A Prediction Model for Recognizing Strangulated Small Bowel Obstruction
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 co...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Gastroenterology Research and Practice |
| Online Access: | http://dx.doi.org/10.1155/2018/7164648 |
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| author | Xiaming Huang Guan Fang Jie Lin Keyu Xu Hongqi Shi Lei Zhuang |
| author_facet | Xiaming Huang Guan Fang Jie Lin Keyu Xu Hongqi Shi Lei Zhuang |
| author_sort | Xiaming Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-30231a7c09534674a11e64329c3c5d9b |
| institution | Kabale University |
| issn | 1687-6121 1687-630X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Gastroenterology Research and Practice |
| spelling | doaj-art-30231a7c09534674a11e64329c3c5d9b2025-02-03T01:00:23ZengWileyGastroenterology Research and Practice1687-61211687-630X2018-01-01201810.1155/2018/71646487164648A Prediction Model for Recognizing Strangulated Small Bowel ObstructionXiaming Huang0Guan Fang1Jie Lin2Keyu Xu3Hongqi Shi4Lei Zhuang5Department of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaIntroduction. 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.http://dx.doi.org/10.1155/2018/7164648 |
| spellingShingle | Xiaming Huang Guan Fang Jie Lin Keyu Xu Hongqi Shi Lei Zhuang A Prediction Model for Recognizing Strangulated Small Bowel Obstruction Gastroenterology Research and Practice |
| title | A Prediction Model for Recognizing Strangulated Small Bowel Obstruction |
| title_full | A Prediction Model for Recognizing Strangulated Small Bowel Obstruction |
| title_fullStr | A Prediction Model for Recognizing Strangulated Small Bowel Obstruction |
| title_full_unstemmed | A Prediction Model for Recognizing Strangulated Small Bowel Obstruction |
| title_short | A Prediction Model for Recognizing Strangulated Small Bowel Obstruction |
| title_sort | prediction model for recognizing strangulated small bowel obstruction |
| url | http://dx.doi.org/10.1155/2018/7164648 |
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