ClinicalTrials.gg
RecruitingNCT07792694PARISUpdated Aug 28, 2026

Predict Arrhythmia Risk Using Intelligent Software

An observational study in Cardiac Arrhythmias, Acute Myocardial Infarction (AMI) and Ventricular Tachycardia (VT), sponsored by Catharina Ziekenhuis Eindhoven. Recruiting at 1 site in Netherlands. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-08-28.

Sponsored by Catharina Ziekenhuis Eindhoven · Observational

From the registry’s dates

  • Started Jan 2023; still recruiting 3 years 10 months later.
Study type
Observational
Model
Cohort
Time perspective
Retrospective
Enrollment
3,000
Ages
18 Years and older
Sex
All
01

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.

Read the detailed description

Primary objective:

Assessment of the performances of AI models in identifying patients at risk of sustained VT and VF from bedside monitor ECG in different timeframes:

  • 30-minute prediction model
  • 1-day prediction model

Secondary objectives:

  • 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

Exploratory objectives:

  • Real time and continuous detection of events for alarming
  • Prediction of other types of arrhythmias (e.g., Atrial fibrillation (AF), atrioventricular block, severe brady-arrhythmia) using in hospital ECG monitoring.
  • Identification of clinical risk factors for sustained VT and/or VF
  • Exploration of development and assessment of new AI models using new (clinical) input
02

Conditions studied

  • 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
03

In context

Arrhythmias, Cardiac

1,051 studies on the registry are indexed under Arrhythmias, Cardiac; 236 are open to participants now.

This study's planned enrollment of 3,000 is above the median of 200 across 467 observational studies indexed under Arrhythmias, Cardiac.

Browse Arrhythmias, Cardiac studies →

Lead sponsor

Catharina Ziekenhuis Eindhoven is the lead sponsor of 85 studies on the registry; 16 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
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.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • 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 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 criteria

Exclusion Criteria:

- 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.

Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

ECG (electrocardiogram)

A recording of the heart’s electrical activity, taken with stickers on the chest, arms and legs. It takes a few minutes and does not hurt.

05

Study design

Observational model
Cohort
Time perspective
Retrospective
Enrollment
3,000 participants (estimated)
Patient registry
No

Groups and cohorts

  • Adult patients admitted for acute cardiac illness/elective cardiac procedures on ECG monitoring
06

What researchers measure

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)

    Time frame: During admission

Secondary outcomes

  1. Secondary outcome measure

    \- A 'textbook' outcome (no adverse events) (Binary outcome measure 0 = no event during admission, 1 = event during admission)

    Time frame: During admission

  2. 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)

    Time frame: during admission

  3. Secondary outcome measure

    \- In hospital death/cardiovascular in-hospital death (include cause if available) (Binary outcome measure 0 = no event during admission, 1 = event during admission)

    Time frame: During admission

  4. Secondary outcome measure

    \- Pulseless electrical activity (PEA) and asystole (Binary outcome measure 0 = no event during admission, 1 = event during admission)

    Time frame: During admission

  5. 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

    Time frame: During admission

07

Study locations

1 of 1 sites recruiting
08

References and documents

Individual participant data

Plan to share: No — Sensitive patient information, no permission to share outside of hospital

No publications or documents are linked to this record.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reached

Started Jan 2023 (actual). Primary completion is estimated for Apr 2029, 75 months after the start.

How these figures are worked out, and which studies are left out of them.

Registered
Aug 12, 2026
First posted
Aug 28, 2026
StartActual
Jan 1, 2023
Primary completionEst.
Apr 1, 2029
CompletionEst.
Apr 1, 2029
Last update
Aug 28, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Aug 28, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07792694
Lead sponsor
Catharina Ziekenhuis Eindhoven
Responsible party
Luuk Otterspoor (Principal Investigator, Cardiologist-Intensivist, Dr., Catharina Ziekenhuis Eindhoven) — Principal investigator
First posted
Aug 28, 2026
Start date
Jan 1, 2023
Primary completion
Apr 1, 2029 (estimated)
Completion
Apr 1, 2029 (estimated)
Last update
Aug 28, 2026

Study contacts

Maud E Kortman, M.D.
Contact
maud.kortman@catharinaziekenhuis.nl
040 239 9111
Luuk C Otterspoor, Dr. M.D.
Contact
luuk.otterspoor@catharinaziekenhuis.nl
Luuk C Otterspoor, Dr. M.D.
principal investigator · Catharina Ziekenhuis Eindhoven

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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