title: Key comorbid conditions that are predictive of survival among hemodialysis patients.
abstract: OBJECTIVE
  Abstracting information about comorbid illnesses from the medical record can be time-consuming, particularly when a large number of conditions are under consideration. We sought to determine which conditions are most prognostic and whether comorbidity continues to contribute to a survival model once laboratory and clinical parameters have been accounted for.
  
  
  METHODS
  Comorbidity data were abstracted from the medical records of Dialysis Outcomes and Practice Pattern Study (DOPPS) I, II, and III participants using a standardized questionnaire. Models that were composed of different combinations of comorbid conditions and case-mix factors were compared for explained variance (R(2)) and discrimination (c statistic).
  
  
  RESULTS
  Seventeen comorbid conditions account for 96% of the total explained variance that would result if 45 comorbidities that were expected to be predictive of survival were added to a demographics-adjusted survival model. These conditions together had more discriminatory power (c statistic 0.67) than age alone (0.63) or serum albumin (0.60) and were equivalent to a combination of routine laboratory and clinical parameters (0.67). The strength of association of the individual comorbidities lessened when laboratory/clinical parameters were added, but all remained significant. The total R(2) of a model adjusted for demographics and laboratory/clinical parameters increased from 0.13 to 0.17 upon addition of comorbidity.
  
  
  CONCLUSIONS
  A relatively small list of comorbid conditions provides equivalent discrimination and explained variance for survival as a more extensive characterization of comorbidity. Comorbidity adds to the survival model a modest amount of independent prognostic information that cannot be substituted by clinical/laboratory parameters.
authors.affiliation: Division of Nephrology, Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111, USA. dmiskulin@tuftsmedicalcenter.org
authors.forename: Dana
authors.initials: D
authors.lastname: Miskulin
authors.forename: Jennifer
authors.initials: J
authors.lastname: Bragg-Gresham
authors.forename: Brenda W
authors.initials: BW
authors.lastname: Gillespie
authors.forename: Francesca
authors.initials: F
authors.lastname: Tentori
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Pisoni
authors.forename: Hocine
authors.initials: H
authors.lastname: Tighiouart
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Levey
authors.forename: Friedrich K
authors.initials: FK
authors.lastname: Port
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000401:mortality*; D002561:Cerebrovascular Disorders / Q000401:mortality; D015897:Comorbidity; D003327:Coronary Disease / Q000401:mortality; D003928:Diabetic Nephropathies / Q000401:mortality*; D005260:Female; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D016491:Peripheral Vascular Diseases / Q000401:mortality; D011237:Predictive Value of Tests; D015995:Prevalence; D006435:Renal Dialysis / Q000401:mortality*; D016019:Survival Analysis