The Use of a New Device-Assisted Needle Guidance versus Conventional Approach to Perform Ultrasound Guided Brachial Plexus Blockade: A Randomized Controlled Study

Amaresh Vydyanathan,1 Priya Agrawal,2 Naveen Shetty,3 Singh Nair,1 Nancy Shilian,4 Naum Shaparin1 1Department of Anesthesiology and Pain Management, Montefiore Medical Center, Bronx, NY, USA; 2Sutter Health System, Oakland, CA, USA; 3Department of Anesthesiology, New York University, New York, NY, USA; 4Department of Family Medicine, Mount Sinai South Nassau Hospital, Oceanside, NY, USACorrespondence: Amaresh Vydyanathan, 1250 Waters Place, Tower II, 8th Floor, Bronx, NY, USA, Tel +216-702-5965, Fax +929-263-3950, Email [email protected]: Ultrasound guidance during nerve blockade poses the challenge of maintaining in-plane alignment of the needle tip. The needle guidance device maintains needle alignment and assists with in-plane needle visualization. The purpose of this study is to evaluate the utility of this device by comparing procedure performance during brachial plexus blockade with the conventional approach.Methods: After the Institutional Review Board approval and obtaining informed consent, 70 patients receiving either interscalene or supraclavicular nerve blocks were randomly assigned into 2 groups, a conventional approach versus utilizing the needle guidance device. An independent observer recorded: total procedure time; needle insertion time; number of unplanned redirections; and number of reinsertions. Additionally, physician satisfaction and ease of needle visualization were assessed.Results: Data from seventy patients were analyzed. The median [25th percentile-75th percentile] time to complete the block by the device assisted needle guidance group was 3 (2– 3.75) minutes and 4 (3– 6) minutes in the conventional approach group (p < 0.001). Additionally, subgroup analyses were performed in the supraclavicular block and interscalene block. Supraclavicular blockade, needle insertion time (median [25th percentile-75th percentile] in seconds) (106 [92– 162] vs 197 [140– 278]), total procedure time (3 [2– 3] vs 4.5 [4– 6] in minutes) and unplanned needle redirections (2 [1– 5] vs 5.5 [3– 9]) were significantly lower in needle guidance group (p < 0.001). With interscalene blockade, needle insertion time (86 [76– 146] vs 126 [94– 295]) and unplanned needle redirections (2 [1– 3] vs 4 [2– 8.5]) were significantly lower with needle guidance (p < 0.001), but total procedure time was similar. All the physicians reported that they would use the needle guidance again, and 90% would prefer it for in-plane blocks.Conclusion: Performing regional blocks using the needle guidance device reduces needle insertion time and unplanned needle redirections in brachial plexus blockade. Moreover, physician satisfaction also improved compared to the use of the conventional technique.Keywords: brachial plexus blockade, ultrasound guidance, peripheral nerve blockade, needle guidance, needle visualizatio

Defining Personas of People Living with Chronic Pain: An Ethnographic Research Study

Melissa Cullens,1 Cyan James,1 Meran Liu,1 Amaresh Vydyanathan,2– 4 Naum Shaparin,3– 5 Michael Schatman,6,7 Jacob Hascalovici2,3,5,8 1Clearing Relief Labs Inc., New York City, NY, USA; 2Relief Medical Group P.A, New York City, NY, USA; 3Department of Anesthesiology, Albert Einstein College of Medicine, Bronx, NY, USA; 4Multidisciplinary Pain Program, Montefiore Medical Center, Bronx, NY, USA; 5The Arthur S. Abramson Department of Physical Medicine and Rehabilitation, Albert Einstein College of Medicine, Bronx, NY, USA; 6Department of Anesthesiology, Perioperative Care and Pain Medicine, NYU Grossman School of Medicine, New York City, NY, USA; 7Department of Population Health – Division of Medical Ethics, NYU Grossman School of Medicine, New York City, NY, USA; 8Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USACorrespondence: Jacob Hascalovici, Relief Medical Group P.A, 169 Madison Ave, Suite 2412, New York City, New York City, 10016, USA, Email [email protected]: Pain is the leading reason for which people seek medical care in the United States, and chronic pain (CP) affects approximately 50 million people in the US Pain perception is deeply personal, is highly correlated with behavioral and emotional disorders, and is greatly influenced by physiological and environmental factors. The patient-provider relationship can have profound implications for clinical outcomes within the context of treating CP. However, limited access to pain specialists, the complex nature of many CP-causing conditions, the absence of instruments for objective pain measurement, and the need to foster a trust-based patient-provider relationship throughout treatment pose unique challenges.Objective: To support a more optimal CP care delivery system that leverages a healthy therapeutic patient-provider relationship, we systematically gathered deeper knowledge of the behaviors, interpersonal dynamics, home environment, values, and mindsets of people who experience CP.Methods: We employed ethnographic research methods to collect and analyze data on views, habits, strategies, attitudes, and life circumstances of a range of participants living with CP. We aggregated, analyzed, and summarized participant data to identify trends and similarities.Results: Our findings suggest that patients can be broadly categorized into five predominant pain typologies, or “personas”, which are characterized by respective symptom durations, care management preferences, values, communication styles, and behaviors.Conclusion: Identifying CP personas may enhance the ability to personalize CP care and help foster more robust therapeutic relationships, which may lead to greater trust, improved patient satisfaction, and better clinical outcomes.Keywords: chronic pain, personas, biopsychosocial, doctor–patient relationshi

