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.

Medicine
|October 4, 2018
PubMed

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.

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