Literature DB >> 29896873

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

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.   

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.
Copyright © 2018 John Wiley & Sons, Ltd.

Entities:  

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

Mesh:

Year:  2018        PMID: 29896873      PMCID: PMC6436550          DOI: 10.1002/pds.4565

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  22 in total

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3.  The Food and Drug Administration's unique device identification system: better postmarket data on the safety and effectiveness of medical devices.

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4.  Uptake of new drugs in the early post-approval period in the Mini-Sentinel distributed database.

Authors:  Katrina Mott; David J Graham; Sengwee Toh; Joshua J Gagne; Mark Levenson; Yong Ma; Marsha E Reichman
Journal:  Pharmacoepidemiol Drug Saf       Date:  2016-05-04       Impact factor: 2.890

5.  Real-World Evidence - What Is It and What Can It Tell Us?

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Journal:  N Engl J Med       Date:  2016-12-08       Impact factor: 91.245

6.  Automated surveillance to detect postprocedure safety signals of approved cardiovascular devices.

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Journal:  JAMA       Date:  2010-11-10       Impact factor: 56.272

Review 7.  Trends in U.S. Cardiovascular Care: 2016 Report From 4 ACC National Cardiovascular Data Registries.

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

8.  Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.

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

Review 9.  Cardiovascular care facts: a report from the national cardiovascular data registry: 2011.

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

10.  Evaluation of an automated safety surveillance system using risk adjusted sequential probability ratio testing.

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

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1.  Medical device surveillance with electronic health records.

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
  1 in total

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