Development and validation of an explainable machine learning model for predicting surgical intervention in pediatric intestinal obstruction

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: AUC 0.988 (internal validation), AUC 0.981 (external validation)

• Primary Outcome: Need for surgical intervention in pediatric intestinal obstruction

• Nnt Or Sensitivity: Sensitivity 89%, Specificity 99% at optimal threshold

• Confidence Interval: Not reported in abstract

Clinical Application

High - uses readily available clinical features and has been developed into a practical clinical tool Highly relevant to Australian pediatric emergency departments and surgical units. Could support standardized decision-making in line with RACS guidelines and reduce inter-hospital variation in surgical timing decisions Children presenting with intestinal obstruction in emergency and surgical settings

Abstract

Pediatric intestinal obstruction, a critical acute abdomen condition, carries a risk of intestinal necrosis. Decisions for urgent surgery lack standardized criteria. This study aimed to develop an explainable machine learning (ML) model to predict surgical indication, reducing diagnostic variability. A retrospective study included 642 children from our center (split into training/internal validation sets) and 137 from two external centers. Surgical indication was defined based on the presence of one or more of the following: evidence of peritonitis on physical examination, radiographic signs of strangulation, or failure of conservative management for > 24 h; in cases of diagnostic uncertainty, consensus among three senior physicians was used as the adjudication standard. We built models using 35 easily obtainable features across eleven algorithms and evaluated performance via AUC and other metrics. SHapley Additive exPlanations (SHAP) provided model interpretability, and an optimal probability threshold was established for clinical stratification. At the optimal threshold determined by maximizing the Youden index, the model achieved a sensitivity of 89% and a specificity of 99% in the internal validation set. The random forest model demonstrated the highest discriminative performance. A streamlined, interpretable model using the eight most important features (including vital sign dynamics and clinical markers) showed no evidence of overfitting based on the close agreement between training and validation performance metrics. It achieved AUCs of 0.988 (internal validation) and 0.981 (external validation). The model has been translated into a convenient clinical tool.Conclusion: Our explainable ML model demonstrates strong discriminative performance in predicting the need for surgery in pediatric intestinal obstruction and is strongly associated with intestinal necrosis. By mitigating the "black-box" concern through SHAP, it offers transparent, interpretable decision support to clinicians.

References

1. 1.Tang, Z., Fu, Y., Zhou, J., Lu, D., & Kang, Q. (2026). Development and validation of an explainable machine learning model for predicting surgical intervention in pediatric intestinal obstruction. European Journal of Pediatrics. https://doi.org/10.1136/bmj.b2393