Jonathan Bates1, Craig S Parzynski1, Sanket S Dhruva1,2,3, Andreas Coppi1, Richard Kuntz4, Shu-Xia Li1, Danica Marinac-Dabic5, Frederick A Masoudi6, Richard E Shaw7, Frederick Warner1, Harlan M Krumholz1,2,8,9, Joseph S Ross1,2,8,10. 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.
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.
Authors: Rachel E Sherman; Steven A Anderson; Gerald J Dal Pan; Gerry W Gray; Thomas Gross; Nina L Hunter; Lisa LaVange; Danica Marinac-Dabic; Peter W Marks; Melissa A Robb; Jeffrey Shuren; Robert Temple; Janet Woodcock; Lilly Q Yue; Robert M Califf Journal: N Engl J Med Date: 2016-12-08 Impact factor: 91.245
Authors: Frederic S Resnic; Thomas P Gross; Danica Marinac-Dabic; Nilsa Loyo-Berrios; Sharon Donnelly; Sharon-Lise T Normand; Michael E Matheny Journal: JAMA Date: 2010-11-10 Impact factor: 56.272
Authors: Frederick A Masoudi; Angelo Ponirakis; James A de Lemos; James G Jollis; Mark Kremers; John C Messenger; John W M Moore; Issam Moussa; William J Oetgen; Paul D Varosy; Robert N Vincent; Jessica Wei; Jeptha P Curtis; Matthew T Roe; John A Spertus Journal: J Am Coll Cardiol Date: 2016-12-23 Impact factor: 24.094
Authors: Joseph S Ross; Jonathan Bates; Craig S Parzynski; Joseph G Akar; Jeptha P Curtis; Nihar R Desai; James V Freeman; Ginger M Gamble; Richard Kuntz; Shu-Xia Li; Danica Marinac-Dabic; Frederick A Masoudi; Sharon-Lise T Normand; Isuru Ranasinghe; Richard E Shaw; Harlan M Krumholz Journal: Med Devices (Auckl) Date: 2017-08-16
Authors: Frederick A Masoudi; Angelo Ponirakis; Robert W Yeh; Thomas M Maddox; Jim Beachy; Paul N Casale; Jeptha P Curtis; James De Lemos; Gregg Fonarow; Paul Heidenreich; Christina Koutras; Mark Kremers; John Messenger; Issam Moussa; William J Oetgen; Matthew T Roe; Kenneth Rosenfield; Thomas P Shields; John A Spertus; Jessica Wei; Christopher White; Christopher H Young; John S Rumsfeld Journal: J Am Coll Cardiol Date: 2013-09-18 Impact factor: 24.094
Authors: Michael E Matheny; Sharon-Lise T Normand; Thomas P Gross; Danica Marinac-Dabic; Nilsa Loyo-Berrios; Venkatesan D Vidi; Sharon Donnelly; Frederic S Resnic Journal: BMC Med Inform Decis Mak Date: 2011-12-14 Impact factor: 2.796
Authors: Alison Callahan; Jason A Fries; Christopher Ré; James I Huddleston; Nicholas J Giori; Scott Delp; Nigam H Shah Journal: NPJ Digit Med Date: 2019-09-25