Effects of Modification of Pain Protocol on Incidence of Post Operative Nausea and Vomiting.

BackgroundA Perioperative Surgical Home (PSH) care model applies a standardized multidisciplinary approach to patient care using evidence-based medicine to modify and improve protocols. Analysis of patient outcome measures, such as postoperative nausea and vomiting (PONV), allows for refinement of existing protocols to improve patient care. We aim to compare the incidence of PONV in patients who underwent primary total joint arthroplasty before and after modification of our PSH pain protocol.MethodsAll total joint replacement PSH (TJR-PSH) patients who underwent primary THA (n=149) or TKA (n=212) in the study period were included. The modified protocol added a single dose of intravenous (IV) ketorolac given in the operating room and oxycodone immediate release orally instead of IV Hydromorphone in the Post Anesthesia Care Unit (PACU). The outcomes were (1) incidence of PONV and (2) average pain score in the PACU. We also examined the effect of primary anesthetic (spinal vs. GA) on these outcomes. The groups were compared using chi-square tests of proportions.ResultsThe incidence of post-operative nausea in the PACU decreased significantly with the modified protocol (27.4% vs. 38.1%, p=0.0442). There was no difference in PONV based on choice of anesthetic or procedure. Average PACU pain scores did not differ significantly between the two protocols.ConclusionSimple modifications to TJR-PSH multimodal pain management protocol, with decrease in IV narcotic use, resulted in a lower incidence of postoperative nausea, without compromising average PACU pain scores. This report demonstrates the need for continuous monitoring of PSH pathways and implementation of revisions as needed

Gravitational waves: search results, data analysis and parameter estimation

The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra-short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients.Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.</div

Pain assessment tool with electrodermal activity for postoperative patients: Method validation study

Background: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.Objective: The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.Methods: The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models.Results: Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.Conclusions: We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities.</p

Demonstration of the temporal matter-wave Talbot effect for trapped matter waves

We demonstrate the temporal Talbot effect for trapped matter waves using ultracold atoms in an optical lattice. We investigate the phase evolution of an array of essentially non-interacting matter waves and observe matter-wave collapse and revival in the form of a Talbot interference pattern. By using long expansion times, we image momentum space with sub-recoil resolution, allowing us to observe fractional Talbot fringes up to 10th order.Comment: 17 pages, 7 figure

Pion, kaon, proton and anti-proton transverse momentum distributions from p+p and d+Au collisions at sNN=200\sqrt{s_{NN}} = 200 GeV

Identified mid-rapidity particle spectra of $\pi^{\pm}$, $K^{\pm}$, and $p(\bar{p})$ from 200 GeV p+p and d+Au collisions are reported. A time-of-flight detector based on multi-gap resistive plate chamber technology is used for particle identification. The particle-species dependence of the Cronin effect is observed to be significantly smaller than that at lower energies. The ratio of the nuclear modification factor ($R_{dAu}$) between protons $(p+\bar{p})$ and charged hadrons ($h$) in the transverse momentum range $1.2<{p_{T}}<3.0$ GeV/c is measured to be $1.19\pm0.05$(stat)$\pm0.03$(syst) in minimum-bias collisions and shows little centrality dependence. The yield ratio of $(p+\bar{p})/h$ in minimum-bias d+Au collisions is found to be a factor of 2 lower than that in Au+Au collisions, indicating that the Cronin effect alone is not enough to account for the relative baryon enhancement observed in heavy ion collisions at RHIC.Comment: 6 pages, 4 figures, 1 table. We extended the pion spectra from transverse momentum 1.8 GeV/c to 3. GeV/

Azimuthal anisotropy at RHIC: the first and fourth harmonics

We report the first observations of the first harmonic (directed flow, v_1), and the fourth harmonic (v_4), in the azimuthal distribution of particles with respect to the reaction plane in Au+Au collisions at the Relativistic Heavy Ion Collider (RHIC). Both measurements were done taking advantage of the large elliptic flow (v_2) generated at RHIC. From the correlation of v_2 with v_1 it is determined that v_2 is positive, or {\it in-plane}. The integrated v_4 is about a factor of 10 smaller than v_2. For the sixth (v_6) and eighth (v_8) harmonics upper limits on the magnitudes are reported.Comment: 6 pages with 3 figures, as accepted for Phys. Rev. Letters The data tables are at http://www.star.bnl.gov/central/publications/pubDetail.php?id=3

Omega-3 Fatty Acids Modify Human Cortical Visual Processing—A Double-Blind, Crossover Study

While cardiovascular and mood benefits of dietary omega-3 fatty acids such as docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) are manifest, direct neurophysiological evidence of their effects on cortical activity is still limited. Hence we chose to examine the effects of two proprietary fish oil products with different EPA∶DHA ratios (EPA-rich, high EPA∶DHA; DHA-rich) on mental processing speed and visual evoked brain activity. We proposed that nonlinear multifocal visual evoked potentials (mfVEP) would be sensitive to any alteration of the neural function induced by omega-3 fatty acid supplementation, because the higher order kernel responses directly measure the degree of recovery of the neural system as a function of time following stimulation. Twenty-two healthy participants aged 18–34, with no known neurological or psychiatric disorder and not currently taking any nutritional supplementation, were recruited. A double-blind, crossover design was utilized, including a 30-day washout period, between two 30-day supplementation periods of the EPA-rich and DHA-rich diets (with order of diet randomized). Psychophysical choice reaction times and multi-focal nonlinear visual evoked potential (VEP) testing were performed at baseline (No Diet), and after each supplementation period. Following the EPA-rich supplementation, for stimulation at high luminance contrast, a significant reduction in the amplitude of the first slice of the second order VEP kernel response, previously related to activation in the magnocellular pathway, was observed. The correlations between the amplitude changes of short latency second and first order components were significantly different for the two supplementations. Significantly faster choice reaction times were observed psychophysically (compared with baseline performance) under the EPA-rich (but not DHA-rich) supplementation, while simple reaction times were not affected. The reduced nonlinearities observed under the EPA-rich diet suggest a mechanism involving more efficient neural recovery of magnocellular-like visual responses following cortical activation

Neural correlates of attention-executive dysfunction in lewy body dementia and Alzheimer's disease.

Attentional and executive dysfunction contribute to cognitive impairment in both Lewy body dementia and Alzheimer's disease. Using functional MRI, we examined the neural correlates of three components of attention (alerting, orienting, and executive/conflict function) in 23 patients with Alzheimer's disease, 32 patients with Lewy body dementia (19 with dementia with Lewy bodies and 13 with Parkinson's disease with dementia), and 23 healthy controls using a modified Attention Network Test. Although the functional MRI demonstrated a similar fronto-parieto-occipital network activation in all groups, Alzheimer's disease and Lewy body dementia patients had greater activation of this network for incongruent and more difficult trials, which were also accompanied by slower reaction times. There was no recruitment of additional brain regions or, conversely, regional deficits in brain activation. The default mode network, however, displayed diverging activity patterns in the dementia groups. The Alzheimer's disease group had limited task related deactivations of the default mode network, whereas patients with Lewy body dementia showed heightened deactivation to all trials, which might be an attempt to allocate neural resources to impaired attentional networks. We posit that, despite a common endpoint of attention-executive disturbances in both dementias, the pathophysiological basis of these is very different between these diseases.This work was supported by an Intermediate Clinical Fellowship . Grant Number: (WT088441MA) to John‐Paul Taylor the National Institute for Health Research (NIHR), and Newcastle Biomedical Research Unit (BRU) based at Newcastle upon Tyne Hospitals NHS Trust, Newcastle University