ECG Patterns In Cardiac Resynchronization Therapy

Time-related factors predicting a positive response to cardiac resynchronisation therapy in patients with heart failure

This study aimed to identify time parameters predicting favourable CRT response. A total of 38 patients with ischemic cardiomyopathy, qualified for CRT implantation, were enrolled in the study. A 15% reduction in indexed end-systolic volume after 6 months was a criterion for a positive response to CRT. We evaluated QRS duration, measured from a standard ECG before and after CRT implantation and obtained from mapping with NOGA XP system (AEMM); and the delay, measured with the implanted device algorithm (DCD) and its change after 6 months (ΔDCD); and selected delay parameters between the left and right ventricles based on AEMM data. A total of 24 patients presented with a positive response to CRT versus 9 non-responders. After CRT implantation, we observed differences between responders and non-responders group in the reduction of QRS duration (31 ms vs. 16 ms), duration of paced QRS (123 ms vs. 142 ms), and the change of ΔDCDMaximum (4.9 ms vs. 0.44 ms) and ΔDCDMean (7.7 ms vs. 0.9 ms). The difference in selected parameters obtained during AEMM in both groups was related to interventricular delay (40.3 ms vs. 18.6 ms). Concerning local activation time and left ventricular activation time, we analysed the delays in individual left ventricular segments. Predominant activation delay of the posterior wall middle segment was associated with a better response to CRT. Some AEMM parameters, paced QRS time of less than 120 ms and reduction of QRS duration greater than 20 ms predict the response to CRT. ΔDCD is associated with favourable electrical and structural remodelling.Clinical trial registration: SUM No. KNW/0022/KB1/17/15.

The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy

Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy. Methods: A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland). Results: The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%. Conclusions: Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy.

When to choose cardiac resynchronization therapy in chronic heart failure: type and duration of the conduction delay

Cardiac resynchronization therapy (CRT) is an established treatment for patients with heart failure (HF), impaired left ventricular (LV) function, and wide QRS complex. The initial randomized clinical trials, which led to the widespread use of CRT, selected patients on the basis of QRS duration, not focusing on QRS morphology. However, recent evidences emphasized the role of left bundle branch block morphology in patients that underwent CRT in order to predict better response to therapy. Moreover, conventional right ventricular apical pacing might have long-term detrimental effects on cardiac structure and LV function, possibly leading to the development of HF. Therefore, current guidelines recommend upgrade from conventional pacemaker or implantable cardioverter-defibrillator to CRT or de novo CRT in patients with high (or expected high) percentage of ventricular pacing and reduced EF. We reviewed current knowledge on candidates’ selection for CRT based on conduction delays that lead to electrical and mechanical dyssynchrony of the left ventricle.

QRS Complex Detection and Measurement Algorithms for Multichannel ECGs in Cardiac Resynchronization Therapy Patients

We developed an automated approach for QRS complex detection and QRS duration (QRSd) measurement that can effectively analyze multichannel electrocardiograms (MECGs) acquired during abnormal conduction and pacing in heart failure and cardiac resynchronization therapy (CRT) patients to enable the use of MECGs to characterize cardiac activation in such patients. The algorithms use MECGs acquired with a custom 53-electrode investigational body surface mapping system and were validated using previously collected data from 58 CRT patients. An expert cohort analyzed the same data to determine algorithm accuracy and error. The algorithms: 1) detect QRS complexes; 2) identify complexes of the most prevalent morphology and morphologic outliers; and 3) determine the array-specific (i.e., anterior and posterior) and global QRS complex onsets, offsets, and durations for the detected complexes. The QRS complex detection algorithm had a positive predictivity and sensitivity of ≥96% for complex detection and classification. The absolute QRSd error was 17 ± 14 ms, or 12%, for array-specific QRSd and 12 ± 10 ms, or 8%, for global QRSd. The absolute global QRSd error (12 ms) was less than the interobserver variation in that measurement (15 ± 10 ms). The sensitivity, positive predictivity, and error of the algorithms were similar to the values reported for current state-of-the-art algorithms designed for and limited to simpler data sets and conduction patterns and within the variation found in clinical 12-lead ECG QRSd measurement techniques. These new algorithms permit accurate, real-time analysis of QRS complex features in MECGs in patients with conduction disorders and/or pacing.