UC Davis

Rationale and objective

Quantification of residual native kidney function (RKF) is rarely performed in patients on hemodialysis. Methods of estimating residual kidney urea clearance (K_RU) that use commonly available laboratory and clinical data, with or without urine volume information, may be useful tools.

Study design

Retrospective, predictive modeling and model validation.

Setting and participants

Initial timed urine collections in 604 incident in-center hemodialysis patients on thrice weekly treatments from a single academic center where K_RU is measured in usual care.

Predictors

Models using combination of serum creatinine and urea, age, weight, height, gender, race, fluid weight gains, and with and without 24-hour urine volume.

Outcomes

Residual kidney urea clearance.

Analytic approach

Generalized linear model was used for model development for K_RU using the first urine collection in 604 patients, as both a continuous and binary outcome (for >2.5 mL/min). Model validation was done by bootstrap resampling of the development cohort and with 1093 follow-up measurements.

Results

Urine volume alone was the strongest predictor of K_RU. The model that included 24-hour urine volume with common clinical data had a high diagnostic accuracy for K_RU >2.5 mL/min (area under the curve 0.91 in both development and bootstrap validation) and R² of 0.56 with outcome as a continuous K_RU value. Our model that did not use urine volume performed less well (e.g., AUC 0.75). Analyses of follow-up urine collections in these same subjects yielded comparable or improved performance.

Limitations

Data were retrospective from a single center, no external validation, not validated in 2 or 4 times weekly hemodialysis patients.

Conclusions

Estimation equations for residual kidney urea clearance that use commonly available data in dialysis clinics, with and without urine volume, may be useful tools for evaluation of hemodialysis patients who still have RKF for individualization of dialysis prescriptions.