Introduction
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, the development of an easy-to-acquire predictive model for StBO via clinical symptoms and laboratory tests is urgently needed.
Aims & 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=0.004), aspartate aminotransferase (AST, p=0.024), serum creatinine (p=0.030), serum urea (p=0.019), glucose (p=0.029), 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, 6.65; 95% CI, 2.97–15.47; p < 0.001), BMI (OR, 0.02; 95% CI, 0.00–0.36; p = 0.009), N (OR, 24.14; 95% CI, 2.16–373.13; p = 0.015), and AST (OR, 1.97; 95% CI, 1.01–4.05, 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 0.773 in the training cohort and 0.778 in the validation cohort. Additionally, the model exhibited high specificity (97.3%) and accuracy (82.2%).
Conclusion
We developed an easy-to-acquire predictive model (BAR-N ) for the diagnosis of StBO that incorporated both clinical data and laboratory data. The developed nomogram enhances its clinical utility.
References
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Disclosure
The authors have no competing interests to declare.