ECG Algorithms for CRT Response Evaluation
Condition: ECG Monitoring
Study type: Observational

Summary: 
      Cardiovascular diseases (CVD) are associated with high healthcare costs,as well as are a
      leading cause of mortality and hospitalizations. One of CVDs is a heart failure which may be
      associated with dyssynchrony of contraction of right and left ventricle. Chance for group of
      patients whose pharmacotherapy is not enough is cardiac resynchronisation therapy (CRT).
      Effectiveness of CRT has been proven in various multicenter clinical studies. The challenge
      limiting CRT usage is it relative low effectiveness - with significant group of patients that
      do not respond to this method of therapy. The device itself does not always show the true
      level of stimulation during interrogation; then invalid functioning is often not detected,
      which presents a real danger to patient's health and life. The main challenge for today's
      researchers is to develop new technologies, which may help to improve diagnosis of CVD,
      thereby reducing healthcare costs and quality of patients' lives. Smart computed systems of
      ECG analysis and interpretation offer new capabilities for the diagnosis and management of
      patients with CRT. Several reports with intelligent machine-based learning algorithms have
      been published, in which achieved very positive results in detecting various ECG
      abnormalities. Aim of our study is to show utility of ECG interpretation software in patients
      with CRT to assess the CRT response using Cardiomatics system.
    

Detailed description: 
      The study is an investigator-initiated, single centre, prospective observational trial. The
      study will be carried out in university hospital on electrocardiology ward. The study will
      consist of two independent groups of patients whose ECG will be collected using the standard
      12-lead ECG or 24-hour Holter monitoring. The study groups will be as follows: cardiac
      resynchronization therapy (CRT) recipients with pacemaker or defibrillation function,
      patients after cardiac implantable electronic devices (CIED) such as : cardiac pacemaker,
      patients with implantable cardioverter defibrillator (ICD) with indications for periodic
      heart stimulation. Approval for all study groups was obtained from institutional review
      board. In patients with already implanted device signal will be recorded in pacing mode and
      standby mode. What is more, in patients with CRT-D/CRT-P signal will be registered with
      different configurations of stimulation (no stimulation, right ventricle pacing, left
      ventricle pacing, biventricular pacing) and by stimulation different regions of left
      ventricle. Patients medical history will be acquired : comorbidities, qualification for
      device implantation, and other examinations at that time. In selected patients with typically
      good response for CRT, ECG signal will be registered with Holter method. Based on collected
      ECG, the correlation between clinical data parameters predicting good therapy response will
      be determined. ECG platform. The analysing system for arrhythmia detection consists of
      cloud-based software platform. The captured electrocardiographic signal uploaded to the
      platform is analysed using deep neural networks algorithms. The software allows the ECG
      standard report visualisation of signal and analysis of acquired data in terms of CRT
      sufficiency. The platform is a medical device certified in the European Union.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        The study will be consisted of two independent patient groups: 250 patients treated with
        cardiac resynchronisation therapy (CRT) of whom 225 ECG signal will be acquired, and 15
        24-hour Holter monitoring will be collected; 250 patients with other cardiac implantable
        electronic devices, of whom 225 ECG signal will be acquired and 15 24-hour Holter
        monitoring will be collected.
      
Keywords: Cardiac Resynchronization Therapy; ECG Algorithms; Machine based learning