American Journal of Kidney Diseases
Volume 50, Issue 4 , Pages 559-565, October 2007

Predicting Renal Replacement Therapy and Mortality in CKD

  • Eric S. Johnson, PhD

      Affiliations

    • Center for Health Research, Kaiser Permanente Northwest, Portland, OR
  • ,
  • Micah L. Thorp, DO, MPH

      Affiliations

    • Department of Nephrology, Kaiser Permanente Northwest, Portland, OR
  • ,
  • Xiuhai Yang, MS

      Affiliations

    • Center for Health Research, Kaiser Permanente Northwest, Portland, OR
  • ,
  • Olivier L. Charansonney, MD, PhD, MBA

      Affiliations

    • Sanofi-Aventis, Paris, France.
  • ,
  • David H. Smith, RPh, PhD

      Affiliations

    • Center for Health Research, Kaiser Permanente Northwest, Portland, OR
    • Corresponding Author InformationAddress correspondence to David H. Smith, RPh, PhD, Center for Health Research, Kaiser Permanente Northwest, 3800 N Interstate Ave, Portland, OR 97227.

Received 18 December 2006; accepted 5 July 2007.

Background

Prognostic risk scores can help clinicians intervene on higher risk patients and counsel them. Our objective is to identify characteristics that predict the rate of progression to renal replacement therapy (RRT) and evaluate how those characteristics predict mortality and a composite end point (RRT and mortality).

Study Design

Retrospective cohort study.

Setting & Participants

We conducted the study at Kaiser Permanente Northwest, a health maintenance organization. We followed up members with an estimated glomerular filtration rate (eGFR) that indicated chronic kidney disease (2 eGFRs < 60 mL/min/1.73 m2 [<1.0 mL/s/1.73 m2] at least 90 days apart).

Predictors

We measured baseline clinical characteristics between January 1997 and June 2000 by using electronic medical records and patients’ histories of hospitalization.

Outcomes & Measurements

We calculated adjusted hazard ratios and concordance statistics for progression to RRT, mortality, and the composite by using Cox regression.

Results

Patients (n = 6,541) were followed up for up to 5 years. We observed 1.6 progressions to RRT/100 person-years and 11.4 deaths/100 person-years. The 6 characteristics of age, sex, eGFR, diabetes, hypertension, and anemia predicted RRT effectively (c statistic, 0.91). However, hypertension and age predicted in the opposite direction for mortality and its composite end point. The c statistic decreased: mortality (0.70), mortality and RRT (0.71).

Limitations

Characteristics were measured without a protocol; extensive missing data prevented the evaluation of known risk factors (eg, proteinuria).

Conclusions

Predicting RRT effectively requires a separate risk score. Predicting the composite end point would favor characteristics that predict mortality because it is 7 times as common as RRT.

Index Words: Chronic kidney disease, mortality, end-stage renal disease, dialysis, kidney transplant, cohort study, natural history study, survival analysis, managed care, health maintenance organization

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PII: S0272-6386(07)01035-9

doi:10.1053/j.ajkd.2007.07.006

American Journal of Kidney Diseases
Volume 50, Issue 4 , Pages 559-565, October 2007