ArticlePeerJ2024
Development and internal and external validation of a nomogram model for predicting the risk of chronic kidney disease progression in IgA nephropathy patients.
Article in PeerJ, 2024. 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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of hematuria on long term kidney outcomes in adults with immunoglobulin A nephropathy: a systematic literature review and meta-analysis.Frontiers in nephrology · 2026Pooled it
- Risk prediction in IgA nephropathy: from conventional models to machine learning, deep learning, and precision nephrology.Renal failure · 2026Review
- Development and validation of a nomogram for predicting advanced chronic kidney disease.iScience · 2026Article
- Identification of key genes related to arginine metabolism in immunoglobulin A nephropathy through the combination of scRNA-seq and bulk RNA-seq data.Frontiers in immunology · 2026Article
- Development and preliminary validation of a predictive model for IgA nephropathy progression.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: IgA nephropathy (IgAN) is the most common primary glomerular disease in chronic kidney disease (CKD), exhibiting significant heterogeneity in both clinical and pathological presentations. We aimed to explore the risk factors influencing short-term prognosis (≥90 days) and to construct a nomogram model for evaluating the risk of CKD progression in IgAN patients. Methods: Clinical and pathological data of patients diagnosed with IgAN through biopsy at two centers were retrospectively collected. Logistic regression was employed to analyze the training cohort dataset and identify the independent predictors to construct a nomogram model based on the final variables. The predictive model was validated both internally and externally, with its performance assessed using the area under the curve (AUC), calibration curves, and decision curve analysis. Results: Out of the patients in the modeling group, 129 individuals (41.6%) did not achieve remission following 3 months of treatment, indicating a high risk of CKD progression. A multivariate logistic regression analysis demonstrated that body mass index, urinary protein excretion, and tubular atrophy/interstitial fibrosis were identified as independent predictors for risk stratification. A nomogram model was formulated utilizing the final variables. The AUCs for the training set, internal validation set, and external validation set were 0.746 (95% confidence intervals (CI) [0.691-0.8]), 0.764 (95% CI [0.68-0.85]), and 0.749 (95% CI [0.65-0.85]), respectively. The validation of the subgroup analysis also demonstrated a satisfactory AUC. Conclusion: This study developed and validated a practical nomogram that can individually predict short-term treatment outcomes (≥90 days) and the risk of CKD progression in IgAN patients. It provides reliable guidance for timely and personalized intervention and treatment strategies.
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