The impact of social isolation on pain interference : a longitudinal study

Online-first April 2018Background: Evidence suggests social interactions play an important role in pain perception. Purpose: The aim of this study was to determine whether social isolation (SI) in people with persistent pain determines pain interference (PI) and physical function over time. Methods: Patients seeking care at a tertiary pain management referral center were administered the Patient Reported Outcome Measurement Information System (PROMIS®) SI, PI, physical function, depression, and average pain intensity item banks at their initial consultation and subsequent visits as part of their routine clinical care. We used a post hoc simulation of an experiment using propensity score matching (n = 4,950) and carried out a cross-lagged longitudinal analysis (n = 312) of retrospective observational data. Results: Cross-lagged longitudinal analysis showed that SI predicted PI at the next time point, above and beyond the effects of pain intensity and covariates, but not vice versa. Conclusions: These data support the importance of SI as a factor in pain-related appraisal and coping and demonstrate that a comprehensive assessment of the individuals’ social context can provide a better understanding of the differential trajectories for a person living with pain. Our study provides evidence that the impact of pain is reduced in individuals who perceive a greater sense of inclusion from and engagement with others. This study enhances the understanding of how social factors affect pain and have implications for how the effectiveness of therapeutic interventions may be improved. Therapeutic interventions aimed at increasing social connection hold merit in reducing the impact of pain on engagement with activities

Serratus muscle stimulation effectively treats notalgia paresthetica caused by long thoracic nerve dysfunction: a case series

Currently, notalgia paresthetica (NP) is a poorly-understood condition diagnosed on the basis of pruritus, pain, or both, in the area medial to the scapula and lateral to the thoracic spine. It has been proposed that NP is caused by degenerative changes to the T2-T6 vertebrae, genetic disposition, or nerve entrapment of the posterior rami of spinal nerves arising at T2-T6. Despite considerable research, the etiology of NP remains unclear, and a multitude of different treatment modalities have correspondingly met with varying degrees of success. Here we demonstrate that NP can be caused by long thoracic nerve injury leading to serratus anterior dysfunction, and that electrical muscle stimulation (EMS) of the serratus anterior can successfully and conservatively treat NP. In four cases of NP with known injury to the long thoracic nerve we performed transcutaneous EMS to the serratus anterior in an area far lateral to the site of pain and pruritus, resulting in significant and rapid pain relief. These findings are the first to identify long thoracic nerve injury as a cause for notalgia paresthetica and electrical muscle stimulation of the serratus anterior as a possible treatment, and we discuss the implications of these findings on better diagnosing and treating notalgia paresthetica

Planet Populations as a Function of Stellar Properties

Exoplanets around different types of stars provide a window into the diverse environments in which planets form. This chapter describes the observed relations between exoplanet populations and stellar properties and how they connect to planet formation in protoplanetary disks. Giant planets occur more frequently around more metal-rich and more massive stars. These findings support the core accretion theory of planet formation, in which the cores of giant planets form more rapidly in more metal-rich and more massive protoplanetary disks. Smaller planets, those with sizes roughly between Earth and Neptune, exhibit different scaling relations with stellar properties. These planets are found around stars with a wide range of metallicities and occur more frequently around lower mass stars. This indicates that planet formation takes place in a wide range of environments, yet it is not clear why planets form more efficiently around low mass stars. Going forward, exoplanet surveys targeting M dwarfs will characterize the exoplanet population around the lowest mass stars. In combination with ongoing stellar characterization, this will help us understand the formation of planets in a large range of environments.Comment: Accepted for Publication in the Handbook of Exoplanet

Patient engagement in designing, conducting, and disseminating clinical pain research : IMMPACT recommended considerations

The consensus recommendations are based on the views of IMMPACT meeting participants and do not necessarily represent the views of the organizations with which the authors are affiliated. The following individuals made important contributions to the IMMPACT meeting but were not able to participate in the preparation of this article: David Atkins, MD (Department of Veterans Affairs), Rebecca Baker, PhD (National Institutes of Health), Allan Basbaum, PhD (University of California San Francisco), Robyn Bent, RN, MS (Food and Drug Administration), Nathalie Bere, MPH (European Medicines Agency), Alysha Croker, PhD (Health Canada), Stephen Bruehl, PhD (Vanderbilt University), Michael Cobas Meyer, MD, MBS (Eli Lilly), Scott Evans, PhD (George Washington University), Gail Graham (University of Maryland), Jennifer Haythornthwaite, PhD (Johns Hopkins University), Sharon Hertz, MD (Hertz and Fields Consulting), Jonathan Jackson, PhD (Harvard Medical School), Mark Jensen, PhD (University of Washington), Francis Keefe, PhD (Duke University), Karim Khan, MD, PhD, MBA (Canadian Institutes of Health Research), Lynn Laidlaw (University of Aberdeen), Steven Lane (Patient-Centered Outcomes Research Institute), Karen Morales, BS (University of Maryland), David Leventhal, MBA (Pfizer), Jeremy Taylor, OBE (National Institute for Health Research), and Lena Sun, MD (Columbia University). The manuscript has not been submitted, presented, or published elsewhere. Parts of the manuscript have been presented in a topical workshop at IASP World Congress on Pain in Toronto, in 2022.Peer reviewedPublisher PD

The ACTTION-APS-AAPM Pain Taxonomy (AAAPT) Multidimensional Approach to Classifying Acute Pain Conditions.

Objective: With the increasing societal awareness of the prevalence and impact of acute pain, there is a need to develop an acute pain classification system that both reflects contemporary mechanistic insights and helps guide future research and treatment. Existing classifications of acute pain conditions are limiting, with a predominant focus on the sensory experience (e.g., pain intensity) and pharmacologic consumption. Consequently, there is a need to more broadly characterize and classify the multidimensional experience of acute pain. Setting: Consensus report following expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM). Methods: As a complement to a taxonomy recently developed for chronic pain, the ACTTION public-private partnership with the US Food and Drug Administration, the APS, and the AAPM convened a consensus meeting of experts to develop an acute pain taxonomy using prevailing evidence. Key issues pertaining to the distinct nature of acute pain are presented followed by the agreed-upon taxonomy. The ACTTION-APS-AAPM Acute Pain Taxonomy will include the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Future efforts will consist of working groups utilizing this taxonomy to develop diagnostic criteria for a comprehensive set of acute pain conditions. Perspective: The ACTTION-APS-AAPM Acute Pain Taxonomy (AAAPT) is a multidimensional acute pain classification system designed to classify acute pain along the following dimensions: 1) core criteria, 2) common features, 3) modulating factors, 4) impact/functional consequences, and 5) putative pathophysiologic pain mechanisms. Conclusions: Significant numbers of patients still suffer from significant acute pain, despite the advent of modern multimodal analgesic strategies. Mismanaged acute pain has a broad societal impact as significant numbers of patients may progress to suffer from chronic pain. An acute pain taxonomy provides a much-needed standardization of clinical diagnostic criteria, which benefits clinical care, research, education, and public policy. For the purposes of the present taxonomy, acute pain is considered to last up to seven days, with prolongation to 30 days being common. The current understanding of acute pain mechanisms poorly differentiates between acute and chronic pain and is often insufficient to distinguish among many types of acute pain conditions. Given the usefulness of the AAPT multidimensional framework, the AAAPT undertook a similar approach to organizing various acute pain conditions

Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings