ArticleEBioMedicine2020
Identification and external validation of IgA nephropathy patients benefiting from immunosuppression therapy.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 18 citations in OpenAlex.
- Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.Chinese medical journal · 2026Article
- Guiding immunotherapy based on the oxford classification activity score in IgA nephropathy.Frontiers in endocrinology · 2026Article
- Construction of LncRNA-miRNA-mRNA regulatory network in IgA nephropathy mice model by RNA-sequencing and bioinformatics analysis.Genes & genomics · 2025Article
- Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.BMC nephrology · 2025Article
- A prognostic model of immunoglobulin A nephropathy using artificial neural network: a retrospective study based on integrated Chinese and Western Medicine.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- Long-term evaluation of the timing of corticosteroid therapy in an IgA nephropathy cohort.Clinical kidney journal · 2025Article
- Urinary exosomal miR-320b as a biomarker of tubular atrophy/interstitial fibrosis in IgA nephropathy patients.American journal of translational research · 2025Article
- Development and Validation of a Machine Learning-Based Prognostic Model for IgA Nephropathy with Chronic Kidney Disease Stage 3 or 4.Kidney diseases (Basel, Switzerland) · 2024Article
- Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.Critical care medicine · 2024Review
- Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis.Heliyon · 2024Review
- Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning.PloS one · 2022Article
- A Deep Insight Into Regulatory T Cell Metabolism in Renal Disease: Facts and Perspectives.Frontiers in immunology · 2022Review
- How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?Seminars in immunopathology · 2021Review
- Reduction in proteinuria after immunosuppressive therapy and long-term kidney outcomes in patients with immunoglobulin A nephropathy.The Korean journal of internal medicine · 2021Article
- The prognostic value of platelet-to-lymphocyte ratio on the long-term renal survival in patients with IgA nephropathy.International urology and nephrology · 2021Observational
- Glomerular Immune Deposition in MPO-ANCA Associated Glomerulonephritis Is Associated With Poor Renal Survival.Frontiers in immunology · 2021Article
- Zhen-Wu-Tang Protects IgA Nephropathy in Rats by Regulating Exosomes to Inhibit NF-κB/NLRP3 Pathway.Frontiers in pharmacology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough IgA nephropathy (IgAN), an immune-mediated disease with heterogeneous clinical and pathological phenotypes, is the most common glomerulonephritis worldwide, it remains unclear which IgAN patients benefit from immunosuppression (IS) therapy.
methodsClinical and pathological data from 4047 biopsy-proven IgAN patients from 24 renal centres in China were included. The derivation and validation cohorts were composed of 2058 and 1989 patients, respectively. Model-based recursive partitioning, a machine learning approach, was performed to partition patients in the derivation cohort into subgroups with different IS long-term benefits, associated with time to end-stage kidney disease, measured by adjusted Kaplan-Meier estimator and adjusted hazard ratio (HR) using Cox regression.
findingsThree identified subgroups obtained a significant IS benefits with HRs ≤ 1. In patients with serum creatinine ≤ 1·437 mg/dl, the benefits of IS were observed in those with proteinuria > 1·525 g/24h (node 6; HR = 0·50; 95% CI, 0·29 to 0·89; P = 0·02), especially in those with proteinuria > 2·480 g/24h (node 8; HR = 0·23; 95% CI, 0·11 to 0·50; P <0·001). In patients with serum creatinine > 1·437 mg/dl, those with high proteinuria and crescents benefitted from IS (node 12; HR = 0·29; 95% CI, 0·09 to 0·94; P = 0·04). The treatment benefits were externally validated in the validation cohort.
interpretationMachine learning could be employed to identify subgroups with different IS benefits. These efforts promote decision-making, assist targeted clinical trial design, and shed light on individualised treatment in IgAN patients.
fundingNational Key Research and Development Program of China (2016YFC0904103), National Key Technology R&D Program (2015BAI12B02).
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