HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: CJN.00640109v1 4/11/1818    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Miskulin, D. Articles by Port, F. K. PubMed PubMed Citation Articles by Miskulin, D. Articles by Port, F. K.

Key Comorbid Conditions that Are Predictive of Survival among Hemodialysis Patients

Dana Miskulin^*, Jennifer Bragg-Gresham, Brenda W. Gillespie, Francesca Tentori, Ronald L. Pisoni, Hocine Tighiouart, Andrew S. Levey^*, and Friedrich K. Port

^* Division of Nephrology, Tufts Medical Center, Boston, Massachusetts; Arbor Research Collaborative for Health, Ann Arbor, Michigan; Department of Statistics, University of Michigan, Ann Arbor, Michigan; and Institute for Clinical Research and Health Policy, Tufts Medical Center, Boston, Massachusetts

Correspondence: Dr. Dana Miskulin, Tufts Medical Center, 800 Washington Street, Box 391, Boston, MA 02111. Phone: 617-636-9936; Fax: 617-636-8329; E-mail: dmiskulin{at}tuftsmedicalcenter.org

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 (R²) 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 R² 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.