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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 lap...

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Autores principales: Shiwakoti, Enesh, Song, Jianning, Li, Jun, Wu, Shanshan, Zhang, Zhongtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520932/
https://www.ncbi.nlm.nih.gov/pubmed/32962496
http://dx.doi.org/10.1177/0300060520957547
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author Shiwakoti, Enesh
Song, Jianning
Li, Jun
Wu, Shanshan
Zhang, Zhongtao
author_facet Shiwakoti, Enesh
Song, Jianning
Li, Jun
Wu, Shanshan
Zhang, Zhongtao
author_sort Shiwakoti, Enesh
collection PubMed
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.
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spelling pubmed-75209322020-10-06 Prediction model for anastomotic leakage after laparoscopic rectal cancer resection Shiwakoti, Enesh Song, Jianning Li, Jun Wu, Shanshan Zhang, Zhongtao J Int Med Res Retrospective Clinical Research Report 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. SAGE Publications 2020-09-22 /pmc/articles/PMC7520932/ /pubmed/32962496 http://dx.doi.org/10.1177/0300060520957547 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Retrospective Clinical Research Report
Shiwakoti, Enesh
Song, Jianning
Li, Jun
Wu, Shanshan
Zhang, Zhongtao
Prediction model for anastomotic leakage after laparoscopic rectal cancer resection
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
topic Retrospective Clinical Research Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520932/
https://www.ncbi.nlm.nih.gov/pubmed/32962496
http://dx.doi.org/10.1177/0300060520957547
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