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(1) Background: It is essential to focus attention on sex-specific factors which are clinically relevant in pain management, especially with regards to opioid use disorder (OUD) risk. The aim of this study was to explore potential sex-differences in chronic non-cancer pain (CNCP) outpatients. (2) Methods: An observational cross-sectional study was conducted under CNCP outpatients with long-term prescribed opioids ( = 806), wherein 137 patients had an OUD diagnosis (cases, 64% females) and 669 did not (controls, 66% females). Socio-demographic, clinical, and pharmacological outcomes were analyzed. (3) Results: Female controls presented an older age and less intensive pain therapy but higher psychotropic prescriptions and emergency department visits compared to male controls. Meanwhile, cases demonstrated a younger age, higher work disability, double morphine equivalent daily dose, and benzodiazepine use compared with controls. Here, female cases showed an 8% greater substance use disorder (OR 2.04 [1.11-3.76]) and 24% lower tramadol use, while male cases presented a 22% higher fentanyl use (OR 2.97 [1.52-5.81]) and reported the highest number of adverse drug reactions (24%, OR 2.40 [1.12-5.16]) compared with controls. (4) Conclusions: An OUD individual risk profile was evidenced with sex-differences to take into consideration to design equal prevention programs.
Learn More >Knee osteoarthritis (OA) is the most common form of OA and a leading cause of disability worldwide. Chronic pain and functional loss secondary to knee OA put patients at risk of developing depression, which can also impair their treatment response. However, no tools exist to assist clinicians in identifying patients at risk. Machine learning (ML) predictive models may offer a solution. We investigated whether ML models could predict the development of depression in patients with knee OA and examined which features are the most predictive.
Learn More >Neuropathic pain is amongst the most common non-communicable disorders and the poor effectiveness of current treatment is an unmet need. Although pain is a universal experience, there are significant inter-individual phenotypic differences. Developing models that can accurately recapitulate the clinical pain features is crucial to better understand underlying pathophysiological mechanisms and find innovative treatments. Current data from heterologous expression systems that investigate properties of specific molecules involved in pain signaling, and from animal models, show limited success with their translation into the development of novel treatments for pain. This is in part because they do not recapitulate the native environment in which a particular molecule functions, and due to species-specific differences in the properties of several key molecules that are involved in pain signaling. The limited availability of post-mortem tissue, in particular dorsal root ganglia (DRG), has hampered research using human cells in pre-clinical studies. Human induced-pluripotent stem cells (iPSCs) have emerged as an exciting alternative platform to study patient-specific diseases. Sensory neurons that are derived from iPSCs (iPSC-SNs) have provided new avenues towards elucidating peripheral pathophysiological mechanisms, the potential for development of personalized treatments, and as a cell-based system for high-throughput screening for discovering novel analgesics. Nevertheless, reprogramming and differentiation protocols to obtain nociceptors have mostly yielded immature homogenous cell populations that do not recapitulate the heterogeneity of native sensory neurons. To close the gap between native human tissue and iPSCs, alternative strategies have been developed. We will review here recent developments in differentiating iPSC-SNs and their use in pre-clinical translational studies. Direct conversion of stem cells into the cells of interest has provided a more cost- and time-saving method to improve reproducibility and diversity of sensory cell types. Furthermore, multi-cellular strategies that mimic in vivo microenvironments for cell maturation, by improving cell contact and communication (co-cultures), reproducing the organ complexity and architecture (three-dimensional organoid), and providing iPSCs with the full spatiotemporal context and nutrients needed for acquiring a mature phenotype (xenotransplantation), have led to functional sensory neuron-like systems. Finally, this review touches on novel prospective strategies, including fluorescent-tracking to select the differentiated neurons of relevance, and dynamic clamp, an electrophysiological method that allows direct manipulation of ionic conductances that are missing in iPSC-SNs.
