Aims Adductor canal block (ACB) has emerged as an alternative to femoral nerve block (FNB) for analgesia after total knee arthroplasty (TKA). The optimal duration of maintenance of the ACB is still questionable. The purpose of this study was to compare the analgesic benefits and physiotherapy (PT) outcomes of single-shot ACB to two different regimens of infusion of the continuous ACB, 24-hour and 48-hour infusion. Patients and Methods This was a prospective, randomized, unblinded study. A total of 159 American Society of Anesthesiologists (ASA) physical status I to III patients scheduled for primary TKA were randomized to one of three study groups. Three patients did not complete the study, leaving 156 patients for final analysis. Group A (n = 53) was the single-shot group (16 female patients and 37 male patients with a mean age of 63.9 years (sd 9.6)), group B (n = 51) was the 24-hour infusion group (22 female patients and 29 male patients with a mean age of 66.5 years (sd 8.5)), and group C (n = 52) was the 48-hour infusion group (18 female patients and 34 male patients with a mean age of 62.2 years (sd 8.7)). Pain scores, opioid requirements, PT test results, and patient-reported outcome instruments were compared between the three groups. Results The proportion of patients reporting severe pain, defined as a pain score of between 7 and 10, on postoperative day number 2 (POD 2) were 21% for the single-shot group, 14% for the 24-hour block group, and 12% for the 48-hour block group (p = 0.05). Cumulative opioid requirements after 48 hours were similar between the groups. Functional outcomes were similar in all three groups in POD 1 and POD 2. Conclusion There was no clear benefit of the 24-hour or 48-hour infusions over the single-shot ACB for the primary endpoint of the study. Otherwise, there were marginal benefits for keeping the indwelling catheter for 48 hours in terms of reducing the number of patients with moderate pain and improving the quality of pain management. However, all three groups had similar opioid usage, length of hospital stay, and functional outcomes. Further studies with larger sample sizes are needed to confirm these findings. Cite this article: Bone Joint J 2019;101-B:340–347.

Figure 1: A self-refining liquid control game uses player analytics to guide precomputation to the most visited regions of the liquid's state space. The game's quality continuously improves over time, ultimately providing a high-quality, interactive experience. AbstractData-driven simulation demands good training data drawn from a vast space of possible simulations. While fully sampling these large spaces is infeasible, we observe that in practical applications, such as gameplay, users explore only a vanishingly small subset of the dynamical state space. In this paper we present a sampling approach that takes advantage of this observation by concentrating precomputation around the states that users are most likely to encounter. We demonstrate our technique in a prototype self-refining game whose dynamics improve with play, ultimately providing realistically rendered, rich fluid dynamics in real time on a mobile device. Our results show that our analytics-driven training approach yields lower model error and fewer visual artifacts than a heuristic training strategy.

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