Evidence map›Paper›PMID 42643238›Full record

ArticleFrontiers in nephrology2026

Development and validation of a routine urinalysis-based risk stratification model for IgA nephropathy in a multicenter real-world hospital population.

Chi Wang, Wan He, QingYuan Zheng, Yuan Zhong, Meng Tang, ChunYing Zhang, Yong He

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

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5 · Who and what money

Authors and funding

7 authors.

Chi WangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Wan HeDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
QingYuan ZhengDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yuan ZhongDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Meng TangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
ChunYing ZhangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Yong HeDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide. Routine urinalysis is universally accessible but underutilized for IgAN risk stratification. This study aimed to develop and validate a urinalysis-based risk stratification model for IgAN in a multicenter real-world hospital population. Methods: This retrospective multicenter study was based on routine urinalysis records from 8 hospitals. A total of 354 patients had biopsy-confirmed primary IgAN, and 18,002 controls had no documented IgAN diagnosis after electronic medical record (EMR) review. The dataset was randomly divided into a training cohort (70%) and a hold-out validation cohort (30%). LASSO penalized logistic regression was used for variable selection. Model performance was evaluated by discrimination, calibration, and decision curve analysis (DCA). Bootstrap validation with 1000 resamplings was performed. An 8-variable simplified model was additionally developed. Results: LASSO regression selected 13 variables for the primary full model. The primary model achieved an AUC of 0.849 (95% CI: 0.824-0.873) in the training cohort and 0.861 (95% CI: 0.833-0.890) in the validation cohort. Calibration was good in the training cohort (Brier score = 0.017), while moderate calibration deviation was observed in the validation cohort (Brier score = 0.020). The model yielded a high negative predictive value (>0.99). The 8-variable simplified model retained favorable discrimination with a validation AUC of 0.840. Conclusions: This routine urinalysis-based model may serve as an accessible laboratory-based risk stratification tool for identifying patients with relatively low or elevated predicted probability of IgAN in real-world hospital settings. It should be interpreted as an adjunct to clinical evaluation rather than as a stand-alone diagnostic test or replacement for renal biopsy.

Indexed as

IgA nephropathyLASSO regressionprediction modelreal-world datarisk stratificationroutine urinalysis

Identifiers

PMID42643238
PMCPMC13503150

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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.