[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100653857":3},{"organization":4,"outcomesModule":7,"designInfo":29,"detailedDescription":32,"studyPopulation":33,"armGroups":34,"interventions":28,"overallOfficials":37,"centralContacts":41,"locations":49,"responsibleParty":67,"collaborators":28,"id":70,"slug":71,"hasResults":72,"nctId":73,"briefTitle":74,"officialTitle":75,"acronym":76,"eligibilityCriteria":77,"healthyVolunteers":72,"sex":78,"minAge":79,"maxAge":28,"enrollmentInfo":80,"targetDuration":28,"studyType":83,"phases":28,"briefSummary":84,"conditions":85,"keywords":90,"overallStatus":52,"whyStopped":28,"lastUpdateSubmitDate":100,"lastUpdatePostDateStruct":101,"startDateStruct":104,"completionDateStruct":106,"leadSponsor":108,"locationsCount":109},{"fullName":5,"class":6},"Catharina Ziekenhuis Eindhoven","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":28},[9],{"measure":10,"description":11,"timeFrame":12},"Occurrence of sustained ventricular tachycardia or ventricular fibrillation","The primary outcome of the study is the occurrence of sustained ventricular tachycardia (VT) (monomorphic and polymorphic with a heartrate \\> 100 bpm and duration \\> 30 seconds or with hemodynamic compromise such as fainting or need for resuscitation) or ventricular fibrillation. (Binary outcome measure 0 = no event during admission, 1 = event during admission)","During admission",[14,17,21,23,25],{"measure":15,"description":16,"timeFrame":12},"Secondary outcome measure","\\- A 'textbook' outcome (no adverse events) (Binary outcome measure 0 = no event during admission, 1 = event during admission)",{"measure":18,"description":19,"timeFrame":20},"Secondary Outcome Measure","\\- In-hospital onset and offset of cardiac arrhythmias (e.g. atrial fibrillation, atrio-ventricular block or severe tachy- or bradyarrhythmia, non-sustained VT). (Binary outcome measure 0 = no event during admission, 1 = event during admission)","during admission",{"measure":15,"description":22,"timeFrame":12},"\\- In hospital death\u002Fcardiovascular in-hospital death (include cause if available) (Binary outcome measure 0 = no event during admission, 1 = event during admission)",{"measure":15,"description":24,"timeFrame":12},"\\- Pulseless electrical activity (PEA) and asystole (Binary outcome measure 0 = no event during admission, 1 = event during admission)",{"measure":26,"description":27,"timeFrame":12},"Performance of AI prediction model","Discrimination of AI prediction model expressed with Area Under the Receiver Operating Characteristic curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), sensitivity, specificity, (Positive Predictive Value) PPV and (Negative Predictive Value) NPV",null,{"allocation":28,"interventionModel":28,"interventionModelDescription":28,"primaryPurpose":28,"observationalModel":30,"timePerspective":31,"maskingInfo":28},"COHORT","RETROSPECTIVE","Primary objective:\n\nAssessment of the performances of AI models in identifying patients at risk of sustained VT and VF from bedside monitor ECG in different timeframes:\n\n* 30-minute prediction model\n* 1-day prediction model\n\nSecondary objectives:\n\n• Assessment of potential healthcare savings if the AI model in would be used in clinical practice, such as CCU length-of-stay (CCU-LOS), hospital length-of-stay and associated costs\n\nExploratory objectives:\n\n* Real time and continuous detection of events for alarming\n* Prediction of other types of arrhythmias (e.g., Atrial fibrillation (AF), atrioventricular block, severe brady-arrhythmia) using in hospital ECG monitoring.\n* Identification of clinical risk factors for sustained VT and\u002For VF\n* Exploration of development and assessment of new AI models using new (clinical) input","Patients aged 18 years or older admitted to the Catharina hospital Eindhoven (CZE) with cardiac diseases from 1\u002F1\u002F2023.",[35],{"label":36,"type":28,"description":28,"interventionNames":28},"Adult patients admitted for acute cardiac illness\u002Felective cardiac procedures on ECG monitoring",[38],{"name":39,"affiliation":5,"role":40},"Luuk C Otterspoor, Dr. M.D.","PRINCIPAL_INVESTIGATOR",[42,47],{"name":43,"role":44,"phone":45,"phoneExt":28,"email":46},"Maud E Kortman, M.D.","CONTACT","040 239 