Background and objectives: 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.
Design, setting, participants, & measurements: 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.