Can Adding Laboratory Values Improve Risk-Adjustment Mortality Models Using Clinical Percutaneous Cardiac Intervention Registry Data?

Background: Registry data for percutaneous coronary intervention (PCI) are being used in New York and Massachusetts and by the American College of Cardiology to risk-adjust provider mortality rates. These registries contain very few numerical laboratory data for risk adjustment.

Methods: For 20 hospitals, New York's PCI registry data from 2008-2010 were used to develop statistic models for predicting in-hospital/30-day mortality with and without appended laboratory data. Discrimination, calibration, correlation in hospital's risk-adjusted mortality rates, and differences in hospital quality outlier status were compared for the two models.

Results: The discrimination of the risk-adjustment models was very similar (C-statistic = 0.898 from the registry model vs C-statistic = 0.908 from the registry/laboratory model; P=.40). Most of the non-laboratory variables in the two models were identical, except that the registry model contained malignant ventricular arrhythmia and the registry/laboratory model contained previous coronary artery bypass surgery. The registry/laboratory model also contained albumin ≤3.3 g/dL, creatine kinase ≥600 U/L, glucose ≥270 mg/dL, platelet count >350 k/μL, potassium >51 mmol/L, and partial thromboplastin time >40 seconds. The addition of laboratory data did not affect outlier status for better-performing hospitals, but there were differences in identifying the hospitals with significantly higher risk-adjusted mortality rates.

Conclusions: Adding laboratory data did not significantly improve the risk-adjustment mortality models' performance and did not dramatically change the quality assessment of hospitals. The pros and cons of adding key laboratory variables to PCI registries require further evaluation.