Evidence map›Paper›PMID 37653403›Full record

ArticleBMC medical informatics and decision making2023

Risk factor mining and prediction of urine protein progression in chronic kidney disease: a machine learning- based study.

Yufei Lu, Yichun Ning, Yang Li, Bowen Zhu, Jian Zhang, Yan Yang, Weize Chen, Zhixin Yan, Annan Chen, Bo Shen and 4 more

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

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% 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, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. 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

14 authors at 3 institutions in 1 country.

Yufei Lu *Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Yichun Ning *Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Yang LiDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Bowen ZhuDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Jian ZhangDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Yan YangDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Weize ChenDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Zhixin YanDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Annan ChenDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Bo ShenDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Yi FangDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China.
Dong WangSchool of Computer Science & Information Engineering, Shanghai Institute of Technology, Shanghai, China. dongwang@sit.edu.cn.
Nana SongDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China. nanasong1129@163.com.
Xiaoqiang DingDepartment of Nephrology, Zhongshan Hospital, Fudan University, Shanghai Clinical Research Center for Kidney Disease, Shanghai Medical Center of Kidney, Shanghai Institute of Kidney and Dialysis, Shanghai Key Laboratory of Kidney and Blood Purification, Hemodialysis Quality Control Center of Shanghai, Shanghai, China. ding.xiaoqiang@zs-hospital.sh.cn.
Shanghai Clinical Research Center · CNFudan University · CNShanghai Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) is a global public health concern. Therefore, to provide timely intervention for non-hospitalized high-risk patients and rationally allocate limited clinical resources is important to mine the key factors when designing a CKD prediction model.

methodsThis study included data from 1,358 patients with CKD pathologically confirmed during the period from December 2017 to September 2020 at Zhongshan Hospital. A CKD prediction interpretation framework based on machine learning was proposed. From among 100 variables, 17 were selected for the model construction through a recursive feature elimination with logistic regression feature screening. Several machine learning classifiers, including extreme gradient boosting, gaussian-based naive bayes, a neural network, ridge regression, and linear model logistic regression (LR), were trained, and an ensemble model was developed to predict 24-hour urine protein. The detailed relationship between the risk of CKD progression and these predictors was determined using a global interpretation. A patient-specific analysis was conducted using a local interpretation.

resultsThe results showed that LR achieved the best performance, with an area under the curve (AUC) of 0.850 in a single machine learning model. The ensemble model constructed using the voting integration method further improved the AUC to 0.856. The major predictors of moderate-to-severe severity included lower levels of 25-OH-vitamin, albumin, transferrin in males, and higher levels of cystatin C.

conclusionsCompared with the clinical single kidney function evaluation indicators (eGFR, Scr), the machine learning model proposed in this study improved the prediction accuracy of CKD progression by 17.6% and 24.6%, respectively, and the AUC was improved by 0.250 and 0.236, respectively. Our framework can achieve a good predictive interpretation and provide effective clinical decision support.

Indexed as

HospitalsUrinalysisArea Under CurveBayes TheoremHumansMachine LearningMaleChronic kidney diseaseClinical decision supportMachine learningModel interpretation

Identifiers

PMID37653403
PMCPMC10472702
OpenAlexW4386325368

What OpenQuestion 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 OpenQuestion graph.