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.