An individualized prognostic nomogram integrating clinical and pathological features in pediatric IgA vasculitis nephritis
Year: 2026
Type: article
Study design: Observational Study
Abstract: Objective To develop and validate an individualized prognostic nomogram integrating clinical and pathological features for estimating the risk of renal function decline in children with IgA vasculitis nephritis (IgAVN). Methods In this single-center retrospective cohort, 603 children with biopsy-confirmed IgAVN and ≥12 months of follow-up were included. The primary endpoint was a composite of eGFR <90 mL/min/1.73 m 2 or a ≥ 30% decline from baseline, first occurring after 12 months of follow-up. The cohort was randomly split into training (80%) and validation (20%) sets. Variable selection was performed using elastic-net regression with five-fold cross-validation, followed by backward stepwise Cox proportional hazards modelling. Results Over a median follow-up of 48.7 months (IQR: 28.5–73.3 months), 68 patients (11.3%) reached the endpoint. The final model identified five independent predictors: male sex (HR = 2.12), renal IgA deposit 3+ (HR = 2.66), ISKDC grade IV–VI (HR = 4.63), Oxford T1/2 lesions (HR = 4.72), and baseline serum creatinine. The model showed strong and consistent discrimination in the validation set ( C -index = 0.796; 60 month AUC = 0.859; 84 month AUC = 0.850), adequate calibration, and provided positive net clinical benefit on decision curve analysis. A practical nomogram was developed for risk estimation. Kaplan-Meier analysis demonstrated significantly lower event-free survival in the high-risk group (log-rank p < 0.001). Conclusion We successfully developed and validated an individualized prognostic nomogram. This tool integrates key clinical and pathological features to quantify the risk of renal function decline in children with IgAVN shortly after renal biopsy, providing a basis for personalized management decisions.
Source: Frontiers in Medicine
Authors: Yueheng Gan, Li Xiao, Shaojun Li, Anshuo Wang, Sijie Yu, Xuejun Yang, Mo Wang, Qiu Li, Han Chan, Haiping Yang
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: G2172462752
OpenAlex ID: W7135095007
