BACKGROUND AND OBJECTIVES:Tourniquet pain often limits the use of intravenous regional anesthesia (IVRA). Intravenous (IV) lidocaine has been shown to be effective in the management of acute and neuropathic pains. We tested the hypothesis that a priming IV injection of lidocaine might have an analgesic effect on tourniquet pain during IVRA.METHODS:A prospective, randomized, double- blind study was conducted on 40 patients scheduled for carpal tunnel decompression. No sedation was given. Each patient received either 1 mg/kg of IV lidocaine (group L) or 0.1 mL/kg of IV isotonic saline (group control = C) 5 minutes before IVRA. Thereafter, they received 3 mg/kg of plain 0.5 % lidocaine into the isolated and exsanguinated arm. A double-cuffed tourniquet was used. Pain at the tourniquet and the surgical sites was assessed every 5 minutes using a linear visual analog scale (VAS) and a verbal rating scale (VRS) during the surgical procedure and the immediate postoperative period (60 minutes).RESULTS:Demographic data and duration of proximal and distal tourniquet were similar in each group. Significant differences in the pain scales were observed for the distal tourniquet at tourniquet inflation time and 15 minutes after (P =.03 and.005, respectively) in the group L. For the proximal tourniquet, only the VRS was significantly improved (P =.03). No analgesic benefit was observed in the immediate postoperative period.CONCLUSIONS:Priming IV lidocaine when compared with isotonic saline is effective in reducing tourniquet pain in IVRA.

Virtual reality (VR) is mostly used as a technical means of immersing the user in a computer-generated situation where the reality is the model to be perfectly recreated. Trainers often provide supportive explanations by using tutorial situations that do not exactly reproduce reality. Therefore, VR systems should also provide such tutorial situations. In order to address this issue, an intelligent tutorial agent has been designed to support the learning process. The agent is based on a cognitive taxonomy of the trainee's behaviour in the VR context. For each trainee, the agent provides two kinds of tutorial according to his/her knowledge level. The first one consists of modifying the scenario and the second one consists of guiding the trainee. These strategies can be represented by different fidelity levels of virtual environments (e.g. enhancement, simplification). They can be activated by the trainee, by the trainer or automatically. They are specified off-line and continuously modified on-line. An application of this intelligent agent is being implemented in a VR simulation-based training system for SNCF train drivers. The goal of this application is to teach the procedure to control and handle switches on high speed train tracks.