title: Comparison of the Charlson Comorbidity Index and the Davies score as a predictor of outcomes in PD patients.
abstract: BACKGROUND
  Comorbidity is a strong predictor and confounds many studies of outcomes. Previous studies have shown that the Charlson Comorbidity Index (CCI) and the Davies score predict mortality in peritoneal dialysis (PD) patients. However, there are few data on the comparison of comorbidity scores.
  
  
  OBJECTIVE
  To compare the CCI (combines comorbidity and age) and Davies score (comorbidity score without age) to see if one score was superior to the other in predicting outcomes.
  
  
  METHODS
  Prospective database study.
  
  
  METHODS
  Seven dialysis centers in Western Pennsylvania.
  
  
  METHODS
  415 incident PD patients, starting PD from 1/1/90 to 2/1/00.
  
  
  METHODS
  The CCI and Davies score calculated at the start of PD; serum albumin levels and demographics at the start of PD; total hospitalizations and mortality, collected prospectively.
  
  
  RESULTS
  The correlation between CCI and Davies was 0.80, p < 0.0001. The CCI was inversely correlated with serum albumin (-0.31, p < 0.0001). Davies was significantly correlated with age (0.32, p < 0.0001) and inversely correlated with albumin (-0.27, p < 0.0001). The CCI alone was a stronger predictor than Davies alone (score by best subsets regression 49.6 vs 42.0, p = 0.0058). The CCI and Davies with age appeared to be equivalent models of survival (49.61 vs 49.64). The best predictive models were CCI and initial albumin, or Davies, age, and initial albumin. Both CCI and Davies were predictors of hospitalization rates, but the model with the Davies score was better (Akaike information criterion 799.2 vs 850.2). The best predictive model was Davies, albumin, age, and race.
  
  
  CONCLUSIONS
  Both comorbidity scores were significant predictors of outcomes, with CCI the stronger predictor for mortality, but the Davies was a stronger predictor of hospitalizations. One or both should be done at the start of dialysis to predict outcome.
authors.affiliation: Renal Section, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA. lff9+@pitt.edu
authors.forename: Linda
authors.initials: L
authors.lastname: Fried
authors.forename: Judith
authors.initials: J
authors.lastname: Bernardini
authors.forename: Beth
authors.initials: B
authors.lastname: Piraino
mesh_terms: D005260:Female; D006305:Health Status Indicators; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D010530:Peritoneal Dialysis / Q000401:mortality; D011379:Prognosis; D011446:Prospective Studies