Evidence map›Paper›PMID 32462139›Full record

ArticleKidney medicine

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

Open access · goldAbstract read
In one paragraph

Article in Kidney medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Observational
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Andrew I ChinDivision of Nephrology, Department of Internal Medicine, University of California, Davis School of Medicine, Sacramento, CA, USA.
Vishwa ShethDivision of Nephrology, Department of Internal Medicine, University of California, Davis School of Medicine, Sacramento, CA, USA.
Jeehyoung KimDepartment of Orthopedic Surgery, Seoul Sacred Heart General Hospital, Seoul, Korea.
Heejung BangDivision of Biostatistics, Department of Public Health Sciences, University of California, Davis School of Medicine, Davis, CA, USA.
University of California, Davis · US

Funding

UC Davis Clinical and Translational Science CenterUL1TR001860 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KENYON, NICHOLAS J., LYLES, COURTNEY REES · 2016 to 2025
$47.3M
NCATS NIH HHS UL1 TR001860
6 · The paper itself

Abstract

RATIONALE AND

objectiveQuantification of residual native kidney function (RKF) is rarely performed in patients on hemodialysis. Methods of estimating residual kidney urea clearance (K STUDY

designRetrospective, predictive modeling and model validation. SETTING AND

participantsInitial timed urine collections in 604 incident in-center hemodialysis patients on thrice weekly treatments from a single academic center where K 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

resultsUrine volume alone was the strongest predictor of K LIMITATIONS: Data were retrospective from a single center, no external validation, not validated in 2 or 4 times weekly hemodialysis patients.

conclusionsEstimation 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.

Indexed as

dialysisESRDEstimation equationhemodialysisprediction modelresidual kidney functionresidual renal functionurea clearance

Identifiers

PMID32462139
PMCPMC7252258
OpenAlexW2981475270

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.