Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators. Issue 8 (12th June 2018) - Bates, Jonathan; Parzynski, Craig S.; Dhruva, Sanket S.; Coppi, Andreas; Kuntz, Richard; Li, Shu‐Xia; Marinac‐Dabic, Danica; Masoudi, Frederick A.; Shaw, Richard E.; Warner, Frederick; Krumholz, Harlan M.; Ross, Joseph S.
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.