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. 2023 Mar-Apr:77:17-22.
doi: 10.1016/j.jelectrocard.2022.12.002. Epub 2022 Dec 13.

Electrocardiographic parameters associated with pacemaker induced cardiomyopathy

Zak Loring  1 , Anna Giczewska  2 , Paul Hofmann  2 , Karen Chiswell  2 , Todd T Schlegel  3 , Martin Ugander  4 , Kevin P Jackson  5 , Jonathan P Piccini  6 , Brett D Atwater  7
Affiliations

Electrocardiographic parameters associated with pacemaker induced cardiomyopathy

Zak Loring et al. J Electrocardiol. 2023 Mar-Apr.

Abstract

Background: Chronic right ventricular (RV) pacing can induce left ventricular (LV) dyssynchrony and cause pacemaker induced cardiomyopathy (PiCM). Identifying which patients are at risk for PiCM is limited.

Methods: Patients receiving RV-only permanent pacemakers (PPMs) at Duke University Medical Center between 2011 and 2017 who had normal baseline ejection fractions (EFs) were identified. Patients who developed a subsequent decrease in EF, died, or underwent cardiac resynchronization therapy, left ventricular assist device, or heart transplant without a competing cause were considered as the primary endpoint. Pre-PPM and post-PPM electrocardiograms (ECGs) were analyzed to extract scalar measurements including the lead one ratio (LOR) as well as advanced-ECG (A-ECG) features to identify predictors of PiCM. Traditional and penalized Cox regression were used to identify variables predictive of the primary endpoint.

Results: Pre-PPM ECGs were evaluated for 404 patients of whom 140 (35%) experienced the primary endpoint. Predictors included female sex (hazard ratio [HR] 1.14), a T' wave in V6 (HR 1.31), a P' wave in aVL (HR 0.88), and estimated glomerular filtration rate (HR 0.88). Post-PPM ECGs were evaluated for 228 patients for whom 94 (41%) experienced the primary endpoint. Predictors included female sex (HR 0.50), age (HR 1.06), and a history of congestive heart failure (HR 1.63). Neither LOR nor A-ECG parameters were strong predictors of the primary endpoint.

Conclusions: Baseline and paced ECG data provide limited insight into which patients are at high risk for developing PiCM.

Keywords: Cardiomyopathy; Electrocardiography; Pacemakers.

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Conflict of interest statement

Declaration of Competing Interest Dr. Loring received support from NIH T32 training grant #5T32HL069749, receives grant support from Boston Scientific and serves as a consultant for Huxley Medical and Boston Scientific. Dr. Schlegel is owner and founder of Nicollier-Schlegel SARL, which performs ECG interpretation consultancy using software that can quantify the adnvanced ECG measures used in the current study. Drs. Schlegel and Ugander are owners and founders of Advanced ECG Systems, a company that is developing commercial applications of advanced ECG technology used in the current study. Dr. Piccini is supported by R01AG074185 from the National Institutes of Aging. He also receives grants for clinical research from Abbott, the American Heart Association, the Association for the Advancement of Medical Instrumentation, Bayer, Boston Scientific, iRhythm, and Philips and serves as a consultant to Abbott, Abbvie, Ablacon, Altathera, ARCA biopharma, Bayer, Biotronik, Boston Scientific, Bristol Myers Squibb (Myokardia), Element Science, Itamar Medical, LivaNova, Medtronic, Milestone, ElectroPhysiology Frontiers, ReCor, Sanofi, Philips, and Up-to-Date. Dr. Atwater receives grants from Boston Scientific, FDA/Nestcc/MDIC, Medtronic, and serves on an advisory board or as a consultant to Abbott Medical, Biotronik, Biosense Webster, Boston Scientific, and Medtronic PLC. All other authors have no interests to disclose.

Figures

Fig. 1.
Fig. 1.
Consort diagram.
Fig. 2.
Fig. 2.
Elastic net model example using clinical and scalar ECG data.
See this image and copyright information in PMC

Comment in

References

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