Predict Arrhythmia Risk Using Intelligent Software

NCT07792694 | Recruiting

• 2 other identifiers: nWMO-2025.186project code 24PPS046
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Ventricular Tachycardia; Ventricular Fibrillation; Acute myocardial infarction; Electrocardiography; Machine Learning; Artificial Intelligence; Deep Learning; Convolutional Neural Network; CNN; Prediction model

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (5)

• Secondary outcome measure

  During admission

• Secondary Outcome Measure

  during admission

• Secondary outcome measure

  During admission

• Secondary outcome measure

  During admission

• Performance of AI prediction model

  During admission

Study Arms (1)

Adult patients admitted for acute cardiac illness/elective cardiac procedures on ECG monitoring

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients aged 18 years or older admitted to the Catharina hospital Eindhoven (CZE) with cardiac diseases from 1/1/2023.

You may qualify if:

• 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.

You may not qualify if:

• \- 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.

Sponsors & Collaborators

• Catharina Ziekenhuis Eindhoven: lead

Study Sites (1)

Catharina Hospital Eindhoven

Eindhoven, North Brabant, 5623 EJ, Netherlands

RECRUITING

MeSH Terms

Conditions

Arrhythmias, Cardiac; Tachycardia, Ventricular; Ventricular Fibrillation

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Tachycardia; Cardiac Conduction System Disease

Study Officials

• Luuk C Otterspoor, Dr. M.D.

  Catharina Ziekenhuis Eindhoven

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Maud E Kortman, M.D.

CONTACT

040 239 9111; maud.kortman@catharinaziekenhuis.nl

Luuk C Otterspoor, Dr. M.D.

CONTACT

luuk.otterspoor@catharinaziekenhuis.nl

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, Cardiologist-Intensivist, Dr.

Study Record Dates

• First Submitted: August 12, 2026
• First Posted: August 28, 2026
• Study Start: January 1, 2023
• Primary Completion (Estimated): April 1, 2029
• Study Completion (Estimated): April 1, 2029
• Last Updated: August 28, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)