Effect of Perioperative Dexamethasone and Different NSAIDs on Anastomotic Leak Risk: A Propensity Score Analysis

Glucocorticoid use and ischemia-reperfusion injury in laparoscopic liver resection: Randomized controlled trial

AIM

Laparoscopic liver resection (LLR) is increasingly carried out worldwide. However, there are concerns regarding ischemia-reperfusion injury caused by pneumoperitoneum and the Pringle maneuver. It is not clear whether perioperative use of glucocorticoids lowers the risk of ischemia-reperfusion hepatic injury in LLR as has been reported for open liver resection. The aim of the present study was to investigate the role of perioperative glucocorticoid use in improving hepatic function and surgical outcomes after LLR.

METHODS

In this double-blind, randomized controlled trial (UMIN000013823), we enrolled 130 patients who presented to our institution for LLR between April 2014 and October 2018. Six patients were excluded, resulting in 124 patients being randomized to either the glucocorticoid or the control group. Preoperatively, patients in the glucocorticoid group received 500 mg methylprednisolone in saline solution, patients in the control group saline solution only. Surgical outcomes and blood parameters were compared between the two groups.

RESULTS

The Pringle maneuver could not be carried out in 24 patients, resulting in 50 patients in each group being included in the analysis. Postoperatively, total, direct and indirect bilirubin, and C-reactive protein and interleukin-6 levels were significantly lower, albumin levels were significantly higher, and prothrombin time was significantly shorter in the glucocorticoid than in the control group. Surgical outcomes were not significantly different between the groups.

CONCLUSION

This first report on preoperative glucocorticoid use in LLR showed that it significantly improved postoperative liver function and thus might enhance the safety of LLR.

Postoperative non-steroidal anti-inflammatory drugs and anastomotic leakage after gastrointestinal anastomoses: Systematic review and meta-analysis

AIM

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to control postoperative pain; however, their postoperative use has been associated with anastomotic leakage after gastrointestinal surgery. This systematic review and meta-analysis aimed to determine the correlation between the use of NSAIDs and anastomotic leakage.

METHODS

We conducted a comprehensive electronic literature search up to August 2018 to identify studies comparing anastomotic leakage in patients with and without postoperative NSAID use following gastrointestinal surgery. We then carried out a meta-analysis using random-effects models to calculate odds ratios (OR) with 95% confidence intervals (CI).

RESULTS

Twenty-four studies were included in this meta-analysis, including a total of 31 877 patients. Meta-analysis showed a significant association between NSAID use and anastomotic leakage (OR 1.73; 95% CI = 1.31-2.29, P < .0001). Subgroup analyses showed that non-selective NSAIDs, but not selective cyclooxygenase-2 inhibitors, were significantly associated with anastomotic leakage. However there was no significant subgroup difference between selective and non-selective NSAIDs.

CONCLUSION

Results of this meta-analysis indicate that postoperative NSAID use is associated with anastomotic leakage following gastrointestinal surgeries. Caution is warranted when using NSAIDs for postoperative analgesic control in patients with gastrointestinal anastomoses.

Modified Colon Leakage Score to Predict Anastomotic Leakage in Patients Who Underwent Left-Sided Colorectal Surgery

Colon leakage score (CLS) was introduced as a clinical tool to predict anastomotic leakage (AL) in patients who underwent left-sided colorectal surgery, but its clinical validity has not been widely studied. We evaluated the clinical utility of CLS and developed a modified CLS (m-CLS). In total, 566 patients who underwent left-sided colorectal surgery were enrolled and categorized into training (n = 396) and validation (n = 170) sets via random sampling. Using CLS variables, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The model's performance was validated in the validation set. The predictive powers of m-CLS and CLS were compared by the area under the receiver operating characteristic (AUROC) curve in the overall group. Twenty-three AL events (4.1%) were noted. The AL group had a significantly higher mean CLS than the No Leakage group (12.5 vs. 9.6, p = 0.001). Five clinical variables were selected and used to generate m-CLS. The predictive performance of m-CLS was similar in training and validation sets (AUROC 0.838 vs. 0.803, p = 0.724). In the overall set, m-CLS was significantly predictive of AL and performed better than CLS (AUROC 0.831 vs. 0.701, p = 0.008). In conclusion, LASSO-model-generated m-CLS could predict AL more accurately than CLS.