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Predict Arrhythmia Risk Using Intelligent Software (PARIS)

26 agosto 2026 aggiornato da: Luuk Otterspoor, Catharina Ziekenhuis Eindhoven

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.

Panoramica dello studio

Stato

Reclutamento

Condizioni

Descrizione dettagliata

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

Tipo di studio

Osservativo

Iscrizione (Stimato)

3000

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Contatto studio

Nome: Maud E Kortman, M.D.
Numero di telefono: 040 239 9111
Email: maud.kortman@catharinaziekenhuis.nl

Backup dei contatti dello studio

Nome: Luuk C Otterspoor, Dr. M.D.
Email: luuk.otterspoor@catharinaziekenhuis.nl

Luoghi di studio

Olanda
- North Brabant
  - Eindhoven, North Brabant, Olanda, 5623 EJ
    - Reclutamento
    - Catharina Hospital Eindhoven
    - Contatto:
      
      Maud E Kortman, M.D.
      
      Numero di telefono: 040 239 9111
      
      Email: maud.kortman@catharinaziekenhuis.nl
    - Investigatore principale:
      
      Luuk C Otterspoor, Dr. M.D.

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

Adulto
Adulto più anziano

Accetta volontari sani

Metodo di campionamento

Campione non probabilistico

Popolazione di studio

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

Descrizione

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 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 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.

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Numero di gruppi/coorti

Coorti e interventi

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

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato	Misura Descrizione	Lasso di tempo
Occurrence of sustained ventricular tachycardia or ventricular fibrillation Lasso di tempo: 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)	During admission

Misure di risultato secondarie

Misura del risultato	Misura Descrizione	Lasso di tempo
Secondary outcome measure Lasso di tempo: During admission	- A 'textbook' outcome (no adverse events) (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Secondary Outcome Measure Lasso di tempo: during admission	- 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)	during admission
Secondary outcome measure Lasso di tempo: During admission	- In hospital death/cardiovascular in-hospital death (include cause if available) (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Secondary outcome measure Lasso di tempo: During admission	- Pulseless electrical activity (PEA) and asystole (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Performance of AI prediction model Lasso di tempo: During admission	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	During admission

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

Catharina Ziekenhuis Eindhoven

Investigatori

Investigatore principale: Luuk C Otterspoor, Dr. M.D., Catharina Ziekenhuis Eindhoven

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Effettivo)

1 gennaio 2023

Completamento primario (Stimato)

1 aprile 2029

Completamento dello studio (Stimato)

1 aprile 2029

Date di iscrizione allo studio

Primo inviato

12 agosto 2026

Primo inviato che soddisfa i criteri di controllo qualità

26 agosto 2026

Primo Inserito (Effettivo)

28 agosto 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)