9111","maud.kortman@catharinaziekenhuis.nl",{"name":39,"role":44,"phone":28,"phoneExt":28,"email":48},"luuk.otterspoor@catharinaziekenhuis.nl",[50],{"facility":51,"status":52,"city":53,"state":54,"zip":55,"country":56,"countryCode":57,"cosmosGeoPoint":58,"geoPoint":63,"contacts":64},"Catharina Hospital Eindhoven","RECRUITING","Eindhoven","North Brabant","5623 EJ","Netherlands","NL",{"type":59,"coordinates":60},"Point",[61,62],5.47778,51.44083,{"lat":62,"lon":61},[65,66],{"name":43,"role":44,"phone":45,"phoneExt":28,"email":46},{"name":39,"role":40,"phone":28,"phoneExt":28,"email":28},{"type":40,"investigatorFullName":68,"investigatorTitle":69,"investigatorAffiliation":5,"oldNameTitle":28,"oldOrganization":28},"Luuk Otterspoor","Principal Investigator, Cardiologist-Intensivist, Dr.","100653857","predict-arrhythmia-risk-using-intelligent-software-100653857",false,"NCT07792694","Predict Arrhythmia Risk Using Intelligent Software","Artificial Intelligence-based Prediction and Detection of Critical Arrhythmias in Acute Cardiac Illness.","PARIS","Inclusion criteria:\n\n* Patients admitted from 1\u002F1\u002F2023\\*\n* Patients aged 18 years or older\n* Admitted for acute cardiac illness or after elective cardiac procedures\n* Who are on ECG monitoring in the CCU, ICU or ward\n* Patients for whom continuous waveform ECG data have been routinely stored.\n\n  * Continuous waveform ECG data has been routinely stored in the CZE since 1\u002F1\u002F2023 on the ICU, since 1\u002F12\u002F2025 on the CCU and on the ward it has yet to be implemented. As our project utilizes this continuous ECG data, it will only include patients for whom this data is available.\n\nExclusion Criteria:\n\n\\- Patients who expressed their preference for not having their data used for scientific research or to improve quality of care in the opt-out program of the CZE.","ALL","18 Years",{"count":81,"type":82},3000,"ESTIMATED","OBSERVATIONAL","Cardiac arrhythmias frequently occur in patients admitted to the Coronary Care Unit (CCU). The majority of these patients are treated for an acute myocardial infarction, which carries an increased risk of life-threatening arrhythmias such as ventricular tachycardia (VT) or ventricular fibrillation (VF). This risk is one of the reasons these patients are monitored for 48 hours after a myocardial infarction, in accordance with the guidelines of the European Society of Cardiology (ESC) for acute coronary syndrome. Other arrhythmias, such as asystole, atrial fibrillation, or atrioventricular block, also occur in CCU patients. These arrhythmias are recorded on the electrocardiogram (ECG) monitor in the CCU and trigger an alarm for healthcare staff. However, in order to apply this alarming with sufficient sensitivity, many false positive alarms are also produced, which increases the workload for nurses (alarm fatigue) and undermines patient well-being.\n\nThis study will investigate whether Artificial Intelligence (AI) models, using continuous ECG data, can help improve the prediction of patients at risk of a life-threatening cardiac arrhythmia. Firstly, this study will aim to predict patients at risk of VT\u002FVF in both the short term (30 minutes) and long term (1 day) in patients under continuous telemetric monitoring. This prediction facilitates timely intervention by the team in the short term, and in the long term, the safe transfer of a patient to a lower-complexity ward or earlier safe discharge of a patient. Secondly, this study will aim for improved detection to reduce the number of false negative alarms and thereby reduce alarm fatigue.\n\nThe performance of these AI models can be evaluated through this retrospective observational study. Patients aged 18 years or older who have been admitted with acute cardiac disease will be included. The primary objective of this study will be to evaluate the performance of AI models that detect and predict critical arrhythmias in the short and long term, using ECG data obtained via the monitoring system.",[86,87,88,89],"Cardiac Arrhythmias","Acute Myocardial Infarction (AMI)","Ventricular Tachycardia (VT)","Ventricular Fibrillation",[91,89,92,93,94,95,96,97,98,99],"Ventricular Tachycardia","Acute myocardial infarction","Electrocardiography","Machine Learning","Artificial Intelligence","Deep Learning","Convolutional Neural Network","CNN","Prediction model","2026-08-26",{"date":102,"type":103},"2026-08-28","ACTUAL",{"date":105,"type":103},"2023-01-01",{"date":107,"type":82},"2029-04-01",{"name":5,"class":6},1]