Literature DB >> 28383453

Predictors of Anastomotic Leak in Elderly Patients After Colectomy: Nomogram-Based Assessment From the American College of Surgeons National Surgical Quality Program Procedure-Targeted Cohort.

Ahmet Rencuzogullari1, Cigdem Benlice, Michael Valente, Maher A Abbas, Feza H Remzi, Emre Gorgun.   

Abstract

BACKGROUND: Elderly patients undergoing colorectal surgery have increasingly become under scrutiny by accounting for the largest fraction of geriatric postoperative deaths and a significant proportion of all postoperative complications, including anastomotic leak.
OBJECTIVE: This study aimed to determine predictors of anastomotic leak in elderly patients undergoing colectomy by creating a novel nomogram for simplistic prediction of anastomotic leak risk in a given patient.
DESIGN: This study was a retrospective review. SETTINGS: The database review of the American College of Surgeons National Surgical Quality Improvement Program was conducted at a single institution. PATIENTS: Patients aged ≥65 years who underwent elective segmental colectomy with an anastomosis at different levels (abdominal or low pelvic) in 2012-2013 were identified from the multi-institutional procedure-targeted database. MAIN OUTCOME MEASURES: We constructed a stepwise multiple logistic regression model for anastomotic leak as an outcome; predictors were selected in a stepwise fashion using the Akaike information criterion. The validity of the nomogram was externally tested on elderly patients (≥65 years of age) from the 2014 American College of Surgeons National Surgical Quality Improvement Program colectomy-targeted database.
RESULTS: A total of 10,392 patients were analyzed, and anastomotic leak occurred in 332 (3.2%). Of the patients who developed anastomotic leak, 192 (57.8%) were men (p < 0.001). Based on unadjusted analysis, factors associated with an increased risk of anastomotic leak were ASA score III and IV (p < 0.001), chronic obstructive pulmonary disease (p = 0.004), diabetes mellitus (p = 0.003), smoking history (p = 0.014), weight loss (p = 0.013), previously infected wound (p = 0.005), omitting mechanical bowel preparation (p = 0.005) and/or preoperative oral antibiotic use (p < 0.001), and wounds classified as contaminated or dirty/infected (p = 0.008). Patients who developed anastomotic leak had a longer length of hospital stay (17 vs 7 d; p < 0.001) and operative time (191 vs 162 min; p < 0.001). A multivariate model and nomogram were created. LIMITATIONS: This study was limited by its retrospective nature and short-term follow-up (30 d).
CONCLUSIONS: An accurate prediction of anastomotic leak affecting morbidity and mortality after colorectal surgery using the proposed nomogram may facilitate decision making in elderly patients for healthcare providers.

Entities:  

Mesh:

Year:  2017        PMID: 28383453     DOI: 10.1097/DCR.0000000000000789

Source DB:  PubMed          Journal:  Dis Colon Rectum        ISSN: 0012-3706            Impact factor:   4.585


  24 in total

1.  Mucosal cancer-associated microbes and anastomotic leakage after resection of colorectal carcinoma.

Authors:  Kosuke Mima; Yuki Sakamoto; Keisuke Kosumi; Yoko Ogata; Keisuke Miyake; Yukiharu Hiyoshi; Takatsugu Ishimoto; Masaaki Iwatsuki; Yoshifumi Baba; Shiro Iwagami; Yuji Miyamoto; Naoya Yoshida; Shuji Ogino; Hideo Baba
Journal:  Surg Oncol       Date:  2019-11-18       Impact factor: 3.279

2.  A Nomogram to Predict Anastomotic Leakage in Open Rectal Surgery-Hope or Hype?

Authors:  Johannes Klose; Ignazio Tarantino; Armin von Fournier; Moritz J Stowitzki; Yakup Kulu; Thomas Bruckner; Claudia Volz; Thomas Schmidt; Martin Schneider; Markus W Büchler; Alexis Ulrich
Journal:  J Gastrointest Surg       Date:  2018-05-18       Impact factor: 3.452

3.  Intraoperative air leak test reduces the rate of postoperative anastomotic leak: analysis of 777 laparoscopic left-sided colon resections.

Authors:  Marco Ettore Allaix; Adriana Lena; Maurizio Degiuli; Alberto Arezzo; Roberto Passera; Massimiliano Mistrangelo; Mario Morino
Journal:  Surg Endosc       Date:  2018-09-10       Impact factor: 4.584

Review 4.  Attitudes of Surgeons toward Elderly Cancer Patients: A Survey from the SIOG Surgical Task Force.

Authors:  Nicole M Saur; Isacco Montroni; Federico Ghignone; Giampaolo Ugolini; Riccardo A Audisio
Journal:  Visc Med       Date:  2017-08-07

5.  Development and Validation of Machine Learning Models to Predict Readmission After Colorectal Surgery.

Authors:  Kevin A Chen; Chinmaya U Joisa; Karyn B Stitzenberg; Jonathan Stem; Jose G Guillem; Shawn M Gomez; Muneera R Kapadia
Journal:  J Gastrointest Surg       Date:  2022-09-07       Impact factor: 3.267

6.  Differential Performance of Machine Learning Models in Prediction of Procedure-Specific Outcomes.

Authors:  Kevin A Chen; Matthew E Berginski; Chirag S Desai; Jose G Guillem; Jonathan Stem; Shawn M Gomez; Muneera R Kapadia
Journal:  J Gastrointest Surg       Date:  2022-05-04       Impact factor: 3.267

7.  Development of a Risk Score to Predict Anastomotic Leak After Left-Sided Colectomy: Which Patients Warrant Diversion?

Authors:  Nicholas P McKenna; Katherine A Bews; Robert R Cima; Cynthia S Crowson; Elizabeth B Habermann
Journal:  J Gastrointest Surg       Date:  2019-06-26       Impact factor: 3.452

8.  Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer.

Authors:  Rongbo Wen; Kuo Zheng; Qihang Zhang; Leqi Zhou; Qizhi Liu; Guanyu Yu; Xianhua Gao; Liqiang Hao; Zheng Lou; Wei Zhang
Journal:  J Gastrointest Oncol       Date:  2021-06

9.  Nomogram to predict postoperative infectious complications after surgery for colorectal cancer: a retrospective cohort study in China.

Authors:  Jing Wen; Tao Pan; Yun-Chuan Yuan; Qiu-Shi Huang; Jian Shen
Journal:  World J Surg Oncol       Date:  2021-07-08       Impact factor: 2.754

10.  Impact of mechanical bowel preparation in elective colorectal surgery: A meta-analysis.

Authors:  Katie E Rollins; Hannah Javanmard-Emamghissi; Dileep N Lobo
Journal:  World J Gastroenterol       Date:  2018-01-28       Impact factor: 5.742

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