Objective To summarize the research progress on risk factors and predictive models (including scoring systems) related to surgical decision-making in acute small bowel obstruction (ASBO), and to provide a reference for constructing an evidence-based decision-making framework in clinical practice. Method The recent domestic and international literature on surgical decision-making for ASBO and the prediction of intestinal ischemia, with a focus on summarizing research progress regarding single clinical and radiological predictors, as well as multivariable predictive models (including scoring systems). Results Traditional decision-making relying on single clinical indicators (such as peritoneal irritation signs or systemic inflammatory response syndrome) or laboratory markers (such as C-reactive protein or lactate) provides limited predictive value and is unreliable for guiding management. Enhanced computed tomography (CT) serves as the cornerstone for evaluating bowel wall perfusion, with decreased bowel wall enhancement being one of the most specific signs for predicting irreversible ischemia. However, the sensitivity of any single CT finding is limited, necessitating a comprehensive assessment that integrates various direct and indirect signs, such as the “whirl sign” “beak sign”, mesenteric fluid, pneumatosis intestinalis. To overcome the limitations of individual indicators, research has shifted towards constructing integrated multivariable predictive models. Early models, such as the intestinal ischemia score based on Eastern Association for the Surgery of Trauma guidelines, attempted to quantify guideline recommendations. For specific predominant etiologies like adhesive ASBO, dedicated models have emerged, exemplified by the Komatsu score (based on clinical indicators) and the Bouassida score (integrating clinical and imaging indicators), which have significantly improved predictive accuracy. Further advancing the field, comprehensive models like the Schwenter score and Huang’s model have achieved more refined risk stratification and flexible selection of decision thresholds by incorporating a broader set of variables and utilizing continuous scoring designs. Currently, multimodal models that combine artificial intelligence (e.g., deep learning) for analyzing CT imaging features demonstrate superior predictive performance, representing the cutting-edge direction in this field. Conclusions Surgical decision-making for ASBO has evolved from reliance on empirical univariate indicators to multi-dimensional comprehensive assessment. CT acts as the core evaluation tool, while comprehensive scoring systems are valuable adjunctive tools to quantify clinical decision-making risks. Future research priorities should focus on developing universally applicable predictive models based on non-contrast CT, conducting prospective multicenter external validations, and exploring the combined application of artificial intelligence and novel biomarkers, aiming to achieve precision and individualized clinical decision-making for ASBO.

Citation： SHI Liangliang, LI Zhenkai. Research progress on risk factors and scoring systems for surgical decision-making in acute small bowel obstruction. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(4): 578-583. doi: 10.7507/1007-9424.202601026 Copy

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