Cardiac ArrhythmiasPARISNCT07792694
Predict Arrhythmia Risk Using Intelligent Software
An observational study of Cardiac Arrhythmias and Acute Myocardial Infarction (AMI), run by Catharina Ziekenhuis Eindhoven at 1 site in Netherlands.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiac Arrhythmias, Acute Myocardial Infarction (AMI), Ventricular Tachycardia (VT)
- How many people
- 3,000 participants
- Where
- 1 siteNetherlands
- Study dates
- 1 Jan 2023to 1 Apr 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients aged 18 years or older admitted to the Catharina hospital Eindhoven (CZE) with cardiac diseases from 1/1/2023.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Luuk C Otterspoor, Dr. M.D. contact
Netherlands1 site, 1 recruiting
- Recruiting
Catharina Hospital Eindhoven
Eindhoven, North Brabant, 5623 EJ
Maud E Kortman, M.D., 040 239 9111, maud.kortman@catharinaziekenhuis.nl
Luuk C Otterspoor, Dr. M.D.
About this study
Official title: 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.
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
- 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
- Medical subject headings
- Arrhythmias, Cardiac, Tachycardia, Ventricular, Ventricular Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adult patients admitted for acute cardiac illness/elective cardiac procedures on ECG monitoring | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: During admission
Secondary outcomes (5)
Secondary outcome measure
- A 'textbook' outcome (no adverse events) (Binary outcome measure 0 = no event during admission, 1 = event during admission)
Measured: During admission
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)
Measured: during admission
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)
Measured: During admission
Secondary outcome measure
- Pulseless electrical activity (PEA) and asystole (Binary outcome measure 0 = no event during admission, 1 = event during admission)
Measured: During admission
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
Measured: During admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Aug 2026
- Study start1 Jan 2023
- Primary completion1 Apr 2029 (estimated)
- Study completion1 Apr 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac ArrhythmiasStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Catharina Ziekenhuis Eindhoven
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Luuk Otterspoor, Catharina Ziekenhuis Eindhoven
- Investigators
- Luuk C Otterspoor, Dr. M.D., Principal investigator, Catharina Ziekenhuis Eindhoven
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07792694
- Sponsor's protocol ID
- nWMO-2025.186
- Other IDs
- project code 24PPS046
- Acronym
- PARIS
- Status last verified
- Aug 2026
- First submitted
- 12 Aug 2026
- Last updated
- 28 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Sensitive patient information, no permission to share outside of hospital
Study at a glance
From the registry record, last updated by the sponsor on 28 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Prediction and Detection of Critical Arrhythmias in Acute Cardiac Illness.
- ClinicalTrials.gov ID
- NCT07792694
- Acronym / sponsor ID
- PARIS / nWMO-2025.186
- 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
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Catharina Ziekenhuis EindhovenUniversity, hospital or other
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 1 Apr 2029 (estimated)
- Study completion
- 1 Apr 2029 (estimated)
- First posted
- 28 Aug 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult patients admitted for acute cardiac illness/elective cardiac procedures on ECG monitoring | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients aged 18 years or older admitted to the Catharina hospital Eindhoven (CZE) with cardiac diseases from 1/1/2023.
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Occurrence of sustained ventricular tachycardia or ventricular fibrillation
Time frame: During admission
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)
5 secondary outcomes
Secondary outcome measure
Time frame: During admission
Secondary Outcome Measure
Time frame: during admission
Secondary outcome measure
Time frame: During admission
Secondary outcome measure
Time frame: During admission
Performance of AI prediction model
Time frame: During admission
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Aug 2026.
- Individual participant dataSharing planned: No. Sensitive patient information, no permission to share outside of hospital
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Luuk C Otterspoor, Dr. M.D. contact
Luuk C Otterspoor, Dr. M.D. principal investigator
Catharina Ziekenhuis Eindhoven
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiac Arrhythmias and Acute Myocardial Infarction (AMI), run by Catharina Ziekenhuis Eindhoven at 1 site in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 28 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Maud E Kortman, M.D. (040 239 9111, maud.kortman@catharinaziekenhuis.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Occurrence of sustained ventricular tachycardia or ventricular fibrillation (time frame: During admission). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Aug 2026.