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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
IgA nephropathy (IgAN) is the most common primary glomerulonephritis and a leading cause of end-stage kidney disease in young adults. There is a lack of validated diagnostic serum or urinary biomarkers for IgAN, and kidney biopsy remains the gold standard for diagnosis and risk stratification according to the KDIGO 2025 Clinical Practice Guideline. This study aimed to develop and validate a risk prediction model using routine clinical laboratory parameters for the non-invasive diagnosis of IgAN and prediction of the Oxford classification.
This retrospective study analyzed data from 2915 patients with primary glomerular disease confirmed by renal biopsy at a general hospital (2019-2023), including 1364 IgAN and 1551 non-IgAN patients. Patients were chronologically split into a training set (2019-2021; n=1843; 819 IgAN) and an internal validation set (2022-2023; n=1072; 545 IgAN). An external validation set comprised 208 patients from another general hospital (2015-2023; 59 IgAN). Predictor selection was performed in the training set using univariable and LASSO regression. A multivariable logistic regression model was developed and presented as a nomogram. Model performance was evaluated in the validation sets using receiver operating characteristic (ROC) analysis for discrimination, calibration curves for accuracy, and decision curve analysis (DCA) for clinical utility.
Six predictors were ultimately included in the models for IgAN diagnosis and Oxford tubular atrophy/interstitial fibrosis (T) prediction: age, sex, log-transformed IgA (log_IgA), low-density lipoprotein (LDL-C), albumin (ALB), and creatinine-based estimated glomerular filtration rate (eGFRcr). This model for IgAN achieved an area under the curve (AUC) of 0.93 (95%CI 0.91-0.94) in the training set. The model maintained strong performance in the internal validation set (AUC 0.92, 95%CI 0.90-0.94) and the external validation set (AUC 0.92, 95%CI 0.88-0.96). Calibration curves demonstrated good agreement between predicted and observed probabilities. Decision curve analysis indicated a high net clinical benefit across a wide threshold probability range of 0.05 to 0.80. Application of this model for initial screening could theoretically reduce the need for kidney biopsies by 34.2%. Furthermore, the predictive model for the Oxford T-lesion yielded AUCs exceeding 0.85 in both the training and validation sets.
In conclusion, we have developed and validated a novel, non-invasive model utilizing six routinely available clinical parameters. This tool not only accurately diagnoses IgAN but also predicts the presence of chronic Oxford T-lesions, a key prognostic indicator. This simple, cost-effective tool aligns with primary care requirements, serving as a decision-support for early IgAN diagnosis and risk stratification, which could reduce the need for kidney biopsies by over one-third. By integrating diagnosis and pathological typing prediction, it addresses critical clinical gaps and holds promise for optimizing early management of IgAN, supporting broad utility in practice.
