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Purpose <p>To develop a systematic and efficient decision tree analysis (DTA) model to improve the diagnostic accuracy of transient small-bowel intussusception (TSBI) and persistent small-bowel intussusception (PSBI) in children.</p> Methods <p>From February 2019 to June 2022, ultrasound (US) features and clinical findings of pediatric patients with small-bowel intussusception (SBI)—including SBI diameter, outer bowel wall thickness, thickness of the head and body of the intussusceptum, length of the intussusceptum, and presence of pathological lead points (PLPs)—were recorded and analyzed. A classification and regression tree algorithm was then used to develop a DTA model, which was trained and validated by randomly categorizing the patients into training (60%, 200/331) and validation (40%, 131/331) datasets to assess diagnostic performance.</p> Results <p>A total of 331 patients with SBI (270 with TSBI and 61 with PSBI) were included; the maximum age was 9&#xa0;years. The initial diagnostic predictor in the DTA model was the detection of a PLP via US, followed by intussusceptum length (<i>P</i> &lt; 0.001). The sensitivity, specificity, and accuracy of the DTA model were 98.2%, 100%, and 98.6%, respectively.</p> Conclusion <p>The DTA model developed in this study facilitated the differential diagnosis of TSBI and PSBI in pediatric patients with SBI, with a clinical concordance rate of 98.6%.</p>

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Transient and persistent small-bowel intussusception in children: a decision tree analysis model based on ultrasound and clinical findings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月24日

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  • Shao Wang,
  • Yu Wang,
  • Liqun Jia,
  • Xiaoman Wang

摘要

Purpose

To develop a systematic and efficient decision tree analysis (DTA) model to improve the diagnostic accuracy of transient small-bowel intussusception (TSBI) and persistent small-bowel intussusception (PSBI) in children.

Methods

From February 2019 to June 2022, ultrasound (US) features and clinical findings of pediatric patients with small-bowel intussusception (SBI)—including SBI diameter, outer bowel wall thickness, thickness of the head and body of the intussusceptum, length of the intussusceptum, and presence of pathological lead points (PLPs)—were recorded and analyzed. A classification and regression tree algorithm was then used to develop a DTA model, which was trained and validated by randomly categorizing the patients into training (60%, 200/331) and validation (40%, 131/331) datasets to assess diagnostic performance.

Results

A total of 331 patients with SBI (270 with TSBI and 61 with PSBI) were included; the maximum age was 9 years. The initial diagnostic predictor in the DTA model was the detection of a PLP via US, followed by intussusceptum length (P < 0.001). The sensitivity, specificity, and accuracy of the DTA model were 98.2%, 100%, and 98.6%, respectively.

Conclusion

The DTA model developed in this study facilitated the differential diagnosis of TSBI and PSBI in pediatric patients with SBI, with a clinical concordance rate of 98.6%.