Effect of intraoperative methadone vs other opioids on postoperative outcomes: a meta-analysis of randomized controlled studies.

ABSTRACT: Recent randomized controlled trials comparing the efficacy between intraoperative methadone and other opioids on postoperative outcomes have been limited by their small sample sizes and conflicting results. We performed a meta-analysis on randomized controlled trials which investigated outcomes between methadone and an opioid control group. Primary outcome data included postoperative opioid consumption, number of patients who received postoperative opioids, time to first analgesic, and pain scores. Secondary outcomes included time to extubation and incidence of nausea, vomiting, and respiratory depression. Statistical analysis was performed using RevMan. A P < 0.05 was considered statistically significant. Nine studies comprising 632 patients were included. There was no statistically significant reduction in opioid consumption postoperatively between the groups. Forty-seven percentage of patients in the methadone group received a dose of opioid postoperatively compared with 55% in the other opioids control group, which was not statistically significant. (P = 0.25) There was no difference in average time to receiving first postoperative analgesic among the groups. Pain scores within 24 hours were significantly lower in the methadone group when compared with other opioids (8 studies, n = 622, -0.49 [-0.74, -0.23], P = 0.002). However, there was no difference between 24 and 72 hours. There was no difference among the groups with respect to extubation time, nausea, vomiting, or respiratory depression. This meta-analysis concludes that there is currently insufficient evidence for the use of intraoperative methadone, when compared with other opioids. Although there was a decrease in average pain scores with methadone when compared with controls at 24 hours, there was no difference between 24 and 72 hours.

Discriminative Multiple Instance Hyperspectral Target Characterization.

In this paper, two methods for discriminative multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed hyperspectral imagery, accurate pixel-level labels on training data is often unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are smaller in size than the resolution of a single pixel, training data is comprised only of mixed data points (in which target training points are mixtures of responses from both target and non-target classes). Results show improved, consistent performance over existing multiple instance concept learning methods on several hyperspectral sub-pixel target detection problems.