Postoperative Sedation in General Care Wards: A Retrospective Cohort Study.

BACKGROUND: We hypothesized that deeper sedation in the postanesthesia care unit (PACU) increases the risk of subsequent sedation in general care wards (ward sedation) and that patients with ward sedation have more postoperative adverse events than those without ward sedation. METHODS: We reviewed the health records of adult patients who underwent procedures with general anesthesia at Mayo Clinic from May 5, 2018, through December 31, 2020, and were discharged from the PACU to the general care ward. Patient groups were dichotomized as with ward sedation (Richmond Agitation-Sedation Scale [RASS], ≤-2) and without ward sedation (RASS, ≥-1) within the first 24 hours after PACU discharge. Multivariable logistic regression was used to assess the association between clinical variables and ward sedation. RESULTS: A total of 23,766 patients were included in our analysis, of whom 1131 had ward sedation (incidence, 4.8 [Poisson 95% confidence interval, CI, 4.5-5.0]) per 100 patients after general anesthesia. Half of the ward sedation episodes occurred within 32 minutes after PACU discharge. The risk of ward sedation increased with the depth of PACU sedation. The odds ratios (95% CI) of ward sedation for patients with a PACU RASS score of -1 was 0.98 (0.75-1.27); -2, 1.87 (1.44-2.43); -3, 2.98 (2.26-3.93); and ≤-4, 3.97 (2.91-5.42). Adverse events requiring an emergency intervention occurred more often for patients with ward sedation (n = 92, 8.1%) than for those without ward sedation (n = 326, 1.4%; P < .001). CONCLUSIONS: Among patients who met our criteria for PACU discharge, deeper sedation during anesthesia recovery was associated with an increased risk of ward sedation. Patients who had ward sedation had worse outcomes than those without ward sedation.

The minor cannabinoid cannabigerol (CBG) is a highly specific blood biomarker of recent cannabis smoking.

INTRODUCTION: The determination of recent cannabis use is of forensic interest in the investigation of automotive crashes, workplace incidents and other mishaps. Because Δ9-tetrahydrocannabinol may persist in blood after psychoactive effects of intoxication resolve, particularly in regular users, short-lived minor cannabinoids such as cannabigerol have merited examination as adjunct indicators of recent cannabis inhalation. METHODS: As part of an observational cohort study, whole blood cannabinoids including cannabigerol were measured in whole blood by liquid chromatography with tandem mass spectrometry at baseline, and 30 minutes after initiation of a 15-minute supervised interval of ad libitum cannabis smoking in occasional (1-2 days/week over the past 30 days) (n = 24) and daily cannabis smokers (n = 32). Per protocol, subjects self-reported abstention from inhaling cannabis (>8 h) or ingesting cannabis (>12 h) prior to baseline measurement. RESULTS: At baseline, none of the occasional users had detectable cannabigerol (limit of detection = 0.2 µg/L), whereas cannabigerol was detectable post-smoking in 7 of 24 (29%). Among daily cannabis users, 2 of 32 (6%) had detectable cannabigerol at baseline, increasing to 21 of 32 (66%) post-smoking. The odds ratio for recent cannabis smoking associated with a detectable cannabigerol was 27 (95% confidence interval: 6.6, 110.3). In this mixed cohort of occasional and daily cannabis users, receiver operator characteristic curve analysis indicated that whole blood cannabigerol concentration of ≥ 0.2 µg/L had 96% specificity, 50% sensitivity, and 73% accuracy for identifying a 15-minute interval of ad libitum cannabis smoking initiated 30 minutes earlier. Post smoking blood Δ9-tetrahydrocannabinol (median = 5.6 µg/L in occasional users, 21.3 µg/L in daily users) was significantly correlated with post-smoking cannabigerol (P < 0.0001). CONCLUSION: Whole blood cannabigerol may have forensic utility as a highly specific albeit insensitive biomarker of recent cannabis smoking.

Blood cannabinoid molar metabolite ratios are superior to blood THC as an indicator of recent cannabis smoking.

INTRODUCTION: Cannabis use is a growing concern in transportation and workplace incidents. Because Δ9-tetrahydrocannabinol is detectable after acute psychoactive effects have resolved, it has limitations as an indicator of recent usage or potential impairment. METHODS: In an observational study of driving and psychomotor performance, we measured whole blood Δ9-tetrahydrocannabinol plus its metabolites 11-hydroxy-Δ9-tetrahydrocannabinol and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol by liquid chromatography with tandem mass spectrometry at baseline and 30 min after starting a 15-minute interval of smoking cannabis in 24 occasional and 32 daily cannabis smokers. We calculated two blood cannabinoid molar metabolite ratios: 1) [Δ9-tetrahydrocannabinol] to [11-nor-9-carboxy-Δ9-tetrahydrocannabinol] and 2) ([Δ9-tetrahydrocannabinol] + [11-hydroxy-Δ9-tetrahydrocannabinol]) to [11-nor-9-carboxy-Δ9-tetrahydrocannabinol]. We compared these to blood [Δ9-tetrahydrocannabinol] alone as indicators of recent cannabis smoking. RESULTS: Median Δ9-tetrahydrocannabinol concentrations increased from 0 ( 0.38). By comparison, a cut-point for Δ9-tetrahydrocannabinol of 5.3 µg/L yielded 88% specificity, 73% sensitivity, and 80% accuracy. CONCLUSIONS: In occasional and daily users, the blood cannabinoid molar metabolite ratios were superior to whole blood Δ9-tetrahydrocannabinol as indicators of recent cannabis smoking. We recommend measurement and reporting of Δ9-tetrahydrocannabinol, 11-hydroxy-Δ9-tetrahydrocannabinol, and 11-nor-9-carboxy-Δ9-tetrahydrocannabinol, and their molar metabolite ratios in forensic and safety investigations.

