ArticlePediatric research2026
Machine learning differentiation of abdominal IgA vasculitis without purpura from appendicitis.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Developing a prediction model for poor prognosis in MPA patients using initial admission examination results: a machine learning study from Southwest China.Frontiers in immunology · 2026Article
- Diagnostic value of triglyceride-rich lipoprotein cholesterol for coronary artery lesions and its predictive significance for long-term prognosis in elderly patients with coronary heart disease.Frontiers in cardiovascular medicine · 2025Article
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4 authors.
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Abstract
backgroundAbdominal IgA vasculitis without purpura (IgAVNP) shares clinical manifestations with acute uncomplicated appendicitis (AUA), and traditional diagnostic methods have limitations. Therefore, more accurate diagnostic tools are needed for differentiation.
methodsA retrospective analysis was conducted on 702 children aged 2-14 years who were diagnosed between 2011 and 2020. Variables were selected using LASSO regression, controlling for confounding factors. Eight machine learning (ML) models were constructed and validated using independent data from 2021-2024.
resultsLASSO regression identified constipation, fatigue, hematochezia, as well as PLT, ALB, and AST as key predictive factors. Comparison of ML models revealed that logistic regression (LR) performed the best in the test set (AUC = 0.942, Brier score (BS) = 0.0680), demonstrating high clinical utility, sensitivity, and negative predictive value. The independent validation set further confirmed the stable performance of LR (AUC = 0.942, BS = 0.062). A nomogram based on LR provided a clear visual assessment of disease risk.
conclusionsThe LR model, based on clinical symptoms and laboratory indicators, effectively distinguishes IgAVNP from AUA, with high diagnostic accuracy and significant clinical value. IMPACT: This study effectively distinguishes IgAVNP from AUA using machine learning models, particularly logistic regression, reducing the risk of misdiagnosis. The key factors identified and the nomogram tool can assist in clinical decision-making, optimizing diagnostic and treatment.
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