Mapping filtering streaming applications

International audienceIn this paper, we explore the complexity of mapping filtering streaming applications on large-scale homogeneous and heterogeneous platforms, with a particular emphasis on communication models and their impact. Filtering applications are streaming applications where each node also has a selectivity which either increases or decreases the size of its input data set. This selectivity makes the problem of scheduling these applications more challenging than the more studied problem of scheduling "non-filtering" streaming workflows. We address the complexity of the following two problems: Evaluation: Given a mapping of nodes to processors, how can one compute the period and latency? Optimization: Given a filtering workflow, how can one compute the mapping and schedule that minimize the period or latency? A solution to this problem requires generating both the mapping and the associated operation list--the order in which each processor executes its assigned tasks. We address this general problem in two steps. First, we address the simplified model without communication cost. In this case, the evaluation problems are easy, and the optimization problems have polynomial complexity on homogeneous platforms. However, we show that the optimization problems become NP-hard on heterogeneous platforms. Second, we consider platforms with communication costs. Clearly, due to the previous results, the optimization problems on heterogeneous platforms are still NP-hard. Therefore we come back to homogeneous platforms and extend the framework with three significant realistic communication models. Now even evaluation problems become difficult, because the mapping must now be enriched with an operation list that provides the time-steps at which each computation and each communication occurs in the system: determining the best operation list has a combinatorial nature. Not too surprisingly, optimization problems are NP-hard too. Altogether, this paper provides a comprehensive overview of the additional difficulties induced by heterogeneity and communication costs

Multi-dimensional multiple query scheduling with distributed semantic caching framework

It is becoming more important to leverage a large number of distributed cache memory seamlessly in modern large scale systems. Several previous studies showed that traditional scheduling policies often fail to exhibit high cache hit ratio and to achieve good system load balance with large scale distributed caching facilities. To maximize the system throughput, distributed caching facilities should balance the workloads and leverage cached data at the same time. In this work, we present a distributed job processing framework that yields high cache hit ratio while achieving balanced system load. Our framework employs a scheduling policy-DEMA that considers both cache hit ratio and system load and it supports geographically distributed multiple job schedulers. We show collaborative task scheduling and the data migration can even further improve the performance by increasing the cache hit ratio while achieving good load balance. Our experiments show that the proposed job scheduling policies outperform legacy load-based job scheduling policy in terms of job response time, load balancing, and cache hit ratioclose0

A distributed task allocation algorithm for a multi-robot system in healthcare facilities

Various ambient assisted living (AAL) technologies have been proposed for improving the living conditions of elderly people. One of them is to introduce robots to reduce dependency on support staff. The tasks commonly encountered in a healthcare facility such as a care home for elderly people are heterogeneous and are of different priorities. A care home environment is also dynamic and new emergency priority tasks, which if not attended shortly may result in fatal situations, may randomly appear. Therefore, it is better to use a multi-robot system (MRS) consisting of heterogeneous robots than designing a single robot capable of doing all tasks. An efficient task allocation algorithm capable of handling the dynamic nature of the environment, the heterogeneity of robots and tasks, and the prioritisation of tasks is required to reap the benefits of introducing an MRS. This paper proposes Consensus Based Parallel Auction and Execution (CBPAE), a distributed algorithm for task allocation in a system of multiple heterogeneous autonomous robots deployed in a healthcare facility, based on auction and consensus principles. Unlike many of the existing market based task allocation algorithms, which use a time extended allocation of tasks before the actual execution is initialised, the proposed algorithm uses a parallel auction and execution framework, and is thus suitable for highly dynamic real world environments. The robots continuously resolve any conflicts in the bids on tasks using inter-robot communication and a consensus process in each robot before a task is assigned to a robot. We demonstrate the effectiveness of the CBPAE by comparing its simulation results with those of an existing market based distributed multi-robot task allocation algorithm and through experiments on real robots