Evidence map›Paper›PMID 26885183›Full record

ArticleInternational journal of clinical and experimental medicine2015

The Chronic Kidney Disease Epidemiology Collaboration equation improves the detection of hyperfiltration in Chinese diabetic patients.

Fangya Zhao, Lei Zhang, Junxi Lu, Kaifeng Guo, Mian Wu, Haoyong Yu, Mingliang Zhang, Yuqian Bao, Haibing Chen, Weiping Jia

Open access · greenAbstract read
PubMed Full text at PMC
In one paragraph

Article in International journal of clinical and experimental medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 19% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment.Journal of the American Society of Nephrology : JASN · 2017
    Review
  10. 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

10 authors at 1 institution in 1 country.

Fangya ZhaoDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Lei ZhangDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Junxi LuDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Kaifeng GuoDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Mian WuDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Haoyong YuDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Mingliang ZhangDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Yuqian BaoDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Haibing ChenDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Weiping JiaDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiaotong University Affiliated Sixth People's Hospital Shanghai 200233, China.
Shanghai Sixth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveHyperfiltration confers an increased risk of diabetic nephropathy. Early detection can ensure timely intervention and improved treatment outcomes. Because GFR is known to be affected by hyperglycemia, the aim of this study was to compare the influence of hyperglycemia on GFR estimations calculated by the CKD-EPI equation, the CG equation, and the MDRD equations in estimating hyperfiltration in Chinese diabetic patients. MATERIALS AND

methodsThe performance of the equations, compared with the measured (99)mTc-DTPA glomerular filtration rate was analyzed in 3492 diabetic patients. Bias, precision, and accuracies were compared with respect to HbA1c status. The Bland-Altman method was used to evaluate the agreement among the equations with respect to the mGFR, and the receiver-operating characteristic curve method was used to evaluate diagnostic value of the three equations with respect to the detection of moderate renal failure and hyperfiltration.

resultsThe mean absolute bias was the smallest for the CKD-EPI equation in the HbA1c < 7.2% cohort, and the highest accuracy within ± 15% and ± 30% was also reached with the CKD-EPI equation in both cohorts. For the detection of hyperfiltration, the CKD-EPI equation exhibited the best performance with the greatest combination of sensitivity and specificity. The biases of the three equations were significantly higher in the HbA1c ≥ 10.5% subgroup compared with the HbA1c < 7.2% cohort.

conclusionThe CKD-EPI equation can be used as a screening tool for hyperfiltration and appears to be a more generalizable and accurate equation for estimating GFR in Chinese diabetic patients.

Indexed as

Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equationDiabetic nephropathyhyperfiltration

Identifiers

PMID26885183
PMCPMC4729969
OpenAlexW2282001398

What OpenQuestion holds

Textmetadata
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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.