Human Studies

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can result in considerable disability and pain. The metacarpophalangeal (MCP) joint is the most common diseased joint in RA. In clinical practice, MCP synovitis is commonly diagnosed on the basis of musculoskeletal ultrasound (MSUS) images. However, because of the vague criteria, the consistency in grading MCP synovitis based on MSUS images fluctuates between ultrasound imaging practitioners. Therefore, a new method for diagnosis of MCP synovitis is needed. Deep learning has developed rapidly in the medical area, which often requires a large-scale data set. However, the total number of MCP-MSUS images fell far short of the demand, and the distribution of different medical grades of images was unbalanced. With use of the traditional image augmentation methods, the diversity of the data remains insufficient. In this study, a high-resolution generative adversarial network (HRGAN) method that generates enough images for network training and enriches the diversity of the training data set is described. In comparison experiments, our proposed diagnostic system based on MSUS images provided more consistent results than those provided by clinical physicians. As the proposed method is image relevant, this study might provide a reference for other medical image classification research with insufficient data sets.

We deployed an online pain sensitivity questionnaire (PSQ) and an at-home version of the cold pressor test (CPT) in a large genotyped cohort. We performed genome-wide association studies (GWAS) on the PSQ score (25,321 participants) and CPT duration (6,853). We identified one new genome-wide significant locus associated with the PSQ score, which was located in the TSSC1 (also known as EIPR1) gene (rs58194899, OR = 0.950 [0.933-0.967], P-value = 1.9*10-8). Although high pain sensitivity measured by both PSQ and CPT was associated with individual history of chronic and acute pains, genetic correlation analyses surprisingly suggested an opposite direction: PSQ score was inversely genetically correlated neck and shoulder pain (rg = -0.71), rheumatoid arthritis (-0.68), and osteoarthritis (-0.38), and with known risk factors, such as the length of working week (-0.65), smoking (-0.36), or extreme BMI (-0.23). Gene-based analysis followed by pathway analysis showed that GWAS results were enriched for genes expressed in the brain and involved in neuronal development and glutamatergic synapse signaling pathways. Finally, we confirmed that females with red hair were more sensitive to pain and found that genetic variation in the MC1R gene was associated with an increase in self-perceived pain sensitivity as assessed by the PSQ.

The reliability of the Nordic Musculoskeletal Questionnaire (NMQ) has not been evaluated in an unselected general population. The aim of this population-based follow-up study was to estimate the reliability between a self-administered NMQ-based questionnaire and a face-to-face interview performed approximately two months later. To interpret the results, we assessed the 1-year prevalence of various pain musculoskeletal pain locations.

The prevalence of chronic musculoskeletal pain (CMSP) is high and rising. The multidimensional impact of CMSP on individuals necessitates multidisciplinary evidence-based strategies to prevent and manage chronic pain. Primary health care (PHC) is the first point of care in many healthcare systems and evidence implementation at this point is important. We aim to describe the process of development of a comprehensive list of evidence-based recommendations derived from different high-quality clinical practice guidelines (CPGs) to inform the PHC healthcare of adults with CMSP.

To address the growing burden of chronic pain, there is a need for national scale-up of community-based pain programs. Primary health networks (PHNs) are best placed to support this scale-up as commissioning bodies of health services. The aim of this eDelphi study was to establish expert consensus on best practice key elements of community-based pain programs and enablers important for program implementation and sustainability to support PHN decision making. A panel of experts was invited to complete three online survey rounds as part of a reactive eDelphi approach to provide feedback on the relevance and importance of proposed key elements and implementation enablers of community-based pain programs. Consensus of 70% agreement by experts was required for each survey round for items to remain, with comments from experts considered by the research team to agree on wording changes and the addition of new items. Ten experts (62.5%) completed all three survey rounds. Expert feedback resulted in a list of 18 best practice key elements of community-based pain program design and 14 program implementation enablers. Changes suggested by experts included the moving of items between lists, rephrasing of items and the addition of new items. The eDelphi results will serve as a resource for PHNs considering the commissioning of community-based pain programs and inform future research to assess the suitability and scalability of existing programs.

Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients.