Assessment of the Value of Electronic Health Records Data for Identifying Implantable Cardiac Lead Failures

  • Technology of Interest

    Cardiac Device Leads

  • Disease Area


  • Network Collaborators


  • Duration

    17 months

  • Status


  • Overview

    The primary objective of this study is to examine the feasibility of establishing a generalizable and efficient process for determining medical device reliability. The study will specifically focus on implantable leads (i.e. electrodes) for permanent cardiac pacemakers and defibrillators using different data sources, including electronic health record (EHR) data, CMS claims data, device manufacturer databases, and the U.S. Food and Drug Administration (FDA)’s Medical Device Adverse Event Reports (MAUDE) database. The leads examined in this study are Class III devices.

    The ability to mobilize vast amounts of patient data from the EHR holds tremendous potential for evaluating the effectiveness and safety of medical devices through observational studies and pragmatic trials. However, test cases of the value of EHR data for such purposes and the superiority of EHR over other extant data are lacking.

    This study seeks to ascertain the degree to which existing EHR data can be harnessed to determine device reliability, using cardiac leads as a test case. The study is also triangulating several different data sources to examine device reliability to determine the strengths and limitations of these different sources, and further examining the value of PCORnet as a national resource to address a wide range of issues related to medical device safety. Using EHR data, lead failures will be determined based on specific procedure codes found in the EHR after the date of implantation.

    This study is an ideal NESTcc Test-Case, as NESTcc brings together key stakeholders from industry, academia, and the regulatory sphere around a common goal of improving methods for assessing device safety and effectiveness. As such, NESTcc facilitates the conduct of research through active matchmaking within its wide range of available Data Network sources.