The relative predictive ability of four different measures of hemodialysis dose

The amount of hemodialysis that patients receive is an independent predictor of mortality. However, the relative predictive ability of four common measures of dialysis dose (urea reduction ratio, single-pool Kt/V, double-pool Kt/V, and urea index) is unclear.

Using The Renal Network Data System, we identified 14,810 incident hemodialysis patients in Indiana, Kentucky, Ohio, and Illinois from 1997 to 2000. We calculated each measure of hemodialysis dose during the first 6 months of treatment, then prospectively followed up patients for an additional 6 months. For each measure of dialysis dose, we developed a logistic regression model to examine the relationship between dose and patient mortality after adjustment for age, race, sex, cause of renal failure, comorbid conditions, and albumin level. We compared the predictive ability of the four models using the c statistic, a measure of how frequently survivors have a lower predicted probability of death compared with nonsurvivors. C statistics can vary from 0.50 (no predictive ability) to 1.00 (perfect predictive ability).

Of all patients, 11.3% died during follow-up. Mortality was independently associated with low dialysis dose, advanced age, white race, female sex, specific comorbid conditions, and low albumin level. All four predictive models had virtually identical c statistics (range, 0.69 to 0.70).

Models including hemodialysis dose and patient characteristics have a modest ability to predict mortality. Moreover, all four measures of dialysis dose have an equivalent predictive ability. Decisions to use a specific measure should be based on other considerations, such as ease of use, need to troubleshoot inadequate dialysis delivery, or research on urea kinetics or nutritional factors.