Simulated driving performance among daily and occasional cannabis users.

OBJECTIVE: Daily cannabis users develop tolerance to some drug effects, but the extent to which this diminishes driving impairment is uncertain. This study compared the impact of acute cannabis use on driving performance in occasional and daily cannabis users using a driving simulator. METHODS: We used a within-subjects design to observe driving performance in adults age 25 to 45 years with different cannabis use histories. Eighty-five participants (43 males, 42 females) were included in the final analysis: 24 occasional users (1 to 2 times per week), 31 daily users and 30 non-users. A car-based driving simulator (MiniSim™, National Advanced Driving Simulator) was used to obtain two measures of driving performance, standard deviation of lateral placement (SDLP) and speed relative to posted speed limit, in simulated urban driving scenarios at baseline and 30 min after a 15 min ad libitum cannabis smoking period. Participants smoked self-supplied cannabis flower product (15% to 30% tetrahydrocannabinol (THC). Blood samples were collected before and after smoking (30 min after the start of smoking). Non-users performed the same driving scenarios before and after an equivalent rest interval. Changes in driving performance were analyzed by repeated measures general linear models. RESULTS: Mean whole blood THC cannabinoids concentrations post smoking were use THC = 6.4 ± 5.6 ng/ml, THC-COOH = 10.9 ± 8.79 ng/mL for occasional users and THC = 36.4 ± 37.4 ng/mL, THC-COOH = 98.1 ± 90.6 ng/mL for daily users. On a scale of 0 to 100, the mean post-use score of subjective high was similar in occasional users and daily users (52.4 and 47.2, respectively). In covariate-adjusted analysis, occasional users had a significant increase in SDLP in the straight road segment from pre to post compared to non-users; non-users decreased by a mean of 1.1 cm (25.5 cm to 24.4 cm) while occasional users increased by a mean of 1.9 cm (21.7 cm to 23.6 cm; p = 0.02). Daily users also increased adjusted SDLP in straight road segments from baseline to post-use (23.2 cm to 25.0 cm), but the change relative to non-users was not statistically significant (p = 0.08). The standardized mean difference in unadjusted SDLP from baseline to post-use in the straight road segments comparing occasional users to non-users was 0.64 (95% CI 0.09 - 1.19), a statistically significant moderate increase. When occasional users were contrasted with daily users, the baseline to post changes in SDLP were not statistically significant. Daily users exhibited a mean decrease in baseline to post-use adjusted speed in straight road segments of 1.16 mph; a significant change compared to slight speed increases in the non-users and occasional users (p = 0.02 and p = 0.01, respectively). CONCLUSION: We observed a decrement in driving performance assessed by SDLP after acute cannabis smoking that was statistically significant only in the occasional users in comparison to the nonusers. Direct contrasts between the occasional users and daily users in SDLP were not statistically significant. Daily users drove slower after cannabis use as compared to the occasional use group and non-users. The study results do not conclusively establish that occasional users exhibit more driving impairment than daily users when both smoke cannabis ad libitum.

No Apparent Influence of Reward upon Visual Statistical Learning.

Humans are capable of detecting and exploiting a variety of environmental regularities, including stimulus-stimulus contingencies (e.g., visual statistical learning) and stimulus-reward contingencies. However, the relationship between these two types of learning is poorly understood. In two experiments, we sought evidence that the occurrence of rewarding events enhances or impairs visual statistical learning. Across all of our attempts to find such evidence, we employed a training stage during which we grouped shapes into triplets and presented triplets one shape at a time in an undifferentiated stream. Participants subsequently performed a surprise recognition task in which they were tested on their knowledge of the underlying structure of the triplets. Unbeknownst to participants, triplets were also assigned no-, low-, or high-reward status. In Experiments 1A and 1B, participants viewed shape streams while low and high rewards were "randomly" given, presented as low- and high-pitched tones played through headphones. Rewards were always given on the third shape of a triplet (Experiment 1A) or the first shape of a triplet (Experiment 1B), and high- and low-reward sounds were always consistently paired with the same triplets. Experiment 2 was similar to Experiment 1, except that participants were required to learn value associations of a subset of shapes before viewing the shape stream. Across all experiments, we observed significant visual statistical learning effects, but the strength of learning did not differ amongst no-, low-, or high-reward conditions for any of the experiments. Thus, our experiments failed to find any influence of rewards on statistical learning, implying that visual statistical learning may be unaffected by the occurrence of reward. The system that detects basic stimulus-stimulus regularities may operate independently of the system that detects reward contingencies.