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Predict Arrhythmia Risk Using Intelligent Software

NCT07792694 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2026-08-28

No results posted yet for this study

Summary

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.

This 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/VF 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.

The 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.

Conditions

  • Cardiac Arrhythmias
  • Acute Myocardial Infarction (AMI)
  • Ventricular Tachycardia (VT)
  • Ventricular Fibrillation

Sponsors & Collaborators

  • Catharina Ziekenhuis Eindhoven

    lead OTHER

Principal Investigators

  • Luuk C Otterspoor, Dr. M.D. · Catharina Ziekenhuis Eindhoven

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-01
Primary Completion
2029-04-01
Completion
2029-04-01

Countries

  • Netherlands

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07792694 on ClinicalTrials.gov