title: Disproportionality analysis for signal detection of implantable cardioverter-defibrillator-related adverse events in the Food and Drug Administration Medical Device Reporting System.
abstract: BACKGROUND
  The Food and Drug Administration (FDA) became aware of lead fracture and inappropriate shock events related to Sprint Fidelis leads in January 2007. The manufacturer announced a voluntary market withdrawal in October 2007.
  
  
  OBJECTIVE
  Our aim was to retrospectively evaluate this safety signal using disproportionality analysis to estimate whether disproportionality analysis could have detected this particular safety signal earlier than actually occurred.
  
  
  METHODS
  The Manufacturer and User Facility Device Experience (MAUDE) database contains reports on device-related adverse events, of which, FDA receives several hundred thousand every year. For each manufacturer, a list of the top lead brand names was ranked by frequency of reports. We used the Multi-item Gamma Poisson Shrinker (MGPS) method for analysis. We isolated 11 top-reported implantable cardioverter defibrillator (ICD) lead brand names. Using MGPS methodology, we calculated the one-sided 95% lower confidence bound EB05 on the empirical Bayes geometric mean of the reporting ratio.
  
  
  RESULTS
  We performed individual MGPS analysis for each of the top reported adverse events in 2006 for ICD leads. Fidelis had the highest EB05 scores for lead fractures and inappropriate shock.
  
  
  CONCLUSIONS
  Through disproportionality analysis of the MAUDE database, we were able to identify known safety signals associated with the Medtronic Sprint Fidelis lead.
  
  
  CONCLUSIONS
  If utilized at the time, this disproportionality analysis would have identified signals earlier for lead fractures, oversensing, high impedance, and inappropriate shock.
authors.affiliation: Division of Epidemiology, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA. Hesha.duggirala@fda.hhs.gov
authors.forename: Hesha J
authors.initials: HJ
authors.lastname: Duggirala
authors.forename: Naomi D
authors.initials: ND
authors.lastname: Herz
authors.forename: Daniel Arthur
authors.initials: DA
authors.lastname: Caños
authors.forename: Roberta A
authors.initials: RA
authors.lastname: Sullivan
authors.forename: Richard
authors.initials: R
authors.lastname: Schaaf
authors.forename: Ellen
authors.initials: E
authors.lastname: Pinnow
authors.forename: Danica
authors.initials: D
authors.lastname: Marinac-Dabic
mesh_terms: D001499:Bayes Theorem; D016208:Databases, Factual; D017147:Defibrillators, Implantable / Q000009:adverse effects*; D004556:Electric Injuries / Q000453:epidemiology* / Q000209:etiology; D004868:Equipment Failure; D004869:Equipment Safety; D006801:Humans; D011358:Product Surveillance, Postmarketing / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D057053:Safety-Based Medical Device Withdrawals; D013997:Time Factors; D014481:United States; D014486:United States Food and Drug Administration