Propensity Score Analysis of Anastomotic Leakage After Rectal Cancer Resection: Risk Factors and Radiation Effects

2012 ◽  
Vol 84 (3) ◽  
pp. S340-S341
Author(s):  
J. Chang ◽  
K. Keum ◽  
N. Kim ◽  
K. Lee ◽  
J. Kim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Rectal Cancer ◽  
Propensity Score ◽  
Anastomotic Leakage ◽  
Radiation Effects ◽  
Propensity Score Analysis ◽  
Cancer Resection ◽  
Rectal Cancer Resection ◽  
Score Analysis

scholarly journals Prediction model for anastomotic leakage after laparoscopic rectal cancer resection

2020 ◽  
Vol 48 (9) ◽  
pp. 030006052095754
Author(s):  
Enesh Shiwakoti ◽  
Jianning Song ◽  
Jun Li ◽  
Shanshan Wu ◽  
Zhongtao Zhang
Keyword(s):  
Risk Factors ◽  
Rectal Cancer ◽  
Multivariate Analysis ◽  
Prediction Model ◽  
Anastomotic Leakage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
High Body Mass Index ◽  
Cancer Resection ◽  
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 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.

Tumor Budding as a Prognostic Marker in Rectal Cancer Patients on Propensity Score Analysis

2021 ◽  
Author(s):  
Jung Kyong Shin ◽  
Yoon Ah Park ◽  
Jung Wook Huh ◽  
Seong Hyeon Yun ◽  
Hee Cheol Kim ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Propensity Score ◽  
Cancer Patients ◽  
Prognostic Marker ◽  
Propensity Score Analysis ◽  
Tumor Budding ◽  
Score Analysis

Minimally invasive approaches to the management of anastomotic leakage following restorative rectal cancer resection

2019 ◽  
Vol 21 (12) ◽  
pp. 1364-1371 ◽  
Author(s):  
B. Creavin ◽  
É. J. Ryan ◽  
M. E. Kelly ◽  
A. Moynihan ◽  
C. E. Redmond ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Minimally Invasive ◽  
Anastomotic Leakage ◽  
Cancer Resection ◽  
Rectal Cancer Resection

A Comparison of Pathologic Outcomes of Open, Laparoscopic, and Robotic Resections for Rectal Cancer Using the ACS-NSQIP Proctectomy-Targeted Database: a Propensity Score Analysis

2018 ◽  
Vol 23 (2) ◽  
pp. 348-356 ◽  
Author(s):  
Richard Garfinkle ◽  
Maria Abou-Khalil ◽  
Sahir Bhatnagar ◽  
Nathalie Wong-Chong ◽  
Laurent Azoulay ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Propensity Score ◽  
Propensity Score Analysis ◽  
Acs Nsqip ◽  
Score Analysis
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