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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.
Identifying patients with Diabetic Kidney Disease (DKD) at high risk for rapid progression remains challenging. Our previous research demonstrated that elevated incident serum TNFR1 levels are associated with a more rapid decline in kidney function. Therefore, we aim to develop and validate a novel clinical risk calculator that integrates inflammatory biomarkers with conventional simple clinical parameters to predict rapid decline in estimated glomerular filtration rate (eGFR).
This retrospective cohort study included 92 patients with DKD with stages 2-4 CKD followed up at a tertiary nephrology centre. Rapid eGFR decline was defined as a decline rate >5mL/min/1.73m²/year. A multivariable linear regression model was developed incorporating three predictors: serum tumour necrosis factor receptor-1 (TNFR1), age, and baseline eGFR. The prediction equation was:
Risk Score = 0.857 + (TNFR1 × 0.00005017) - (Age × 0.015) + (eGFR × 0.005)
Risk Percentage = (1 ÷ (1 + e^(-Risk Score)))
e^ = 2.71828^ (Euler's number)
Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and calibration metrics. Patients were stratified into four risk categories based on predicted score: low (<0.25), moderate (0.25-0.50), high (0.50-0.75), and very high (≥0.75).
Among 91 patients (mean age 60.3±13.8 years, median baseline eGFR 34.6 mL/min/1.73m²), 34 (37.4%) experienced rapid eGFR decline >5 mL/min/year. All three predictors were independently associated with rapid decline: TNFR1 (p=0.025), age (p<0.001), and baseline eGFR (p=0.033). The model demonstrated excellent discrimination (AUC 0.850, 95% CI 0.758-0.943) with good calibration and explained 37.7% of variance (R²=0.377, p<0.001). The model also showed excellent performance for predicting decline >10 mL/min/1.73m²/year (AUC 0.859, 95% CI 0.764-0.955). Risk stratification successfully identified high-risk patients across all categories with strong predictive accuracy.
This novel three-factor risk calculator accurately predicts rapid kidney function decline and demonstrates strong discrimination. The integration of TNFR1 with age and baseline eGFR provides robust risk stratification beyond conventional parameters. This practical tool can be easily implemented at the bedside to guide clinical decision-making, optimize nephrology referral timing, and identify patients requiring intensive monitoring and early intervention.
