Factors Influencing the Use of Higher-Tech/Higher Cost Implantable Cardioverter-Defibrillators: Data from the NCDR® ICD RegistryTM

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 www.ncdr.com or email ncdrresearch@acc.org

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-costmodels, 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

Summary

Conclusion

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” device Multivariable model: Adjusted odds ratios for use of an outmoded device

Multivariable ROC model C-statistic = 0.597Contribution of between-hospital effect to unexplained variation in a random effects model was 69%.

Description Pr > |t| OR LOR UOR

Intercept 0.0000DemograhicsRace: White 1.0000Race: Black 0.9095 0.9971 0.9478 1.0489Race: Other 0.8015 1.0094 0.9387 1.0854Payor: Government 1.0000Payor: Commercial 0.0021 0.9381 0.9007 0.9771Payor: HMO 0.0178 0.9259 0.8688 0.9868Payor: Other 0.0011 1.1545 1.0593 1.2583Clinical CharacteristicsNYHA: Class I 1.0000NYHA: Class II 0.7049 0.9903 0.9415 1.0416NYHA: Class III 0.0091 0.9311 0.8824 0.9824NYHA: Class IV 0.1974 0.9442 0.8652 1.0304QRS Duration 0.3745 1.0003 0.9997 1.0009AVC: Normal 1.0000AVC: Abnormal-1st Degree HB Only 0.9003 1.0027 0.9615 1.0456

AVC: Abnormal-HB 2nd or 3rd Degree 0.0047 1.1328 1.0389 1.2352AVC: Paced(any) 0.0549 1.1510 0.9970 1.3288Cardiac Arrest: No 1.0000Cardiac Arrest: Brady Arrest 0.4064 1.0704 0.9116 1.2567Cardiac Arrest: Tachy Arrest 0.1894 1.0384 0.9816 1.0984Atrial Fibrillation/Atrial Flutter 0.0257 0.9602 0.9265 0.9951Electrophysiology Study Done 0.3502 0.9771 0.9309 1.0257Syncope 0.0055 1.0600 1.0173 1.1044Previous CABG 0.0373 1.0367 1.0021 1.0724ICD Type: Single Chamber 0.0000 1.9317 1.8333 2.0353ICD Type: Dual Chamber 0.0000 1.2020 1.1466 1.2602ICD Type: Biventricular 1.0000Physician CharacteristicsEP Operator ICD Training: EP 1.0000EP Operator ICD Training: CVD 0.0043 0.9198 0.8685 0.9741EP Operator ICD Training: TS 0.5286 0.9500 0.8100 1.1142EP Operator ICD Training: IM 0.4617 1.0731 0.8893 1.2950EP Operator ICD Training: HRS 0.2762 1.1099 0.9200 1.3389EP Operator ICD Training: Other 0.0059 0.8514 0.7593 0.9548Hospital CharacteristicsHCO Community: Rural 0.0004 1.4869 1.1937 1.8520HCO Community: SubUrban 0.4398 0.9323 0.7804 1.1137HCO Community: Urban 1.0000Teaching 0.9175 1.0092 0.8478 1.2014HCO Profit Type: Government 0.1763 1.5023 0.8329 2.7098HCO Profit Type: Private/Community 1.0000HCO Profit Type: University 0.7223 0.9501 0.7166 1.2597Annualized ICD volume 0.0087 0.9986 0.9975 0.9996Census region: NortheastCensus region: South 0.9181 1.0125 0.7992 1.2828Census region: Midwest 0.8686 0.9798 0.7692 1.2480Census region: West 0.0350 0.7449 0.5665 0.9795         

0

10

20

30

40

50

60

70

80

90

100

totalSingle-chamber

Dual chamberBiventricular

MaleFemale

WhiteBlack

Other

GovernmentCommercial

HMO

Other

Device type

Demographiccharacteristics

No arrestBrady arrest

I IIIII IV

NYHA Class

Tachy arrest

Clinicalcharacteristics

EPCardiologist

Surgeon

Physiciancharacteristics

TeachingNon-teaching

NortheastSouth

Midwest

West

Hospitalcharacteristics

*

* P <0.0001

* * * * * * *

Per

cent

use

of

outm

ode

d de

vice

s0

10

20

30

40

50

60

70

80

90

100

totalSingle-chamber

Dual chamberBiventricular

MaleFemale

WhiteBlack

Other

GovernmentCommercial

HMO

Other

Device type

Demographiccharacteristics

No arrestBrady arrest

I IIIII IVII IV

NYHA Class

Tachy arrest

Clinicalcharacteristics

EPCardiologist

Surgeon

Physiciancharacteristics

TeachingNon-teaching

NortheastSouth

Midwest

West

Hospitalcharacteristics

*

* P <0.0001

* * * * * * *

Per

cent

use

of

outm

ode

d de

vice

s

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