A predictive model based on routine blood inflammatory markers for IgA vasculitis in children.

Background: IgA vasculitis (IgAV) is the most prevalent systemic vasculitis in children, however, there is still a lack of sensitive biomarkers for its diagnosis. This study aims to analyse the association between blood count-derived inflammatory markers and the onset and progression of pediatric IgAV, thereby providing novel reference criteria for early disease identification and risk warning. Methods: Pediatric patients diagnosed with IgAV at Handan Central Hospital between June 2022 and September 2023 formed the case group, matched with healthy children as the control group. Blood counts were analysed using the Sysmex XN3000 fully automated haematology analyser. Logistic regression screened risk factors and constructed a diagnostic nomogram model, with model efficacy assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Results: Significant differences were observed between the two groups in neutrophil count, platelet count, neutrophil percentage, lymphocyte percentage, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Multivariate regression analysis showed that the combination of NLR and PLR was an independent risk factor for childhood IgAV. A nomogram integrated with age, gender, NLR, and PLR was further constructed. Evaluation via calibration curves and clinical decision curve analysis (DCA) demonstrated that this predictive model has some potential as an auxiliary diagnostic tool for the early detection of the disease. Conclusions: The results of this study indicate that NLR and PLR are significantly associated with the onset of IgAV in children. The combined nomogram model based on these two indicators serves only as a preliminary exploratory tool for a single center. It relies solely on routine complete blood counts for quantitative assessment and can assist clinicians in conducting early, preliminary screening for IgAV.