Abstract
OBJECTIVE: The risk factors for Henoch-Schönlein purpura nephritis (HSPN) remain largely unclear, particularly in family environment and vaccination. This study aimed to develop a predictive framework to quantify the risk of HSPN by examining family environmental factors and COVID-19 vaccination outcomes in children with Henoch-Schönlein purpura (HSP) in Anhui, China. METHODS: This study retrospectively analyzed 362 children diagnosed with HSP at Anhui Children's Hospital between January 2020 and February 2024. A questionnaire was designed to collect information from enrolled children. For patients with incomplete medical records, parents were contacted via phone or the questionnaire was sent to them to complete the survey. After data collection, the patients were split randomly into a training group and a validation group at a 7:3 ratio, univariate and multivariate logistic regression analyses were performed to identify risk factors for nephritis, and a nomogram was constructed from these factors to provide a visual prediction of the likelihood of nephritis in HSP. The nomogram's performance was evaluated in both the training and validation groups using the area under the receiver operating characteristic (AUC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: The study identified family income/month, age of onset, BMI, number of recurrences, and COVID-19 vaccination status as independent risk factors for HSPN. A nomogram was subsequently developed afterward using these factors. In the training group, the nomogram achieved an area under the curve (AUC) of 0.83 (95% CI: 0.78-0.88), while in the validation group, the AUC was 0.90 (95% CI: 0.84-0.96), demonstrating strong predictive performance. The calibration curve showed that the nomogram's predictions were well-aligned with the actual outcomes. Additionally, DCA indicated that the nomogram provided considerable clinical net benefit. CONCLUSION: The nomogram offers accurate risk prediction for nephritis in children with HSP, helping healthcare professionals identify high-risk patients early and make informed clinical decisions.