Evidence map›Paper›PMID 34809582›Full record

ArticleBMC nephrology2021

Evaluation and enhancement of standard equations for renal function estimation in individuals with components of metabolic disease.

Luigi Brunetti, Hyunmoon Back, Sijia Yu, Urma Jalil, Leonid Kagan

Abstract readEvaluation Study
In one paragraph

Article in BMC nephrology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Dose banding - Weighing up benefits, risks and therapeutic failure.British journal of clinical pharmacology · 2022
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Luigi BrunettiDepartment of Pharmacy Practice; Ernest Mario School of Pharmacy; Rutgers, The State University of New Jersey, Piscataway, NJ, USA. brunetti@pharmacy.rutgers.edu.
Hyunmoon BackDepartment of Pharmaceutics; Ernest Mario School of Pharmacy; Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Sijia YuDepartment of Pharmaceutics; Ernest Mario School of Pharmacy; Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
Urma JalilLake Erie College of Osteopathic Medicine, Erie, PA, USA.
Leonid KaganDepartment of Pharmaceutics; Ernest Mario School of Pharmacy; Rutgers, The State University of New Jersey, Piscataway, NJ, USA.

Funding

Mechanisms of obesity-induced changes in drug pharmacokineticsR01GM124046 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI KAGAN, LEONID · 2018 to 2022
$2.6M
NIGMS NIH HHS R01 GM124046
6 · The paper itself

Abstract

backgroundThe primary objective of this study aims to test patient factors, with a focus on cardiometabolic disease, influencing the performance of the Cockcroft-Gault equation in estimating glomerular filtration rate.

methodsA cohort study was performed using data from adult patients with both a 24-h urine creatinine collection and a serum creatinine available. Creatinine clearance was calculated for each patient using the Cockcroft-Gault, Modified Diet in Renal Disease, and Chronic Kidney Disease Epidemiology Collaboration equations and estimates were compared to the measured 24-h urine creatinine clearance. In addition, new prediction equations were developed.

resultsIn the overall study population (n = 484), 44.2% of patients were obese, 44.0% had diabetes, and 30.8% had dyslipidemia. A multivariable model which incorporating patient characteristics performed the best in terms of correlation to measured 24-h urine creatinine clearance, accuracy, and error. The modified Cockcroft-Gault equation using lean body weight performed best in the overall population, the obese subgroup, and the dyslipidemia subgroup in terms of strength of correlation, mean bias, and accuracy.

conclusionsRegardless of strategy used to calculate creatinine clearance, residual error was present suggesting novel methods for estimating glomerular filtration rate are urgently needed.

Indexed as

AgedAge FactorsBody WeightCreatinineFemaleHumansKidneyKidney Function TestsMaleMetabolic Clearance RateMetabolic SyndromeMiddle AgedRetrospective StudiesCreatinineCockcroft-GaultGlomerular filtration rateMetabolic diseaseRenal functionSerum creatinine

Identifiers

PMID34809582
PMCPMC8609865

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Registered trials

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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.