Evidence map›Paper›PMID 42026889›Full record

ArticleRenal failure2026

Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.

Zhongqing Zhou, Fei Sheng, Jiazhe Hou, Jing Yang, Dongjian Xu, Zhiping Shen, Hengjing Wu, Lijuan Zhang

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Renal failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zhongqing ZhouHealth Management Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Fei ShengCommunity Health Service Center of Nanxiang Town, School of Medicine, Tongji University, Shanghai, China.
Jiazhe HouHealth Management Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Jing YangHealth Management Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Dongjian XuCommunity Health Service Center of Nanxiang Town, School of Medicine, Tongji University, Shanghai, China.
Zhiping ShenCommunity Health Service Center of Anting Town, School of Medicine, Tongji University, Shanghai, China.
Hengjing WuHealth Management Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Lijuan ZhangHealth Management Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperglycemia is a major risk factor for chronic kidney disease (CKD). This multicenter prospective study developed and validated a machine learning (ML) model to predict CKD risk in prediabetic and diabetic populations for early intervention, following TRIPOD+AI guidelines. Participants were enrolled from four communities, with three sites providing training (80%) and internal test (20%) datasets, and the fourth for external validation. Five ML algorithms were constructed, and SHapley Additive exPlanations (SHAP) was applied to interpret the optimal model. The XGBoost model showed excellent predictive performance, with AUCs of 0.905, 0.809, and 0.837 in training, internal test, and external validation sets, respectively. Serum creatinine (Scr), age, and hemoglobin (Hb) were the leading predictors, with higher Scr, older age, and lower Hb elevating CKD risk. Risk stratification (low: 0%-5%, medium: 5%-25%, high: 25%-100%) yielded distinct CKD incidences of 0.7%, 9.9%, and 55.5% (

Indexed as

HyperglycemiaMachine LearningRenal Insufficiency, ChronicAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsCreatinineDisease ProgressionFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsProspective StudiesRisk AssessmentCreatininechronic kidney diseasecommunity screeningHyperglycemiamachine learningrisk predictionSHAP

Identifiers

PMID42026889
PMCPMC13112873

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.