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An interactive assistant for patients with cardiac implantable electronic devices: A study protocol of the LUCY trial
Joanna Michalik1, Andrzej Cacko1, Jakub Poliński1
1Department of Medical Informatics and Telemedicine.
Insights
This study introduces a multivariate model to predict necessary ambulatory follow-ups for patients with cardiac implantable electronic devices (CIEDs). The goal is to optimize cardiology center resources by identifying patients needing closer monitoring.
Area of Science:
- Cardiology
- Biomedical Engineering
- Health Informatics
Background:
- Patients with chronic heart failure (CHF) and reduced ejection fraction benefit from cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillators (ICDs).
- The increasing number of patients with these devices leads to an overload of cardiology centers and ambulatory visits.
- Efficient patient management is crucial for optimizing healthcare resources.
Purpose of the Study:
- To develop a multivariate model for predicting the need for ambulatory follow-up in patients with cardiac implantable electronic devices (CIEDs).
- To optimize resource allocation in cardiology centers by identifying patients requiring closer monitoring.
- To explore the application of machine learning and artificial intelligence in healthcare for CIED patient management.
Main Methods:
- The LUCY study is a prospective, observational, cohort trial including 600 patients (aged 18-90) with CHF (NYHA classes I-III) and implanted ICD or CRT.
- Patients undergo standard ambulatory visits, with primary endpoints focusing on clinically indicated visits due to patient condition or device malfunction.
- Secondary endpoints include visits triggered by remote monitoring alerts (e.g., Medtronic CareLink Express) for arrhythmias or device status.
Main Results:
- The study aims to identify key predictors for ambulatory follow-up requirements in CIED patients.
- The development of a predictive model will aid in stratifying patient needs for follow-up.
- This research pioneers the use of AI and machine learning for optimizing CIED patient care.
Conclusions:
- A predictive model for ambulatory follow-up needs in CIED patients can help manage cardiology center workloads.
- Implementing AI and machine learning in healthcare can enhance the efficiency of managing patients with CIEDs.
- The LUCY study lays the groundwork for future advancements in remote patient monitoring and personalized healthcare.
Abstract:
Patients with chronic heart failure (CHF) and reduced left ventricle ejection fraction benefit from cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillator (ICD). However, increasing numbers of patient with CRT and ICD devices produce overload of cardiology centers where patients are admitted to ambulatory visits. This study aims to find multivariate model predicting the requirement for ambulatory follow-up of cardiac implantable electronic devices (CIEDs).The LUCY study is an observational, cohort, prospective, 2-stage trial. As equal number of patients (300) will be included in the first and the second part of the study, finally, 600 patients will be included in the study. The inclusion criteria will be: age between 18 and 90 years, CHF (New York Heart Association classes I-III) and implanted ICD or CRT at least 30 days before study inclusion. The exclusion criteria will be dementia and other conditions impeding cooperation during the study. All patients included in the study will undergo standard ambulatory visit. Primary endpoint will be defined as any ambulatory visit qualified as necessary due to patient's condition or device malfunction diagnose by the cardiologist: any change in pharmacotherapy related to patient's clinical status assessed during the visit, any change in tachyarrythmia counter or discriminator status, any change in tachyarrythmia threshold, presence of ventricular undersensing or oversensing, presence of atrial or ventricular ineffective pacing, or device's pocket infection. Secondary endpoint will be defined as any ambulatory visit qualified as necessary due to the alarm identified via Medtronic CareLink Express (MCLE): sustained or treated ventricular tachyarrythmia, any not previously diagnosed supraventricular tachyarrythmia, or elective replacement indicator.Our study is the first attempt of implementation of the machine learning and elements artificial intelligence in health care optimization of patients with CIED. The LUCY will be an open product, available for additional testing and improvement with supplementary functionalities: quality of life assessment, teleconsultation, video-streaming, automated imagine recognizing.
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