Impact of Independent Data Adjudication on Hospital-Specific Estimates of Risk-Adjusted Mortality Following Percutaneous Coronary Interventions in Massachusetts

Abstract

Background— As part of state-mandated public reporting of outcomes after percutaneous coronary interventions (PCIs) in Massachusetts, procedural and clinical data were prospectively collected. Variables associated with higher mortality were audited to ensure accuracy of coding. We examined the impact of adjudication on identifying hospitals with possible deficiencies in the quality of PCI care. Methods and Results— From October 2005 to September 2006, 15 721 admissions for PCI occurred in 21 hospitals. Of the 864 high-risk variables from 822 patients audited by committee, 201 were changed, with reassignment to lower acuities in 97 (30%) of the 321 shock cases, 24 (43%) of the 56 salvage cases, and 73 (15%) of the 478 emergent cases. Logistic regression models were used to predict patient-specific in-hospital mortality. Of 241 (1.5%) patients who died after PCI, 30 (12.4%) had a lower predicted mortality with adjudicated than with unadjudicated data. Model accuracy was excellent with either adjudicated or unadjudicated data. Hospital-specific risk-standardized mortality rates were estimated using both adjudicated and unadjudicated data through hierarchical logistic regression. Although adjudication reduced between-hospital variation by one third, risk-standardized mortality rates were similar using unadjudicated and adjudicated data. None of the hospitals were identified as statistical outliers. However, cross-validated posterior-predicted P values calculated with adjudicated data increased the number of borderline hospital outliers compared with unadjudicated data. Conclusions— Independent adjudication of site-reported high-risk features may increase the ability to identify hospitals with higher risk-adjusted mortality after PCI despite having little impact on the accuracy of risk prediction for the entire population.