Dataset Information

Trends in risk factors of anastomotic leakage after colorectal cancer surgery (2011-2019): A Dutch population-based study.

ABSTRACT:

Aim

Anastomotic leakage (AL) after colon cancer (CC) and rectal cancer (RC) surgery often requires reintervention. Prevalence and morbidity may change over time with evolutions in treatment strategies and changes in patient characteristics. This nationwide study aimed to evaluate changes in the incidence, risk factors and mortality from AL during the past nine years.

Methods

Data of CC and RC resections with primary anastomosis were extracted from the Dutch Colorectal Audit (2011-2019). AL was registered if requiring reintervention. Three consecutive cohorts were compared using logistic regression analysis.

Results

Incidence of AL after CC surgery decreased from 6.6% in 2011-2013 to 4.8% in 2017-2019 and increased from 8.6% to 11.9% after RC surgery. In 2011-2013, male sex, ASA ≥3, (y)pT3-4, neoadjuvant therapy, emergency surgery and multivisceral resection were identified as risk factors for AL after CC surgery. In 2017-2019, only male sex and ASA ≥3 were risk factors for AL. For RC patients, male sex and neoadjuvant therapy were a risk factor for AL in 2011-2013. In 2017-2019, transanal approach was also a risk factor for AL. Postoperative mortality rate after AL was 12% (CC) and 2% (RC) in 2017-2019, without significant changes over time.

Conclusion

Contradictory trends in incidence and mortality for AL were observed among CC and RC surgery with changing risk factors over the past 9 years. High mortality after AL is only observed after CC surgery and remains unchanged. Continued efforts should be made to improve early detection and treatment of AL for these patients.

SUBMITTER: Arron MNN

PROVIDER: S-EPMC9293104 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAnastomotic leakage (AL) is a severe complication after colorectal surgery. This study aimed to investigate a method for the early diagnosis of AL after surgical resection by analysing inflammatory factors (IFs) in peritoneal drainage fluid.MethodsAbdominal drainage fluid of patients with colorectal cancer who underwent resection between April 2017 and April 2018, were prospectively collected in the postoperative interval. Six IFs, including interleukin (IL)-1β, IL-6, IL-10, tumour necrosis factor (TNF)-α, matrix metalloproteinase (MMP)2, and MMP9, in drainage were determined by multiplex immunoassay to investigate AL (in patients undergoing resection and anastomosis) and pelvic collection (in patients undergoing abdominoperineal resection). Sparreboom and colleagues' prediction model was first evaluated for AL/pelvic collection, followed by a new IF-based score system (AScore) that was developed by a least absolute shrinkage and selection operator (LASSO) regression, for the same outcomes. The model performance was tested for the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV).ResultsOut of 123 patients eligible, 119 patients were selected, including 12 patients with AL/pelvic collection. Sparreboom and colleagues' prediction model was documented with the best diagnostic efficacy on postoperative day 3 (POD3), with an AUC of 0.77. After optimization, AScore on POD3 increased the AUC to 0.83 and on POD1 showed the best diagnostic efficiency, with an AUC of 0.88. Based on the Youden index, the cut-off value of AScore on POD1 was set as -2.46 to stratify patients into low-risk and high-risk groups for AL/pelvic collection. The model showed 90.0 per cent sensitivity, 69.7 per cent specificity, 98.4 per cent NPV, and 25.0 per cent PPV.ConclusionsThe early determination of IFs in abdominal drainage fluid of patients undergoing colorectal surgery could be useful to predict AL or pelvic collection.

Project description:BACKGROUND:Anastomotic leakage (AL) remains the most important complication following colorectal surgery, and is associated with high morbidity and mortality rates. Previous research has focused on identifying risk factors and potential biomarkers for AL, but the sensitivity of these tests remains poor. OBJECTIVE:This prospective multicenter observational study aims at combining multiple parameters to establish a diagnostic algorithm for colorectal AL. METHODS:This study aims to include 588 patients undergoing surgery for colorectal carcinoma. Patients will be eligible for inclusion when surgery includes the construction of a colorectal anastomosis. Patient characteristics will be collected upon consented inclusion, and buccal swabs, breath, stool, and blood samples will be obtained prior to surgery. These samples will allow for the collection of information regarding patients' inflammatory status, genetic predisposition, and intestinal microbiota. Additionally, breath and blood samples will be taken postoperatively and patients will be strictly observed during their in-hospital stay, and the period shortly thereafter. RESULTS:This study has been open for inclusion since August 2015. CONCLUSIONS:An estimated 8-10% of patients will develop AL following surgery, and they will be compared to non-leakage patients. The objectives of this study are twofold. The primary aim is to establish and validate a diagnostic algorithm for the pre-operative prediction of the risk of AL development using a combination of inflammatory, immune-related, and genetic parameters. Previously established risk factors and novel parameters will be incorporated into this algorithm, which will aid in the recognition of patients who are at risk for AL. Based on these results, recommendations can be made regarding the construction of an anastomosis or deviating stoma, and possible preventive strategies. Furthermore, we aim to develop a new algorithm for the post-operative diagnosis of AL at an earlier stage, which will positively reflect on short-term survival rates. TRIAL REGISTRATION:Clinicaltrials.gov: NCT02347735; https://clinicaltrials.gov/ct2/show/NCT02347735 (archived by WebCite at http://www.webcitation.org/6hm6rxCsA).