Prediction model for anastomotic leakage after laparoscopic rectal cancer resection
Objective This study was performed to identify risk factors for anastomotic leakage (AL) and combine these factors to create a prediction model for the risk of AL after laparoscopic rectal cancer resection. Methods This retrospective study involved 185 patients with rectal cancer who underwent lapar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-09-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/0300060520957547 |
| _version_ | 1818841723342684160 |
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| author | Enesh Shiwakoti Jianning Song Jun Li Shanshan Wu Zhongtao Zhang |
| author_facet | Enesh Shiwakoti Jianning Song Jun Li Shanshan Wu Zhongtao Zhang |
| author_sort | Enesh Shiwakoti |
| collection | DOAJ |
| description | Objective This study was performed to identify risk factors for anastomotic leakage (AL) and combine these factors to create a prediction model for the risk of AL after laparoscopic rectal cancer resection. Methods This retrospective study involved 185 patients with rectal cancer who underwent laparoscopic resection from March 2012 to February 2017. Five risk factors were analyzed by multivariate analysis. A prediction model was established by combining the risk factors from the multivariate analysis, and the accuracy of the model was evaluated by a receiver operating characteristic curve. Results The overall AL rate was 17.84%. The multivariate analysis identified the following independent risk factors for AL: high body mass index (odds ratio [OR], 3.009; 95% confidence interval [CI], 1.127–7.125), preoperative radiochemotherapy (OR, 3.778; 95% CI, 1.168–12.219), larger tumor size (OR, 2.710; 95% CI, 1.119–6.562), and longer surgical time (OR, 2.476; 95% CI, 1.033–5.932). We established a prediction model that can evaluate the risk of AL by determining the predictive probability. The area under the curve for the model’s predictive performance was 0.70 (95% CI, 0.598–0.795). Conclusion A prediction model was created to predict the risk of AL after laparoscopic rectal cancer resection. |
| first_indexed | 2024-12-19T04:30:37Z |
| format | Article |
| id | doaj.art-c1f3d006238142d7a43460c500807e5f |
| institution | Directory Open Access Journal |
| issn | 1473-2300 |
| language | English |
| last_indexed | 2024-12-19T04:30:37Z |
| publishDate | 2020-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj.art-c1f3d006238142d7a43460c500807e5f2022-12-21T20:35:53ZengSAGE PublishingJournal of International Medical Research1473-23002020-09-014810.1177/0300060520957547Prediction model for anastomotic leakage after laparoscopic rectal cancer resectionEnesh ShiwakotiJianning SongJun LiShanshan WuZhongtao ZhangObjective This study was performed to identify risk factors for anastomotic leakage (AL) and combine these factors to create a prediction model for the risk of AL after laparoscopic rectal cancer resection. Methods This retrospective study involved 185 patients with rectal cancer who underwent laparoscopic resection from March 2012 to February 2017. Five risk factors were analyzed by multivariate analysis. A prediction model was established by combining the risk factors from the multivariate analysis, and the accuracy of the model was evaluated by a receiver operating characteristic curve. Results The overall AL rate was 17.84%. The multivariate analysis identified the following independent risk factors for AL: high body mass index (odds ratio [OR], 3.009; 95% confidence interval [CI], 1.127–7.125), preoperative radiochemotherapy (OR, 3.778; 95% CI, 1.168–12.219), larger tumor size (OR, 2.710; 95% CI, 1.119–6.562), and longer surgical time (OR, 2.476; 95% CI, 1.033–5.932). We established a prediction model that can evaluate the risk of AL by determining the predictive probability. The area under the curve for the model’s predictive performance was 0.70 (95% CI, 0.598–0.795). Conclusion A prediction model was created to predict the risk of AL after laparoscopic rectal cancer resection.https://doi.org/10.1177/0300060520957547 |
| spellingShingle | Enesh Shiwakoti Jianning Song Jun Li Shanshan Wu Zhongtao Zhang Prediction model for anastomotic leakage after laparoscopic rectal cancer resection Journal of International Medical Research |
| title | Prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| title_full | Prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| title_fullStr | Prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| title_full_unstemmed | Prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| title_short | Prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| title_sort | prediction model for anastomotic leakage after laparoscopic rectal cancer resection |
| url | https://doi.org/10.1177/0300060520957547 |
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