A Predictive Model for the Long-Term Electrical Performance of a Leadless Transcatheter Pacemaker.

Journal Article (Journal Article)

OBJECTIVES: This study sought to formulate a predictive model for describing the long-term electrical performance of Micra (Medtronic, Mounds View, Minnesota). BACKGROUND: The Micra leadless pacemaker is an alternative ventricular pacing option that avoids the pitfalls of transvenous leads. However, well-defined metrics to predict the long-term electrical performance of the device are lacking. METHODS: We identified all patients who underwent successful Micra implantation enrolled in the investigational device exemption study, continued access study, or post-approval registry with complete 1-year post-implantation data or system revision due to elevated thresholds (N = 1,843). The analysis endpoint was an elevated pacing capture threshold (PCT) at ≥12 months post-implantation, defined as ≥2.0 V at 0.24 ms or an increase of ≥1.5 V from implantation or need for system revision due to elevated thresholds at ≤12 months post-implantation. We evaluated for univariate and multivariate associations between patient and device characteristics at implantation and for elevated thresholds at 12 months. RESULTS: Among the total cohort, 75 patients (4.1%) had elevated thresholds at 12 months; of these, 13 required system revisions. Predictors associated with elevated thresholds in univariate analysis included the total number of deployments (excluded from the multivariable model), impedance and PCT at implantation, male sex, history of diabetes, and ischemic cardiomyopathy. Multivariable regression modeling found that male sex, history of diabetes, implantation PCT of ≥2 V, and impedance of <800 Ω were independent predictors of elevated PCT at 12 months (all p < 0.05). CONCLUSION: A history of diabetes, male sex, elevated PCT, and low impedance at implantation were independent predictors of elevated thresholds at 12 months. These metrics represent the foundation of a simple tool to aid in procedural decision making.

Full Text

Duke Authors

Cited Authors

  • Kiani, S; Wallace, K; Stromberg, K; Piccini, JP; Roberts, PR; El-Chami, MF; Soejima, K; Garweg, C; Fagan, DH; Lloyd, MS

Published Date

  • April 2021

Published In

Volume / Issue

  • 7 / 4

Start / End Page

  • 502 - 512

PubMed ID

  • 33358666

Electronic International Standard Serial Number (EISSN)

  • 2405-5018

Digital Object Identifier (DOI)

  • 10.1016/j.jacep.2020.09.010


  • eng

Conference Location

  • United States