Effect of esketamine on opioid consumption and postoperative pain in thyroidectomy: A randomized controlled trial.

AIMS: Thyroidectomy is frequently associated with substantial postoperative pain. Esketamine, an N-methyl-d-aspartate receptor antagonist, has been demonstrated to be effective in multiple analgesia scenarios. We hypothesized that intraoperative administration of esketamine may reduce perioperative opioid consumption and postoperative pain in patients undergoing thyroidectomy. METHODS: Sixty patients undergoing thyroidectomy were randomly assigned to two groups. Patients in the esketamine group received a pre-incisional intravenous bolus of esketamine (0.5 mg kg-1 ) followed by a continuous infusion of 0.24 mg kg-1  h-1 until the beginning of wound closure. Patients in the placebo group received 0.9% NaCl (bolus and infusion). The primary outcome was perioperative sufentanil consumption. The postoperative pain, sleep quality and adverse events during the first postoperative 24 h were also evaluated. RESULTS: Patients in the esketamine group consumed significantly less sufentanil than those in the saline group (24.6 ± 3.1 µg vs. 33.7 ± 5.1 µg, mean difference, 9.1; 95% confidence interval [CI], 6.9-11.3, P < .001). Postoperative pain scores were significantly lower in the esketamine group than those in the saline group during the first 24 h postoperatively (P < .05). Patients receiving esketamine experienced higher sleep quality than those in the saline group during surgical night (P = .043). There were no significant differences in adverse events between the two groups. CONCLUSIONS: Intraoperative administration of esketamine reduces perioperative sufentanil consumption and postoperative pain without increasing psychotomimetic side effects in patients undergoing thyroidectomy. The development of combined anaesthesia regimens, including esketamine, may foster strategies for pain management during thyroidectomy.

Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs.

In this article, we propose a hybrid artificial bee colony (ABC) algorithm to solve a parallel batching distributed flow-shop problem (DFSP) with deteriorating jobs. In the considered problem, there are two stages as follows: 1) in the first stage, a DFSP is studied and 2) after the first stage has been completed, each job is transferred and assembled in the second stage, where the parallel batching constraint is investigated. In the two stages, the deteriorating job constraint is considered. In the proposed algorithm, first, two types of problem-specific heuristics are proposed, namely, the batch assignment and the right-shifting heuristics, which can substantially improve the makespan. Next, the encoding and decoding approaches are developed according to the problem constraints and objectives. Five types of local search operators are designed for the distributed flow shop and parallel batching stages. In addition, a novel scout bee heuristic that considers the useful information that is collected by the global and local best solutions is investigated, which can enhance searching performance. Finally, based on several well-known benchmarks and realistic industrial instances and via comprehensive computational comparison and statistical analysis, the highly effective performance of the proposed algorithm is favorably compared against several algorithms in terms of both solution quality and population diversity.