Factors Influencing the Use of Higher-Tech/Higher Cost Implantable Cardioverter-Defibrillators: Data from the NCDR ® ICD Registry TM Rachel Lampert, MD, Yongfei Wong, MS, Jeptha Curtis, MD Yale University School of Medicine, New Haven, CT and on behalf of the NCDR Background We analyzed the 84,321 cases receiving a new ICD implants submitted by 981 hospitals to the NCDR ICD Registry from 1/1/05 to 7/1/07. Market-release dates of all devices used during this time-period were obtained from the manufacturers Devices were categorized as “outmoded” 3 months after a new model from the same manufacturer was released, based on date of implant. Devices with specific and unique functions or purposes were not included in the analysis, including: Medtronic Onyx, a “shock box”, (N=128,) Guidant Vitality AT (N=94) and Medtronic Gem IIIAT (N=4). Also excluded were devices listed in the Registry as “clinical trial device”, N=143. Patient and hospital characteristics between patients who received the outmoded devices and patients who did not receive the outmoded devices were compared using chi-square test for categorical variables and t-test for continuous variables. A non-parsimonious model to predict use of an “outmoded” device was derived from all demographic, clinical, provider, and hospital characteristics using logistic regression model with stepwise selection method (entry P value=0.15 and retain P value=0.05). Hospital and physician use of the “outmoded devices” are examined within the deciles of the corresponding use rates. Hierarchical logistic regression model with variables in this non-parsimonious model was used to estimate the intra-class correlation showing the hospital variation on the use of “outmoded” devices. A random effects model (two level, patient and hospital) was used to determine the extent of variance due to between-hospital variation. Methods For more information go to or New ICD models are regularly introduced, incorporating new technologies As of January 1, 2005, the start-time for the analysis, there were 61 ICD models on the market, from five manufacturers. During the period of analysis, 1/1/05 through 7/1/07, another 45 models were market-released Factors influencing the use of newer, higher-tech, usually higher-cost models, over older models still available, have not been identified We sought to determine whether race, gender, or other demographic variables influenced use of the most up-to-date, versus outmoded, devices SummaryConclusion Results In bivariate analysis, while there were statistically significant differences in demographic, clinical, provider, and hospital factors in use of outmoded devices, these were of very small magnitude, with the exception of number of chambers implanted (single vs. dual vs. biventricular) Overall, in multivariable analysis, measured variables had limited ability to predict use of outmoded devices (multivariable ROC C-statistic 0.597) While specific provider characteristics (training) had minimal influence on use of outmoded devices, there was enormous variation among individual providers in use of outmoded devices, ranging from 0% in the bottom 20% of implanting physicians to 100% in the top 10% of implanters Similarly, although specific hospital characteristics (profit type, academic, region) had minimal influence on use of outmoded devices, there was wide variation between individual hospitals in use of outmoded devices, ranging from 2% In the bottom 98 hospitals (bottom decile) to 90% in the top 98.. After adjusting for the given covariates, the unexplained variance in the use of outmoded devices was primarily at the hospital level, with 69% of the unexplained variance being between hospitals. The use of outmoded devices is influenced minimally by patient or provider characteristics. Rather, the main determinant of whether patients receive the most up-to- date, versus an outmoded device, appears to be practice patterns at individual hospitals, which may be affected by cost, implanting physician preference, and other factors. Percent of patients receiving an “outmoded” deviceMultivariable model: Adjusted odds ratios for use of an outmoded device Multivariable ROC model C-statistic = Contribution of between-hospital effect to unexplained variation in a random effects model was 69%. DescriptionPr > |t|ORLORUOR Intercept Demograhics Race: White Race: Black Race: Other Payor: Government Payor: Commercial Payor: HMO Payor: Other Clinical Characteristics NYHA: Class I NYHA: Class II NYHA: Class III NYHA: Class IV QRS Duration AVC: Normal AVC: Abnormal-1st Degree HB Only AVC: Abnormal-HB 2nd or 3rd Degree AVC: Paced(any) Cardiac Arrest: No Cardiac Arrest: Brady Arrest Cardiac Arrest: Tachy Arrest Atrial Fibrillation/Atrial Flutter Electrophysiology Study Done Syncope Previous CABG ICD Type: Single Chamber ICD Type: Dual Chamber ICD Type: Biventricular Physician Characteristics EP Operator ICD Training: EP EP Operator ICD Training: CVD EP Operator ICD Training: TS EP Operator ICD Training: IM EP Operator ICD Training: HRS EP Operator ICD Training: Other Hospital Characteristics HCO Community: Rural HCO Community: SubUrban HCO Community: Urban Teaching HCO Profit Type: Government HCO Profit Type: Private/Community HCO Profit Type: University Annualized ICD volume Census region: Northeast Census region: South Census region: Midwest Census region: West In bivariate analysis, while there were statistically significant differences in demographic, clinical, provider, and hospital factors in use of outmoded devices, these were of very small magnitude, with the exception of number of chambers implanted (single vs. dual vs. biventricular) Hospital variation in use of “outmoded” devices Physician variation in use of “outmoded” devices