Predict Arrhythmia Risk Using Intelligent Software

Trial details

Who runs it

Catharina Ziekenhuis Eindhoven

Trial code

NCT07792694

Record updated

28 August 2026

Original description

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.Read full original descriptionShow less

Artificial Intelligence-based Prediction and Detection of Critical Arrhythmias in Acute Cardiac Illness.

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.

Inclusion: Patients admitted from 1/1/2023*; Patients aged 18 years or older; Admitted for acute cardiac illness or after elective cardiac procedures; Who are on ECG monitoring in the CCU, ICU or ward; Patients for whom continuous waveform ECG data have been routinely stored; Continuous waveform ECG data has been routinely stored in the CZE since 1/1/2023 on the ICU, since 1/12/2025 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.

Exclusion: 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.

ClinicalTrials.gov NCT07792694