Fully automated QRS area measurement for predicting response tocardiac resynchronization therapy

0536110 - ÚPT 2021 RIV US eng J - Journal Article
Plešinger, Filip - van Stipdonk, A. M. W. - Smíšek, Radovan - Halámek, Josef - Jurák, Pavel - Maass, A. H. - Meine, M. - Vernooy, K. - Prinzen, F. W.
Fully automated QRS area measurement for predicting response tocardiac resynchronization therapy.
Journal of Electrocardiology. Roč. 63, NOV-DEC (2020), s. 159-163. ISSN 0022-0736. E-ISSN 1532-8430
R&D Projects: GA ČR GA17-13830S; GA MŠMT(CZ) LO1212
Grant - others:AV ČR(CZ) MSM100651602
Program: Program na podporu mezinárodní spolupráce začínajících výzkumných pracovníků
Institutional support: RVO:68081731
Keywords : heart failure * cardiac resynchronization therapy * vectorcardiography * QRS area * software * signal averaging
OECD category: Medical engineering
Impact factor: 1.438, year: 2020
Method of publishing: Open access
https://www.sciencedirect.com/science/article/pii/S0022073619303437

Background: Cardiac resynchronization therapy (CRT) is an established treatment in patients with heart failureand conduction abnormalities. However, a significant number of patients do not respond to CRT. Currentlyemployed criteria for selection of patients for this therapy (QRS duration and morphology) have several short-comings. QRS area was recently shown to provide superior association with CRT response. However, its assess-ment was not fully automated and required the presence of an expert. Objective: Our objective was to develop a fully automated method for the assessment of vector-cardiographic(VCG) QRS area from electrocardiographic (ECG) signals. Methods: Pre-implantation ECG recordings (N = 864, 695 left-bundle-branch block, 589 men) in PDFfiles wereconverted to allow signal processing. QRS complexes were found and clustered into morphological groups. Sig-nals were converted from 12‑lead ECG to 3‑lead VCG and an average QRS complex was built. QRS area was com-putedfrom individualareasinthe X, Y and Z leads. Practical usability wasevaluatedusing Kaplan-Meierplots and5-year follow-up data. Results:The automatically calculated QRS area values were 123 ± 48μV.s (mean values and SD), while the man-ually determined QRS area values were 116 ± 51 ms, the correlation coefficient between the two was r = 0.97.The automated and manual methods showed the same ability to stratify the population (hazard ratios 2.09 vs2.03, respectively).Conclusion:The presented approach allows the fully automatic and objective assessment of QRS area values.Significance:Until this study, assessing QRS area values required an expert, which means both additional costsand a risk of subjectivity. The presented approach eliminates these disadvantages and is publicly available aspart of free signal-processing software.
Permanent Link: http://hdl.handle.net/11104/0313942