Learn More >This is the English version of the Clinical Practice Guidelines for the Management of Atopic Dermatitis 2021. Atopic dermatitis (AD) is a disease characterized by relapsing eczema with pruritus as a primary lesion. In Japan, from the perspective of evidence-based medicine, the current strategies for the treatment of AD consist of three primary measures: (i) use of topical corticosteroids, tacrolimus ointment, and delgocitinib ointment as the main treatment of the inflammation; (ii) topical application of emollients to treat the cutaneous barrier dysfunction; and (iii) avoidance of apparent exacerbating factors, psychological counseling, and advice about daily life. In the present revised guidelines, descriptions of three new drugs, namely, dupilumab, delgocitinib, and baricitinib, have been added. The guidelines present recommendations to review clinical research articles, evaluate the balance between the advantages and disadvantages of medical activities, and optimize medical activity-related patient outcomes with respect to several important points requiring decision-making in clinical practice.
Learn More >Opioid use disorder is one of the most pressing public health problems of our time. Mobile health tools, including wearable sensors, have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with health or pathology. The current manuscript describes a longitudinal, observational study of adult patients receiving opioid analgesics for acute painful conditions. Participants in the study are monitored with a wrist-worn E4 sensor, during which time physiologic parameters (heart rate/variability, electrodermal activity, skin temperature, and accelerometry) are collected continuously. Opioid use events are recorded via electronic medical record and self-report. Three-hundred thirty-nine discreet dose opioid events from 36 participant are analyzed among 2070 h of sensor data. Fifty-one features are extracted from the data and initially compared pre- and post-opioid administration, and subsequently are used to generate machine learning models. Model performance is compared based on individual and treatment characteristics. The best performing machine learning model to detect opioid administration is a Channel-Temporal Attention-Temporal Convolutional Network (CTA-TCN) model using raw data from the wearable sensor. History of intravenous drug use is associated with better model performance, while middle age, and co-administration of non-narcotic analgesia or sedative drugs are associated with worse model performance. These characteristics may be candidate input features for future opioid detection model iterations. Once mature, this technology could provide clinicians with actionable data on opioid use patterns in real-world settings, and predictive analytics for early identification of opioid use disorder risk.
Learn More >Despite the high prevalence of a myofascial pain component in chronic pelvic pain (CPP) syndromes, awareness and management of this component are lacking among health care providers.
Learn More >Chronic overlapping pain conditions (COPCs) are a collection of chronic pain syndromes that often co-occur and are thought to share underlying nociplastic pathophysiology. Since they can manifest as seemingly unrelated syndromes they have historically been studied in isolation. Use of International Classification of Diseases (ICD) codes in medical records has been proposed as a means to identify and study trends in COPCs at the population level, however validated code sets are needed. Recently, a code set comprising ICD-10 codes as proxies for 11 COPCs was validated. The goal of this project was to validate a code set composed of ICD-9 codes for the identification of COPCs in administrative datasets. Data was extracted using the Electronic Medical Record Search Engine at the University of Michigan Health System from January 1st, 2011 to January 1st, 2015. The source population were patients with one of the candidate ICD-9 codes corresponding to various COPCs. Natural language searches were used as a reference standard. If code sets met a pre-specified threshold of agreement between ICD-9 codes and natural language searches (≥ 70%), they were retained and diagnostic accuracy statistics were calculated for each code set. Validated ICD-9 code sets were generated for 10 of the 11 COPCs evaluated. The majority had high levels of diagnostic accuracy, with all but one code set achieving ≥ 80% specificity, sensitivity, and predictive values. This code set may be used by pain researchers to identify COPCs using ICD-9 codes in administrative datasets.
Learn More >Osteoarthritis (OA) and chronic low back pain (CLBP) are common musculoskeletal disorders with substantial patient and societal burden. Nordic administrative registers offer a unique opportunity to study the impact of these conditions in the real-world setting. The Burden of Disease and Management of Osteoarthritis and Chronic Low Back Pain: Health Care Utilization and Sick Leave in Sweden, Norway, Finland and Denmark (BISCUITS) study was designed to study disease prevalence and the societal and economic burden in broad OA and CLBP populations.
Learn More >Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.
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