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Background <p>Abdominal 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.</p> Methods <p>A 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.</p> Results <p>LASSO 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.</p> Conclusions <p>The LR model, based on clinical symptoms and laboratory indicators, effectively distinguishes IgAVNP from AUA, with high diagnostic accuracy and significant clinical value.</p> Impact <p>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.</p>

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Machine learning differentiation of abdominal IgA vasculitis without purpura from appendicitis

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Gui Na Wang,
  • Zhihua Wang,
  • Huihui Huo,
  • Lingfen Xu

摘要

Background

Abdominal 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.

Methods

A 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.

Results

LASSO 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.

Conclusions

The 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.