Electrocardiographic parameters associated with pacemaker induced cardiomyopathy

Zak Loring; Anna Giczewska; Paul Hofmann; Karen Chiswell; Todd T Schlegel; Martin Ugander; Kevin P Jackson; Jonathan P Piccini; Brett D Atwater

doi:10.1016/j.jelectrocard.2022.12.002

Electrocardiographic parameters associated with pacemaker induced cardiomyopathy

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

Authors

Zak Loring¹, Anna Giczewska², Paul Hofmann², Karen Chiswell², Todd T Schlegel³, Martin Ugander⁴, Kevin P Jackson⁵, Jonathan P Piccini⁶, Brett D Atwater⁷

Affiliations

¹ Duke Clinical Research Institute, Durham, NC, USA; Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA. Electronic address: zak.loring@duke.edu.
² Duke Clinical Research Institute, Durham, NC, USA.
³ Nicollier-Schlegel SARL, Trelex, Vaud, Switzerland; Department of Clinical Physiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.
⁴ Department of Clinical Physiology, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden; Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia.
⁵ Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA.
⁶ Duke Clinical Research Institute, Durham, NC, USA; Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA.
⁷ Inova Heart and Vascular Institute, Falls Church, VA, USA.

PMID: 36549180
DOI: 10.1016/j.jelectrocard.2022.12.002

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cardiac Pacing, Artificial
Cardiac Resynchronization Therapy* / adverse effects
Cardiomyopathies* / diagnosis
Cardiomyopathies* / etiology
Electrocardiography
Female
Heart Failure* / diagnosis
Heart Failure* / etiology
Humans
Pacemaker, Artificial* / adverse effects
Ventricular Function, Left

Grants and funding

T32 HL069749/HL/NHLBI NIH HHS/United States