title: A simple comorbidity scale predicts clinical outcomes and costs in dialysis patients.
abstract: OBJECTIVE
  In a university-based dialysis program, we found that 25% of the patients accounted for 50% of the costs and 42% of the deaths. We determined whether the Charlson Comorbidity Index, a simple measure of comorbid conditions, could predict clinical outcomes and costs in these patients.
  
  
  METHODS
  Patients on hemodialysis or peritoneal dialysis from July 1996 to June 1998 at the University of Pittsburgh outpatient dialysis unit were studied. Comorbidity scores and outcomes were determined by reviewing the Medical Archival Retrieval System database and outpatient records.
  
  
  RESULTS
  Two hundred sixty-eight patients were observed for 293 patient-years. The Comorbidity Index strongly predicted admission rate (relative risk per each unit increase = 1.20; 95% confidence interval [CI]: 1.16 to 1.23, P = 0.0001), hospital days and inpatient costs (both P <0.0001), and mortality (relative risk per unit increase = 1.24, 95% CI: 1.11 to 1.39, P = 0.0002.). Age and diabetes, used in the Health Care Financing Administration dialysis capitation model, correlated poorly with outcomes.
  
  
  CONCLUSIONS
  The modified Charlson Comorbidity Index predicts outcomes and costs in dialysis patients. This index may be useful in determining appropriate payment for care of dialysis patients under capitated payment schemes and as a research tool to stratify dialysis patients in order to compare the outcomes of various interventions.
authors.affiliation: Renal-Electrolyte Division, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
authors.forename: S
authors.initials: S
authors.lastname: Beddhu
authors.forename: F J
authors.initials: FJ
authors.lastname: Bruns
authors.forename: M
authors.initials: M
authors.lastname: Saul
authors.forename: P
authors.initials: P
authors.lastname: Seddon
authors.forename: M L
authors.initials: ML
authors.lastname: Zeidel
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D002675:Child, Preschool; D015897:Comorbidity; D003920:Diabetes Mellitus / Q000191:economics / Q000453:epidemiology; D005260:Female; D017048:Health Care Costs; D006305:Health Status Indicators; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007676:Kidney Failure, Chronic / Q000191:economics* / Q000453:epidemiology* / Q000401:mortality / Q000628:therapy; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania / Q000453:epidemiology; D010530:Peritoneal Dialysis / Q000191:economics / Q000401:mortality; D011237:Predictive Value of Tests; D006435:Renal Dialysis / Q000191:economics / Q000401:mortality; D012306:Risk; D016896:Treatment Outcome; D014481:United States / Q000453:epidemiology