title: Does Co-morbidity provide significant improvement on age adjustment when predicting medical outcomes?
abstract: OBJECTIVE
  Using three risk-adjustment methods we evaluated whether co-morbidity derived from electronic hospital patient data provided significant improvement on age adjustment when predicting major outcomes following an elective total joint replacement (TJR) due to osteoarthritis.
  
  
  METHODS
  Longitudinal data from 819 elderly men who had had a TJR were integrated with hospital morbidity data (HMD) and mortality records. For each participant, any morbidity or health-related outcome was retrieved from the linked data in the period 1970 through to 2007 and this enabled us to better account for patient co-morbidities. Co-morbidities recorded in the HMD in all admissions preceding the index TJR admission were used to construct three risk-adjustment methods, namely Charlson co-morbidity index (CCI), Elixhauser's adjustment method, and number of co-morbidities. Postoperative outcomes evaluated included length of hospital stay, 90-day readmission, and 1-year and 2-year mortality. These were modelled using Cox proportional hazards regression as a function of age for the baseline models, and as a function of age and each of the risk-adjustment methods. The difference in the statistical performance between the models that included age alone and those that also included the co-morbidity adjustment method was assessed by measuring the difference in the Harrell's C estimates between pairs of models applied to the same patient data using Bootstrap analysis with 1000 replications.
  
  
  RESULTS
  Number of co-morbidities did not provide any significant improvement in model discrimination when added to baseline models observed in all outcomes. CCI significantly improved model discrimination when predicting post-operative mortality but not when length of stay or readmission was modelled. For every one point increase in CCI, postoperative 1- and 2-year mortality increased by 37% and 30%, respectively. Elixhauser's method outperformed the other two providing significant improvement on age adjustment in all outcomes.
  
  
  CONCLUSIONS
  The predictive performance of co-morbidity derived from electronic hospital data is outcome and risk-adjustment method specific.
authors: Dr George Mnatzaganian, Faculty of Health Sciences, Australian Catholic University, Room 8.70, Level 8, 250 Victoria Parade, East Melbourne, Victoria, Australia, E-mail: George.Mnatzaganian@acu.edu.au.
authors: G
authors: G
authors: Mnatzaganian
authors: P
authors: P
authors: Ryan
authors: J E
authors: JE
authors: Hiller
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019643:Arthroplasty, Replacement / Q000706:statistics & numerical data*; D015331:Cohort Studies; D015897:Comorbidity; D003661:Decision Support Techniques; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D010003:Osteoarthritis / Q000601:surgery*; D017063:Outcome Assessment, Health Care; D010359:Patient Readmission / Q000706:statistics & numerical data; D020379:Risk Adjustment; D016019:Survival Analysis
keywords: Hospital morbidity data; age; co-morbidity adjustment method; length of stay; model discrimination; mortality; readmission