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

Abstract

Background

Small bowel obstruction (SBO) is a prevalent gastrointestinal disorder that consists primarily of two types: simple bowel obstruction (SiBO) and strangulated bowel obstruction (StBO). Due to life-threatening complications such as septic shock and multiple organ dysfunction syndrome, there is an urgent need for an easy-to-acquire predictive model for StBO based on clinical symptoms and laboratory.

Methods

A total of 453 patients diagnosed with SBO were randomly divided into training and validation datasets at a ratio of 7:3. The demographic, clinical, and laboratory data were collected. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify relevant variables, and a multivariable logistic regression (LR) model was subsequently developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, and diagnostic metrics, including accuracy, sensitivity, specificity, and area under the curve (AUC), were calculated.

Results

Of the 453 patients diagnosed with SBO, 62 (13.7%) had StBO, and 391 (86.3%) had SiBO. Univariate analysis revealed significant associations between bowel ischemia and the following variables: body mass index (BMI, p = 0.027), neutrophil percentage (N, p = 0.002), aspartate aminotransferase (AST, p = 0.024), serum creatinine (p = 0.030), serum urea (p = 0.019), glucose (p = 0.029), prothrombin time (PT, p = 0.043), cessation of defecation and flatus (p = 0.013), tenderness (p = 0.004), and rebound tenderness (p < 0.001). A LASSO regression model with optimized regularization parameters (α = 0.3, λ = 0.0202; log[λ] = − 3.902) was used to select 10 predictors. Rebound tenderness (OR, 6.64; 95% CI, 2.97–15.48; p < 0.001), BMI (OR, 0.02; 95% CI, 0.00–0.37; p = 0.010), N (OR, 1.05; 95% CI,1.01–1.09; p = 0.009), and AST (OR, 1.97; 95% CI, 1.01–4.06, p = 0.055) were significantly associated with intestinal ischemia via multivariable LR. The final predictive model (BAR-N) had a strong performance, with an AUC of 0.784 in the training cohort and 0.750 in the validation cohort. Additionally, the model exhibited high specificity (90.3%) and accuracy (80.7%), although its sensitivity remained relatively low at 31.8%.

Conclusions

We developed an easy-to-acquire predictive model (BAR-N) for the diagnosis of StBO that incorporates both clinical and laboratory data. This model shows promise as an adjunctive decision-support tool, particularly in resource-limited or high-acuity settings. However, its generalizability is limited by the absence of external validation, underscoring the need for future multicenter studies to confirm its broader applicability.