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Predict Arrhythmia Risk Using Intelligent Software (PARIS)

2026년 8월 26일 업데이트: 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.

연구 개요

상태

모병

정황

상세 설명

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

연구 유형

관찰

등록 (추정된)

3000

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

이름: Maud E Kortman, M.D.
전화번호: 040 239 9111
이메일: maud.kortman@catharinaziekenhuis.nl

연구 연락처 백업

이름: Luuk C Otterspoor, Dr. M.D.
이메일: luuk.otterspoor@catharinaziekenhuis.nl

연구 장소

네덜란드
- North Brabant
  - Eindhoven, North Brabant, 네덜란드, 5623 EJ
    - 모병
    - Catharina Hospital Eindhoven
    - 연락하다:
      
      Maud E Kortman, M.D.
      
      전화번호: 040 239 9111
      
      이메일: maud.kortman@catharinaziekenhuis.nl
    - 수석 연구원:
      
      Luuk C Otterspoor, Dr. M.D.

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

성인
고령자

건강한 자원 봉사자를 받아들입니다

아니

샘플링 방법

비확률 샘플

연구 인구

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

설명

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.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

그룹/코호트 수

코호트 및 개입

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

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정	측정값 설명	기간
Occurrence of sustained ventricular tachycardia or ventricular fibrillation 기간: 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

2차 결과 측정

결과 측정	측정값 설명	기간
Secondary outcome measure 기간: 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 기간: 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 기간: 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 기간: 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 기간: 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

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Catharina Ziekenhuis Eindhoven

수사관

수석 연구원: Luuk C Otterspoor, Dr. M.D., Catharina Ziekenhuis Eindhoven

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2023년 1월 1일

기본 완료 (추정된)

2029년 4월 1일

연구 완료 (추정된)

2029년 4월 1일

연구 등록 날짜

최초 제출

2026년 8월 12일

QC 기준을 충족하는 최초 제출

2026년 8월 26일

처음 게시됨 (실제)

2026년 8월 28일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)