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Gastroenterology Research and Practice
Volume 2018, Article ID 7164648, 7 pages
Research Article

A Prediction Model for Recognizing Strangulated Small Bowel Obstruction

1Department of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China
2Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

Correspondence should be addressed to Hongqi Shi; moc.361@18891785781 and Lei Zhuang; moc.qq@268339345

Received 3 August 2017; Revised 17 December 2017; Accepted 26 December 2017; Published 26 March 2018

Academic Editor: Eiji Sakai

Copyright © 2018 Xiaming Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Introduction. Early and accurate diagnosis of strangulated small bowel obstruction (SSBO) is difficult. This study aimed to devise a prediction model for predicting the risk of SSBO. Materials and Methods. A database of 417 patients who had clinical symptoms of intestinal obstruction confirmed by computed tomography (CT) were evaluated for inclusion in this study. Symptoms and laboratory and radiologic findings of these patients were collected after admission. These clinical factors were analyzed using logistic regression. A logistic regression model was applied to identify determinant variables and construct a clinical score that would predict SSBO. Results. Seventy-six patients were confirmed to have SSBO, 169 patients required surgery but had no evidence of intestinal ischemia, and 172 patients were successfully managed conservatively. In multivariate logistic regression analysis, body temperature ≥ 38.0°C, positive peritoneal irritation sign, white blood cell (WBC) count > 10.0 × 10^9/L, thick-walled small bowel ≥3 mm, and ascites were significantly associated with SSBO. A new prediction model with total scores ranging from 0 to 481 was developed with these five variables. The area under the curve (AUC) of the new prediction model was 0.935. Conclusions. Our prediction model is a good predictive model to evaluate the severity of SBO.