A randomized trial to determine the minimum effective lidocaine volume for median nerve block using hydrodissection

Ultrasound-guided hydrodissection with 5% dextrose in water (DW5) creates a peri-nervous compartment, separating the nerve from the neighboring anatomical structures. The aim of this randomized study was to determine the minimum volume of lidocaine 2% with epinephrine 1:200,000 required when using this technique to achieve an effective median nerve block at the elbow in 95% of patients (MEAV95). Fifty-two patients scheduled for elective hand surgery received an ultrasound-guided circumferential perineural injection of 4 ml DW5 and an injection of local anesthetic (LA) following a biased coin up-and-down sequential allocation method. A successful block was defined as a light touch completely suppressed on the two distal phalanges of the index finger within a 30-min evaluation period. The MEAV95 of lidocaine 2% with epinephrine was 4 ml [IQR 3.5–4.0]. Successful median nerve block was obtained in 38 cases (82.6%) with median onset time of 20.0 [10.0–21.2] minutes (95% CI 15–20). The analgesia duration was 248 [208–286] minutes (95% CI 222–276). Using an ultrasound-guided hydrodissection technique with DW5, the MEAV95 to block the median nerve at the elbow with 2% lidocaine with epinephrine was 4 ml [IQR 3.5–4.0]. This volume is close to that usually recommended in clinical practice.Trial registration clinicaltrials.gov. NCT02438657, Date of registration: May 8, 2015.

Minimal Effective Dose of Ropivacaine Under Spinal Anesthesia for Cesarean Section After Failed Vaginal Delivery by Epidural Labor Analgesia

Background: The studies associated with EC50 of ropivacaine for spinal anesthesia are few worldwide during failed vaginal trial by epidural labor analgesia transfer to cesarean section. We preliminarily explore it to determine the minimum local analgesic dose (MLAD) of ropivacaine for spinal anesthesia during failed vaginal trial by epidural labor analgesia transfer to cesarean section(CS) and survey its adverse effect. Trial design: a sequential experimentMethods: The analgesia quality was defined as effective if VAS ( Visual Analogue Scale) score was less than 3 from 15 min after spinal anesthesia to the end of CS. The Brownlee up-and-down sequential allocation was used to estimate the MLAD of subarachnoid ropivacaine and its 95% confidence intervals during failed vaginal trial by epidural labor analgesia transfer to CS. Results: There were significant changes for the time to reach maximum sensory block, the time to reach maximum motor block and the duration from spinal anesthesia to starting operation and hypotension occurence (p＜0.05, p＜0.0001, respectively) between the effective group and ineffective group. Bradyarrhythmia, nausea, vomiting and chills were no significant changes between these two groups. The EC50 dose of f subarachnoid ropivacaine for failed vaginal trial conversion to CS by epidural labor analgesia was 8.2985 mg , and 95% CI( Confidence Interval) was 8.07947mg~8.52348mg.Conclusion: The MLAD of ropivacaine was 8.2985 mg ( 95% CI: 8.0795mg~ 8.5235mg) for spinal anesthesia for failed vaginal trial by epidural labor analgesia conversion to CS. It was indicated that 8.2985 mg ropivacaine by subarachnoid block for failed vaginal trial transfer to CS can provide satisfactory and safe analgesia to parturients with low incidence rate of side effects.Clinical Trial Registration: This clinical study has been registered at www.chictr.org.cn(ChiCTR1900027527).

Non-Parametric Stochastic Sequential Assignment With Random Arrival Times

We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that they may only accept at most n jobs over some reference time period. The decision-maker only has access to M independent realisations of the job arrival process. We propose an algorithm, Non-Parametric Sequential Allocation (NPSA), for solving this problem. Moreover, we prove that the expected reward returned by the NPSA algorithm converges in probability to optimality as M grows large. We demonstrate the effectiveness of the algorithm empirically on synthetic data and on public fraud-detection datasets, from where the motivation for this work is derived.

Addressing the Long-term Impact of ML Decisions via Policy Regret

Machine Learning (ML) increasingly informs the allocation of opportunities to individuals and communities in areas such as lending, education, employment, and beyond. Such decisions often impact their subjects' future characteristics and capabilities in an a priori unknown fashion. The decision-maker, therefore, faces exploration-exploitation dilemmas akin to those in multi-armed bandits. Following prior work, we model communities as arms. To capture the long-term effects of ML-based allocation decisions, we study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm. We focus on reward functions that are initially increasing in the number of pulls but may become (and remain) decreasing after a certain point. We argue that an acceptable sequential allocation of opportunities must take an arm's potential for growth into account. We capture these considerations through the notion of policy regret, a much stronger notion than the often-studied external regret, and present an algorithm with provably sub-linear policy regret for sufficiently long time horizons. We empirically compare our algorithm with several baselines and find that it consistently outperforms them, in particular for long time horizons.

One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study

In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs). The essential feature of SOMs, besides dimensional reduction into a discrete map, is the conservation of topology. In SOMs, two forms of learning are applied: competitive, by sequential allocation of sample observations to a winning node in the map, and cooperative, by the update of the weights of the winning node and its neighbors. By means of cooperative learning, the conservation of topology from the original data space to the reduced (typically 2D) map is achieved. Here, we compared the performance of one- and two-layer SOMs in the outlier representation task. The same stratified sampling was applied for both the one-layer and two-layer SOMs; although, stratification would only be relevant for the two-layer setting—to estimate the outlying mixture component detection power. Two distance measures between points in the map were defined to quantify the conservation of topology. The results of the experiment showed that the two-layer setting was more efficient in outlier detection while maintaining the basic properties of the SOM, which included adequately representing distances from the outlier component to the remaining ones.

Median Effective Volume of 0.5% Ropivacaine for Ultrasound-guided Costoclavicular Block

Background The median effective dose of ropivacaine required for producing an effective costoclavicular block has not yet been determined. The authors conducted this dose-finding study with the objective of determining the median effective dose of 0.5% ropivacaine required to produce a successful costoclavicular block for surgical anesthesia in 50% of the patients (ED50) as well as the calculated dose required for effective blockade in 95% of the patients (ED95). Methods This single-armed prospective study was conducted on 40 American Society of Anesthesiologists physical status I or II patients, aged 18 to 60 yr, with a body mass index of 18 to 30 kg/m2, scheduled to undergo forearm and hand surgeries under ultrasound-guided costoclavicular block. A volume of 0.5% ropivacaine administered in the costoclavicular space was determined using the sample up-and-down sequential allocation study design of binary response variables. The first patient received a volume of 26 ml of 0.5% ropivacaine. After a successful or unsuccessful block, the volume of local anesthetic was decreased or increased, respectively, by 2 ml in the next patient. Evaluation of sensory and motor block was performed every 5 min for 30 min and graded using a 3-point scale. Surgical anesthesia was considered to be successful if a minimum score of 14 was achieved and the surgeon was able to proceed with surgery without needing to supplement anesthesia. Results The volume of local anesthetic administered ranged from 8 to 26 ml. Centered isotonic regression with a bias-corrected Morris 95% CI derived by bootstrapping showed ED50 of 13.5 ml (95% CI, 11.5 to 15.4 ml) and ED95 of 18.9 ml (95% CI, 17.9 to 27.5 ml). Conclusions A 19-ml dose of 0.5% ropivacaine is likely to produce an effective ultrasound-guided costoclavicular block for providing adequate surgical anesthesia to 95% of the patients. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New