Estimating Residual Native Kidney Urea Clearance in Hemodialysis Patients With and Without 24-Hour Urine Volume

Andrew I. Chin, Vishwa Sheth, Jeehyoung Kim, Heejung Bang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Rationale & Objective: Quantification of residual native kidney function is rarely performed in patients receiving hemodialysis. Methods of estimating residual kidney urea clearance 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 & Participants: Initial timed urine collections in 604 incident in-center hemodialysis patients on thrice-weekly treatments from a single academic center in which residual kidney urea clearance is measured in usual care. Predictors: Models using a combination of serum creatinine and urea levels, age, weight, height, sex, 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 residual kidney urea clearance 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 1,093 follow-up measurements. Results: Urine volume alone was the strongest predictor of residual kidney urea clearance. The model that included 24-hour urine volume with common clinical data had high diagnostic accuracy for residual kidney urea clearance > 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 residual kidney urea clearance value. Our model that did not use urine volume performed less well (eg, area under the curve, 0.75). Analyses of follow-up urine collections in these same participants 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 residual kidney function for individualization of dialysis prescriptions.

Original languageEnglish (US)
Pages (from-to)376-382
Number of pages7
JournalKidney Medicine
Volume1
Issue number6
DOIs
StatePublished - Nov 1 2019

Fingerprint

Renal Dialysis
Urea
Urine
Kidney
Urine Specimen Collection
Area Under Curve
Dialysis
Weight Gain
Prescriptions
Linear Models
Creatinine
Retrospective Studies
Weights and Measures
Serum

Keywords

  • dialysis
  • ESRD
  • Estimation equation
  • hemodialysis
  • prediction model
  • residual kidney function
  • residual renal function
  • urea clearance

ASJC Scopus subject areas

  • Internal Medicine
  • Nephrology

Cite this

Estimating Residual Native Kidney Urea Clearance in Hemodialysis Patients With and Without 24-Hour Urine Volume. / Chin, Andrew I.; Sheth, Vishwa; Kim, Jeehyoung; Bang, Heejung.

In: Kidney Medicine, Vol. 1, No. 6, 01.11.2019, p. 376-382.

Research output: Contribution to journalArticle

Chin, Andrew I. ; Sheth, Vishwa ; Kim, Jeehyoung ; Bang, Heejung. / Estimating Residual Native Kidney Urea Clearance in Hemodialysis Patients With and Without 24-Hour Urine Volume. In: Kidney Medicine. 2019 ; Vol. 1, No. 6. pp. 376-382.
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abstract = "Rationale & Objective: Quantification of residual native kidney function is rarely performed in patients receiving hemodialysis. Methods of estimating residual kidney urea clearance 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 & Participants: Initial timed urine collections in 604 incident in-center hemodialysis patients on thrice-weekly treatments from a single academic center in which residual kidney urea clearance is measured in usual care. Predictors: Models using a combination of serum creatinine and urea levels, age, weight, height, sex, 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 residual kidney urea clearance 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 1,093 follow-up measurements. Results: Urine volume alone was the strongest predictor of residual kidney urea clearance. The model that included 24-hour urine volume with common clinical data had high diagnostic accuracy for residual kidney urea clearance > 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 residual kidney urea clearance value. Our model that did not use urine volume performed less well (eg, area under the curve, 0.75). Analyses of follow-up urine collections in these same participants 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 residual kidney function for individualization of dialysis prescriptions.",
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N2 - Rationale & Objective: Quantification of residual native kidney function is rarely performed in patients receiving hemodialysis. Methods of estimating residual kidney urea clearance 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 & Participants: Initial timed urine collections in 604 incident in-center hemodialysis patients on thrice-weekly treatments from a single academic center in which residual kidney urea clearance is measured in usual care. Predictors: Models using a combination of serum creatinine and urea levels, age, weight, height, sex, 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 residual kidney urea clearance 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 1,093 follow-up measurements. Results: Urine volume alone was the strongest predictor of residual kidney urea clearance. The model that included 24-hour urine volume with common clinical data had high diagnostic accuracy for residual kidney urea clearance > 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 residual kidney urea clearance value. Our model that did not use urine volume performed less well (eg, area under the curve, 0.75). Analyses of follow-up urine collections in these same participants 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 residual kidney function for individualization of dialysis prescriptions.

AB - Rationale & Objective: Quantification of residual native kidney function is rarely performed in patients receiving hemodialysis. Methods of estimating residual kidney urea clearance 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 & Participants: Initial timed urine collections in 604 incident in-center hemodialysis patients on thrice-weekly treatments from a single academic center in which residual kidney urea clearance is measured in usual care. Predictors: Models using a combination of serum creatinine and urea levels, age, weight, height, sex, 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 residual kidney urea clearance 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 1,093 follow-up measurements. Results: Urine volume alone was the strongest predictor of residual kidney urea clearance. The model that included 24-hour urine volume with common clinical data had high diagnostic accuracy for residual kidney urea clearance > 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 residual kidney urea clearance value. Our model that did not use urine volume performed less well (eg, area under the curve, 0.75). Analyses of follow-up urine collections in these same participants 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 residual kidney function for individualization of dialysis prescriptions.

KW - dialysis

KW - ESRD

KW - Estimation equation

KW - hemodialysis

KW - prediction model

KW - residual kidney function

KW - residual renal function

KW - urea clearance

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