Improved equation for estimating single-pool Kt/V at higher dialysis frequencies

Nephrol Dial Transplant. 2013 Aug;28(8):2156-60. doi: 10.1093/ndt/gfs115. Epub 2012 May 4.


Rationale: To measure adequacy in patients dialyzed other than three times per week, guidelines recommend the use of 'standard' Kt/V, which commonly is estimated from treatment Kt/V, time and frequency; however, the accuracy of equations that predict treatment Kt/V in patients being dialyzed other than three times per week has not been evaluated.

Methods: In patients enrolled in the Frequent Hemodialysis Network (FHN) Daily and Nocturnal Trials who were being dialyzed three, four or six times per week, we tested the accuracy of the following Kt/V prediction equation: Kt/V = -ln(R - GFAC × T_hours) + (4-3.5 × R) × 0.55 × weight loss/V, where R = post-dialysis/pre-dialysis blood urea nitrogen and GFAC, originally set to 0.008 for a 3/week schedule (Daugirdas, J Am Soc Nephrol 1993), is a factor that adjusts for urea generation.

Results: With the above equation, there was <0.1% mean error in predicted treatment Kt/V for 3/week patients, but mean errors were -5, -9 and -13% for the 6/week daily, 4/week nocturnal and 6/week nocturnal patients. Modeling simulations were performed to optimize the GFAC term for dialysis schedule and length of the preceding interdialysis interval (PIDI). After substituting schedule- and interval-optimized GFAC terms, the treatment Kt/V prediction errors were reduced to -0.81, +0.1 and -1.3% for the three frequent dialysis schedules tested.

Conclusion: For frequent dialysis schedules, the urea generation factor (GFAC) of one commonly used Kt/V prediction equation should be adjusted based on length in days of the PIDI and number of treatments per week.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers / analysis*
  • Glomerular Filtration Rate
  • Humans
  • Kidney Failure, Chronic / therapy*
  • Kidney Function Tests
  • Kinetics
  • Models, Biological*
  • Prognosis
  • Renal Dialysis / statistics & numerical data*
  • Urea / analysis*


  • Biomarkers
  • Urea