[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100557441":3},{"organization":4,"armGroups":7,"interventions":24,"overallOfficials":37,"centralContacts":42,"locations":51,"responsibleParty":89,"collaborators":92,"id":101,"slug":102,"hasResults":103,"nctId":104,"briefTitle":105,"officialTitle":106,"acronym":107,"eligibilityCriteria":108,"healthyVolunteers":109,"sex":110,"minAge":111,"maxAge":10,"enrollmentInfo":112,"targetDuration":10,"studyType":115,"phases":10,"briefSummary":116,"conditions":117,"keywords":120,"overallStatus":53,"whyStopped":10,"lastUpdateSubmitDate":125,"lastUpdatePostDateStruct":126,"startDateStruct":129,"completionDateStruct":131,"leadSponsor":133,"locationsCount":134},{"fullName":5,"class":6},"Università Vita-Salute San Raffaele","OTHER",[8,14,20],{"label":9,"type":10,"description":11,"interventionNames":12},"Wearable device users",null,"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.",[13],"Device: Wearable device",{"label":15,"type":10,"description":16,"interventionNames":17},"Patients with cardiac arrest","Adults resuscitated after cardiac arrest or during ongoing cardiopulmonary resuscitation (CPR).",[18,19],"Other: Cardiopulmonary resuscitation","Other: CT scan, TEE exam, or chest X ray",{"label":21,"type":10,"description":22,"interventionNames":23},"Patients who received a CT scan","Adults who received a chest CT scan for any reasons.",[19],[25,30,33],{"type":26,"name":27,"description":28,"armGroupLabels":29,"otherNames":10},"DEVICE","Wearable device","Wearable devices that are preferentially Food and Drug Administration (FDA) and\u002For Conformité Européenne (CE) marked",[9],{"type":6,"name":31,"description":31,"armGroupLabels":32,"otherNames":10},"Cardiopulmonary resuscitation",[15],{"type":6,"name":34,"description":35,"armGroupLabels":36,"otherNames":10},"CT scan, TEE exam, or chest X ray","Chest CT scan, transesophageal echocardiogram (TEE) scans, or chest X ray",[21,15],[38],{"name":39,"affiliation":40,"role":41},"Alberto Zangrillo, MD","IRCCS Ospedale San Raffaele","PRINCIPAL_INVESTIGATOR",[43,48],{"name":44,"role":45,"phone":46,"phoneExt":10,"email":47},"Giovanni Landoni, MD","CONTACT","+390226436151","landoni.giovanni@hsr.it",{"name":49,"role":45,"phone":46,"phoneExt":10,"email":50},"Tommaso Scquizzato, MD","scquizzato.tommaso@hsr.it",[52,70,80],{"facility":40,"status":53,"city":54,"state":10,"zip":55,"country":56,"countryCode":57,"cosmosGeoPoint":58,"geoPoint":63,"contacts":64},"RECRUITING","Milan","20132","Italy","IT",{"type":59,"coordinates":60},"Point",[61,62],9.18951,45.46427,{"lat":62,"lon":61},[65],{"name":66,"role":45,"phone":67,"phoneExt":68,"email":69},"Tommaso Squizzato, MD","0226438296","+39","squizzato.tommaso@hsr.it",{"facility":71,"status":72,"city":73,"state":10,"zip":74,"country":56,"countryCode":57,"cosmosGeoPoint":75,"geoPoint":79,"contacts":10},"AOU Policlinico Federico II","NOT_YET_RECRUITING","Naples","80100",{"type":59,"coordinates":76},[77,78],14.26811,40.85216,{"lat":78,"lon":77},{"facility":81,"status":72,"city":73,"state":10,"zip":82,"country":56,"countryCode":57,"cosmosGeoPoint":83,"geoPoint":85,"contacts":86},"Azienda Ospedaliera Universitaria Vanvitelli","80138",{"type":59,"coordinates":84},[77,78],{"lat":78,"lon":77},[87],{"name":88,"role":45,"phone":10,"phoneExt":10,"email":10},"Maria Caterina Pace, MD",{"type":41,"investigatorFullName":90,"investigatorTitle":91,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Giovanni Landoni","Prof",[93,94,96,99],{"name":40,"class":6},{"name":95,"class":6},"Politecnico di Milano",{"name":97,"class":98},"Azienda Ospedaliera Universitaria \"Luigi Vanvitelli\" (AOV)","UNKNOWN",{"name":100,"class":98},"Azienda Ospedaliera Universitaria Federico II (AOU Federico II)","100557441","technological-and-patient-tailored-innovations-for-maximizing-effectiveness-of-cardiac-arrest-resuscitation-100557441",false,"NCT06538155","Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation","Technological and Patient-tailored Innovations for Maximizing Effectiveness of Cardiac Arrest Resuscitation: the TIME-CARE Project","TIME-CARE","AIM 1: PREDICTION AND RECOGNITION OF CARDIAC ARREST AIM 1.1: PREDICT A MAJOR CARDIOVASCULAR EVENT\n\nInclusion criteria:\n\n* Age 18-70 years;\n* 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);\n* Users of a smartwatch or smartphone that continuously and automatically collect health data;\n* Informed consent.\n\nExclusion criteria:\n\n* Impossibility to access\u002Fexport data;\n* User did not wear the wearable device for periods longer than 24 hours;\n* User did not wear the wearable device in the 4 weeks preceding the event.\n\nAIM 1.2 CARDIAC ARREST DETECTION FROM VIDEOS No patient involved.\n\nAIM 2: TECHNOLOGIES TO INCREASE CPR AND DEFIBRILLATION USE BEFORE AMBULANCE ARRIVAL No patient involved.\n\nAIM 3: PATIENT-TAILORED RESUSCITATION AIM 3.1: CLINICAL STUDY IN PATIENTS WHO RECEIVED CPR\n\nInclusion criteria:\n\n* Adults (≥ 18 years);\n* Patients suffering a non-traumatic cardiac arrest treated with chest compressions (both survivors and non-survivors);\n* Received a TEE, chest x-ray, or chest CT scan as the standard clinical assessment following cardiac arrest;\n* Informed consent.\n\nExclusion criteria:\n\n\\- Patients with severe thorax\u002Fmediastinal deformity.\n\nAIM 3.2 CLINICAL STUDY IN PATIENTS WHO RECEIVED A CHEST CT SCAN\n\nInclusion criteria:\n\n* Adults (≥ 18 years);\n* Received a chest CT scan for any reasons;\n* Informed consent.\n\nExclusion criteria:\n\n\\- Patients with severe thorax\u002Fmediastinal deformity.\n\nAIM 3.3 MACHINE LEARNING (ML) ALGORITHM No patient involved.",true,"ALL","18 Years",{"count":113,"type":114},500,"ESTIMATED","OBSERVATIONAL","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.\n\nTo 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.\n\nFirst, 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.\n\nSecond, 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.\n\nThird, 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.\n\nOur 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.",[118,119],"Out-Of-Hospital Cardiac Arrest","Cardiac Arrest",[121,122,123,124],"out-of-hospital cardiac arrest","cardiac arrest","cardiopulmonary resuscitation","extracorporeal cardiopulmonary resuscitation","2025-08-05",{"date":127,"type":128},"2025-08-06","ACTUAL",{"date":130,"type":128},"2025-01-27",{"date":132,"type":114},"2026-08-31",{"name":5,"class":6},3]