Evidence map›Paper›PMID 32740698›Full record

ArticleClinical and experimental nephrology2020

Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence.

Qiongjing Yuan, Haixia Zhang, Yanyun Xie, Wei Lin, Liangang Peng, Liming Wang, Weihong Huang, Song Feng, Xiangcheng Xiao

Abstract read
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In one paragraph

Article in Clinical and experimental nephrology, 2020. 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
1.4field-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

8 citing papers in PubMed, 4 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Article
  7. Observational
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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

9 authors at 1 institution in 1 country.

Qiongjing YuanDepartment of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Haixia ZhangDepartment of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Yanyun XieDepartment of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Wei LinDepartment of Pathology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China.
Liangang PengChangsha Aeronautical Vocational and Technical College, Changsha, 410014, Hunan, China.
Liming WangBitvalue Technology (Hunan) Company Limited, Xiangjiang Road, Changsha, 410082, China.
Weihong HuangMobile Health Ministry of Education-China Mobile Joint Laboratory, Xiangya Hospital, Central South University, Changsha, 410008, China.
Song FengNetwork Information Center, Xiangya Hospital, Central South University, Xiangya Road, Changsha, 410008, China. fs205@sina.com.
Xiangcheng XiaoDepartment of Nephrology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China. xiaoxc@csu.edu.cn.ORCID http://orcid.org/0000-0003-2515-5503
Central South University · CN

Funding

Natural Science Foundation of Hunan Province 2018JJ5056
6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m

methodsWe used a computational intelligence model (CKD stage 3 Modeling, CSM) to distinguish CKD stage 3 with CKD stage 3a/3b by data distribution rules, pearson correlation coefficient (PCC), spearman correlation (SCC) analysis, logistic regression (LR), random forest (RF), support vector machine (SVM), and neural network (Nnet) to develop Prognostic Model for patients with CKD stage 3a/3b in South Central China. Furthermore, we used RF to discover risk factors of progression of CKD stage 3a and 3b to CKD stage 5. 1090 cases of CKD stage 3 patients in Xiangya Hospital were collected. Among them, 455 patients progressed to CKD stage 5 in a median follow-up of 4 years (IQR 4.295, 4.489).

resultsWe found that the common risk factors for progression of CKD stage 3a/3b to CKD stage 5 included albumin, creatinine, total protein, etc. Proteinuria, direct bilirubin, hemoglobin, etc. accounted for the progression from stage CKD stage 3a to stage 5. The risk factors for CKD stage 3b progression to stage 5 included low-density lipoprotein cholesterol, diabetes, eosinophil percentage, etc.

conclusionsCSM could be used as a point-of-care test to screen patients at high risk for disease progression, might allowing individualized therapeutic management.

Indexed as

Models, StatisticalNeural Networks, ComputerSupport Vector MachineAdultChinaDisease ProgressionFemaleFollow-Up StudiesGlomerular Filtration RateHumansKidney Failure, ChronicMaleMiddle AgedPrognosisRegression AnalysisRenal Insufficiency, ChronicChronic kidney diseaseCKD stage 3 modelingComputational intelligenceEnd-stage renal disease

Identifiers

PMID32740698
OpenAlexW3046236278

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.