Interactive Patient's Assistant to Optimize Long-term Care in Patients With Chronic Health Failure and Determine Which Parameters of CIED Indicate the Requirement for Ambulatory Follow-up

Patients with chronic heart failure (CHF) and reduced left ventricle ejection fraction benefit from cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillator (ICD). Currently used devices, besides delivering low and high-energy therapies, record patient's activity and many hemodynamic parameters. However, increasing numbers of patient with CRT and ICD devices produce overload of cardiology centers where patients are admitted to ambulatory visits. Contrarily, there are technological possibilities for remote monitoring proven to be effective in recognizing damage of the implanted device and risk of exacerbation of chronic heart failure. Patients' registries show that majority of ambulatory visits are unproductive and do not result in significant modification of device's parameters. Promising experiences with application of data mining and machine learning techniques allow us to assume probable benefits from using modern methods of data analysis in determination of requirement for ambulatory follow-up on basis of data gathered through telemonitoring and clinical assessment of a patient.

Aim of the study is to find multivariate model predicting the requirement for ambulatory follow-up of IECD.