments before a cardiac arrest occurs. We also aim to recognize OHCAs that occur in isolation from videos of surveillance cameras.
Second, to increase the rate of CPR and defibrillation provided before ambulance arrival, we will develop a smartphone app that will geolocate and alert nearby citizens to act as first responders. The app will also provide guidance on quickly retrieving a defibrillator.
Third, to determine the optimal compressions and defibrillation position on the chest, we will acquire scans of chest computer tomography and transesophageal echocardiography in elective patients and in victims of cardiac arrest. This will allow to determine optimal compression and defibrillator pads position, understanding the effects on outcomes of different cardiac structures compressed, and developing a modern sensor to estimate the optimal compression and defibrillator pads position on the chest.
Through experimental, simulation and observational studies and a multidisciplinary team of clinicians, researchers and engineers, we will develop the proof-of-concept of such technologies, evaluate their patentability, design an exploitation plan, and test efficacy in preventing and anticipating recognition of OHCA, reducing time to CPR and defibrillation, and offering patient-tailored CPR and defibrillation. Our underlying hypothesis is that developing novel methods and technologies to enhance each link in the chain of survival (preventative measures, early recognition, timely initiation of CPR and defibrillation, and high-quality advanced resuscitation) will significantly anticipate lifesaving treatments and reduce the global mortality and disability caused by OHCA.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Wearable device users | – | Device: Wearable deviceHealthy volunteers (every adult individual with no history of cardiovascular events willing to contribute to the project) and patients who experienced major cardiovascular events (i.e., myocardial infarction or cardiac arrest). Both groups must have worn a wearable device or used a smartphone able to collect healthcare data and biosignals. |
| Patients with cardiac arrest | – | Other: Cardiopulmonary resuscitation, Other: CT scan, TEE exam, or chest X rayAdults resuscitated after cardiac arrest or during ongoing cardiopulmonary resuscitation (CPR). |
| Patients who received a CT scan | – | Other: CT scan, TEE exam, or chest X rayAdults who received a chest CT scan for any reasons. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- * Healthy volunteers (every adult individual with no history of cardiovascular events willing to contribute to the project) and patients who experienced major cardiovascular events (i.e., myocardial infarction or cardiac arrest). Both groups must have worn a wearable device or used a smartphone able to collect healthcare data and biosignals. * Patients ≥18 years resuscitated after cardiac arrest or during ongoing cardiopulmonary resuscitation (CPR). * Patients ≥ 18 years who received a chest CT scan for any reasons.
Key inclusion criteria (11 listed)
- 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.
- Adults (≥ 18 years);
Key exclusion criteria (10 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Wearable deviceWearable devices that are preferentially Food and Drug Administration (FDA) and/or Conformité Européenne (CE) marked | Device | Wearable device users |
| Cardiopulmonary resuscitationCardiopulmonary resuscitation | Other | Patients with cardiac arrest |
| CT scan, TEE exam, or chest X rayChest CT scan, transesophageal echocardiogram (TEE) scans, or chest X ray | Other | Patients who received a CT scan, Patients with cardiac arrest |
Primary outcome
Survival
Time frame: Hospital discharge (4 weeks for hospital admission)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Alberto Zangrillo, MD principal investigator
IRCCS Ospedale San Raffaele
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Out-Of-Hospital Cardiac Arrest and Cardiac Arrest, run by Università Vita-Salute San Raffaele at 3 sites in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 11 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Giovanni Landoni, MD (+390226436151, landoni.giovanni@hsr.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Survival (time frame: Hospital discharge (4 weeks for hospital admission)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2025.