Evidence map›Paper›PMID 29254270›Full record

ArticleOncotarget2017

A non-laboratory-based risk score for predicting diabetic kidney disease in Chinese patients with type 2 diabetes.

Mian Wu, Junxi Lu, Lei Zhang, Fengjing Liu, Si Chen, Ying Han, Fangya Zhao, Kaifeng Guo, Yuqian Bao, Haibing Chen and 1 more

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

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 4 pooled it
1.3field-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

8 citing papers in PubMed, 4 syntheses or guidelines pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  4. Pooled it
  5. Article
  6. Article
  7. Article
  8. Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019
    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

11 authors at 2 institutions in 1 country.

Mian Wu *Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Junxi Lu *Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Lei ZhangShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Fengjing LiuShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Si ChenShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Ying HanShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Fangya ZhaoShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Kaifeng GuoShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Yuqian BaoShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Haibing ChenShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Weiping JiaShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Shanghai Sixth People's Hospital · CNShanghai Eighth People Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo construct a simple screening tool for predicting diabetic kidney disease in Chinese patients with type 2 diabetes. MATERIALS AND

methodsIn the development cohort, the clinical and procedural characteristics of the 4,795 patients were considered as candidate univariate predictors of diabetic kidney disease. The β-coefficients derived from a multiple logistic regression model predicting the presence of DKD were used to calculate the risk score. The performance of the risk score was validated in a cross-sectional and a prospective cohort population.

resultsThe risk score included sex, body mass index, systolic blood pressure, and duration of diabetes. The total point ranged from 0 to 39. In the development cohort, compared with participants with risk score < 10, those with risk score between 10 to 20, 21 to 30, and > 30 had ORs of 3.21, 7.92 and 17.55 for developing diabetic kidney disease, respectively. In the prospective cohort, 60.9% patients with risk score over 30 were expected to develop DKD at 72 months of follow-up.

conclusionsSex, body mass index, systolic blood pressure, and duration of diabetes were independent predictors of diabetic kidney disease, and the derived risk equation was a simple screening tool for screening diabetic kidney disease in Chinese patients with type 2 diabetes.

Indexed as

chinesediabetic kidney diseaseprognosisrisk factorstype 2 diabetes

Identifiers

PMID29254270
PMCPMC5731980
OpenAlexW2762805068

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.