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ECG Algorithms for CRT Response Evaluation (OVERCOME)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT04061434
Recruitment Status : Completed
First Posted : August 19, 2019
Last Update Posted : October 6, 2021
National Center for Research and Development, Poland
Information provided by (Responsible Party):
Marcin Grabowski, Medical University of Warsaw

Tracking Information
First Submitted Date May 20, 2019
First Posted Date August 19, 2019
Last Update Posted Date October 6, 2021
Actual Study Start Date March 1, 2019
Actual Primary Completion Date July 30, 2020   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: August 15, 2019)
  • Number of correctly assessed ECG signals by the automatic recognition of resynchronization in CRT-mediated therapy. [ Time Frame: 14 months ]
    Evaluating the effectiveness of CRT therapy based on the record from an implantable device Assessment of the rationale for the use of machine based learning algorithms in detecting ECG abnormalities to determine which clinical conditions have impact on long-term effectiveness of cardiac resynchronization therapy using both standard 12-lead ECG and 24-hour Holter monitoring . The study might identify which clinical parameters in patients with CRT indicate the most benefit and the least benefit from CRT. It is planned to reach 99% sensitivity of automatized recognizing resynchronization in CRT-mediated therapy
  • Correctly recognized ECG signals after adding each cycle of 20 new ECG recordings from patients with electrical heart function disturbances. [ Time Frame: 7 months ]
    To achieve this goal we will collect representative base of ECG recordings containing both paced rhythm in subjects undergoing therapy and those in qualification process in order to use the software to predict CRT response. The final model assumes fully automatized diagnosis of CRT-therapy response based on machine learning. Using this feature in connection with new methods of digital signal processing will constantly increase system's efficacy measured by simultaneous achievement of high test specificity and sensitivity. Increase by 1% of test sensitivity withholding high specificity after adding each cycle of 20 new ECG recordings from patients with electrical heart function disturbances is planned.
Original Primary Outcome Measures Same as current
Change History
Current Secondary Outcome Measures
 (submitted: August 15, 2019)
Number of registered ECG signals from patients holding a CIED. [ Time Frame: 14 months ]
Creation of an database of ECG and Holter monitoring acquired signal from patients with cardiac implantable electronic devices (CIED). Reaching more than 258 ECG recordings in the database to qualify and discriminate signal patterns that can be qualified as certain arrhythmia.
Original Secondary Outcome Measures Same as current
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
Descriptive Information
Brief Title ECG Algorithms for CRT Response Evaluation
Official Title Evaluation of the Effectiveness of CRT Therapy Based on the Record From an Implantable Device
Brief 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.
Study Type Observational
Study Design Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration Not Provided
Biospecimen Not Provided
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.
Condition ECG Monitoring
Intervention Not Provided
Study Groups/Cohorts
  • Cardiac resynchronisation therapy recipients
  • Other cardiac implantable electronic devices recipients
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by Identifier (NCT Number) in Medline.
Recruitment Information
Recruitment Status Completed
Actual Enrollment
 (submitted: October 5, 2021)
Original Estimated Enrollment
 (submitted: August 15, 2019)
Actual Study Completion Date July 30, 2020
Actual Primary Completion Date July 30, 2020   (Final data collection date for primary outcome measure)
Eligibility Criteria

Inclusion Criteria:

  • State after CRT implantation with cardiac defibrillation function (CRT-D)
  • State after CRT implantation with pacing function (CRT-P)
  • State after implantation of cardiac pacemaker
  • State after ICD implantation with indications for periodic heart stimulation
  • Signed written informed consent

Exclusion Criteria:

  • Patient's lack of consent
  • Pacemaker dependency with patient's own rhythm insufficient for appropriate perfusion of central nervous system
Sexes Eligible for Study: All
Ages 18 Years to 100 Years   (Adult, Older Adult)
Accepts Healthy Volunteers No
Contacts Contact information is only displayed when the study is recruiting subjects
Listed Location Countries Poland
Removed Location Countries  
Administrative Information
NCT Number NCT04061434
Other Study ID Numbers OVERCOME
Has Data Monitoring Committee No
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: Yes
Responsible Party Marcin Grabowski, Medical University of Warsaw
Study Sponsor Medical University of Warsaw
Collaborators National Center for Research and Development, Poland
Investigators Not Provided
PRS Account Medical University of Warsaw
Verification Date October 2021