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Pharmacoepidemiol Drug Saf. 2018 Aug;27(8):848-856. doi: 10.1002/pds.4565. Epub 2018 Jun 12.

Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators.

Author information

1
Center for Outcomes Research and Evaluation, Yale-New Haven Health System, New Haven, CT, USA.
2
National Clinician Scholars Program, Yale School of Medicine, New Haven, CT, USA.
3
Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
4
Medtronic, Inc., Minneapolis, MN, USA.
5
Division of Epidemiology, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA.
6
Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
7
Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA.
8
Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA.
9
Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, USA.
10
Section of General Internal Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, USA.

Abstract

PURPOSE:

To estimate medical device utilization needed to detect safety differences among implantable cardioverter defibrillators (ICDs) generator models and compare these estimates to utilization in practice.

METHODS:

We conducted repeated sample size estimates to calculate the medical device utilization needed, systematically varying device-specific safety event rate ratios and significance levels while maintaining 80% power, testing 3 average adverse event rates (3.9, 6.1, and 12.6 events per 100 person-years) estimated from the American College of Cardiology's 2006 to 2010 National Cardiovascular Data Registry of ICDs. We then compared with actual medical device utilization.

RESULTS:

At significance level 0.05 and 80% power, 34% or fewer ICD models accrued sufficient utilization in practice to detect safety differences for rate ratios <1.15 and an average event rate of 12.6 events per 100 person-years. For average event rates of 3.9 and 12.6 events per 100 person-years, 30% and 50% of ICD models, respectively, accrued sufficient utilization for a rate ratio of 1.25, whereas 52% and 67% for a rate ratio of 1.50. Because actual ICD utilization was not uniformly distributed across ICD models, the proportion of individuals receiving any ICD that accrued sufficient utilization in practice was 0% to 21%, 32% to 70%, and 67% to 84% for rate ratios of 1.05, 1.15, and 1.25, respectively, for the range of 3 average adverse event rates.

CONCLUSIONS:

Small safety differences among ICD generator models are unlikely to be detected through routine surveillance given current ICD utilization in practice, but large safety differences can be detected for most patients at anticipated average adverse event rates.

KEYWORDS:

implantable defibrillators; medical devices; pharmacoepidemiology; postmarketing product surveillance; sample size

PMID:
29896873
DOI:
10.1002/pds.4565

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