title: Can Adding Laboratory Values Improve Risk-Adjustment Mortality Models Using Clinical Percutaneous Cardiac Intervention Registry Data?
abstract: 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.
authors.affiliation: Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany - State University of New York, One University Place, Room 169, Rensselaer, NY 12144 USA. fqian@albany.edu.
authors.forename: Feng
authors.initials: F
authors.lastname: Qian
authors.forename: Edward L
authors.initials: EL
authors.lastname: Hannan
authors.forename: Michael
authors.initials: M
authors.lastname: Pine
authors.forename: Donald E
authors.initials: DE
authors.lastname: Fry
authors.forename: Kay
authors.initials: K
authors.lastname: Whitman
authors.forename: Barbara A
authors.initials: BA
authors.lastname: Dennison
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000175:diagnosis / Q000401:mortality / Q000601:surgery*; D016208:Databases, Factual; D005260:Female; D005500:Follow-Up Studies; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009518:New York / Q000453:epidemiology; D062645:Percutaneous Coronary Intervention / Q000401:mortality*; D012042:Registries; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D015996:Survival Rate / Q000639:trends; D013997:Time Factors; D055815:Young Adult