Key result
Combined registry and administrative models identify secondary prevention ICD indications with ~90% accuracy.
Why the study?
Administrative health data and cardiac device registries often have incomplete information on whether implantable cardioverter defibrillator implantation was for primary or secondary prevention.
Can administrative health data and cardiac device registry data accurately distinguish secondary prevention ICDs from primary prevention ICDs?
Observational (n=5,936)
Yes
Can administrative health data and cardiac device registry data accurately distinguish secondary prevention ICDs from primary prevention ICDs?
Effect estimate: Sensitivity ≥ 90%, Specificity ≥ 89%
Administrative health and cardiac device registry data can accurately predict whether an ICD was implanted for primary or secondary prevention, enabling more robust retrospective research and health system monitoring.
May enable robust retrospective ICD research with administrative data; extends validation of registry linkages for indication classification.
Background: Administrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death). Methods: We used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples. Results: Models that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%). Conclusions: Administrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.
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Robinson et al. (2024) conducted an observational in Implantable cardioverter defibrillator (ICD) recipients (n=5,936). Statistical prediction models using registry and administrative data vs. Reference standard (chart review or nonmissing registry indication) was evaluated on Prediction of secondary prevention ICD indication (Sensitivity ≥ 90%, Specificity ≥ 89%). Statistical models combining cardiac device registry and administrative health data accurately distinguished secondary from primary prevention ICD indications with sensitivity ≥90% and specificity ≥89%.
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