Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.

Accurate identification of early pediatric abdominal sepsis (PAS) is essential to improving outcomes, yet most existing pediatric sepsis criteria and scoring tools primarily focus on cardiopulmonary dysfunction and overlook early intra-abdominal infections. To address this gap, we combined the real-world data with explainable machine learning to develop the Abdominal Sepsis Diagnosis model (ABSeD) for clinical decision support. The model construction used the retrospective data from 6566 pediatric patients who were admitted to the Children's Hospital, Zhejiang University School of Medicine from 2019 to 2023. Prospective data from 308 recruited patients across seven independent hospitals collected between January and March 2025 served as an external validation cohort. PAS status was determined through consensus or by reviewing laparoscopic surgery records. Multiple machine learning algorithms were compared, and the optimal model was further refined by hyper-parameter tuning. The ABSeD model, integrating nine routine clinical variables, demonstrated high diagnostic accuracy (training set: AUC = 0.934, 95% CI: [0.912, 0.950]; accuracy = 0.870, precision = 0.910), and robust multicenter generalizability (AUC = 0.928, 95% CI: [0.895, 0.961]; accuracy = 0.873, precision = 0.924). This model offers an explainable and practical digital tool for early detection of PAS, with potential to enhance timely intervention in hospitalized children with suspected or clinically identified intra-abdominal septic pathology.