An easily acquirable predictive model for strangulated bowel obstruction: the BAR-N

Area of Science:

• Gastroenterology
• Surgical Oncology
• Medical Diagnostics

Background:

• Small bowel obstruction (SBO) is a common gastrointestinal condition with two main types: simple (SiBO) and strangulated (StBO).
• Strangulated SBO poses life-threatening risks, including septic shock and multiple organ dysfunction syndrome.
• There is a critical need for accessible predictive models to identify StBO early using clinical and laboratory findings.

Purpose of the Study:

• To develop and validate an easy-to-acquire predictive model for diagnosing strangulated small bowel obstruction (StBO).
• To identify key clinical and laboratory variables associated with StBO.
• To evaluate the diagnostic performance of the developed model.

Main Methods:

• A cohort of 453 SBO patients was divided into training (70%) and validation (30%) sets.
• Least Absolute Shrinkage and Selection Operator (LASSO) regression identified significant predictors.
• A multivariable logistic regression (LR) model was constructed and evaluated using ROC curve analysis.

Main Results:

• The final BAR-N model incorporated predictors such as rebound tenderness, BMI, neutrophil percentage, and AST.
• The BAR-N model demonstrated strong performance with an AUC of 0.784 (training) and 0.750 (validation).
• The model achieved high specificity (90.3%) and accuracy (80.7%), with a sensitivity of 31.8%.

Conclusions:

• An accessible predictive model (BAR-N) integrating clinical and laboratory data for StBO diagnosis was successfully developed.
• The BAR-N model shows potential as a decision-support tool, particularly in resource-constrained environments.
• Further multicenter studies are required to validate the model's generalizability and confirm its clinical utility.