Keli Wang1, Meijiao Li1, Rui Liu1, Yang Ji2, Jin Yan1,3. 1. Department of Clinical Medicine, Southwest Medical University, Luzhou, People's Republic of China. 2. Department of Clinical Medicine, University of Electronic Science and Technology of China, Chengdu, People's Republic of China. 3. Department of Gastrointestinal Surgery, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital, School of Medicine, University of Electronic Science and Technology, Chengdu, People's Republic of China.
Abstract
Objective: To explore the risk factors of anastomotic leakage (AL) after laparoscopic anterior resection (AR) of rectal cancer and establish a nomogram prediction model. Methods: Clinical and surgical data of patients who underwent AR of rectal cancer at Sichuan Cancer Hospital from January 2017 to December 2020 were retrospectively collected. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AL after AR. A nomogram risk prediction model was established based on the selected independent risk factors and the predictive performance of nomogram was evaluated. Results: A 1013 patients undergoing laparoscopic AR were included, of which 67 had AL, with an incidence of 6.6%. Univariate and multivariate logistic regression analyses showed that male gender, tumors distance from the anus verge of ≤ 5cm, tumors distance from the anus verge of 5-10cm, circumferential tumor growth, operation time ≥ 240min, and no diverting stoma were independent risk factors for AL after AR. A nomogram prediction model was established based on these results. The calibration curve showed that the predicted occurrence probability of the nomogram model was in good agreement with the actual occurrence probability. The area under the receiver operating characteristic (ROC) curve was 0.749. Conclusion: The nomogram prediction model based on the independent risk factors of patients undergoing AL after AR can effectively predict the probability of AL.
Objective: To explore the risk factors of anastomotic leakage (AL) after laparoscopic anterior resection (AR) of rectal cancer and establish a nomogram prediction model. Methods: Clinical and surgical data of patients who underwent AR of rectal cancer at Sichuan Cancer Hospital from January 2017 to December 2020 were retrospectively collected. Univariate and multivariate logistic regression analyses were used to screen the independent risk factors of AL after AR. A nomogram risk prediction model was established based on the selected independent risk factors and the predictive performance of nomogram was evaluated. Results: A 1013 patients undergoing laparoscopic AR were included, of which 67 had AL, with an incidence of 6.6%. Univariate and multivariate logistic regression analyses showed that male gender, tumors distance from the anus verge of ≤ 5cm, tumors distance from the anus verge of 5-10cm, circumferential tumor growth, operation time ≥ 240min, and no diverting stoma were independent risk factors for AL after AR. A nomogram prediction model was established based on these results. The calibration curve showed that the predicted occurrence probability of the nomogram model was in good agreement with the actual occurrence probability. The area under the receiver operating characteristic (ROC) curve was 0.749. Conclusion: The nomogram prediction model based on the independent risk factors of patients undergoing AL after AR can effectively predict the probability of AL.
Authors: Al B Benson; Alan P Venook; Mahmoud M Al-Hawary; Mustafa A Arain; Yi-Jen Chen; Kristen K Ciombor; Stacey Cohen; Harry S Cooper; Dustin Deming; Ignacio Garrido-Laguna; Jean L Grem; Andrew Gunn; Sarah Hoffe; Joleen Hubbard; Steven Hunt; Natalie Kirilcuk; Smitha Krishnamurthi; Wells A Messersmith; Jeffrey Meyerhardt; Eric D Miller; Mary F Mulcahy; Steven Nurkin; Michael J Overman; Aparna Parikh; Hitendra Patel; Katrina Pedersen; Leonard Saltz; Charles Schneider; David Shibata; John M Skibber; Constantinos T Sofocleous; Elena M Stoffel; Eden Stotsky-Himelfarb; Christopher G Willett; Alyse Johnson-Chilla; Lisa A Gurski Journal: J Natl Compr Canc Netw Date: 2020-07 Impact factor: 11.908
Authors: Byoung Chul Lee; Seok-Byung Lim; Jong Lyul Lee; Chan Wook Kim; Yong Sik Yoon; In Ja Park; Chang Sik Yu; Jin Cheon Kim Journal: Ann Coloproctol Date: 2020-01-16