The Problem of Pain in Rheumatology: Variations in Case Definitions Derived From Chronic Pain Phenotyping Algorithms Using Electronic Health Records.

OBJECTIVE: The aim of this study was to investigate and compare different case definitions for chronic pain to provide estimates of possible misclassification when researchers are limited by available electronic health record and administrative claims data, allowing for greater precision in case definitions. METHODS: We compared the prevalence of different case definitions for chronic pain (N = 3042) in patients with autoimmune rheumatic diseases. We estimated the prevalence of chronic pain based on 15 unique combinations of pain scores, diagnostic codes, analgesic medications, and pain interventions. RESULTS: Chronic pain prevalence was lowest in unimodal pain phenotyping algorithms: 15% using analgesic medications, 18% using pain scores, 21% using pain diagnostic codes, and 22% using pain interventions. In comparison, the prevalence using a well-validated phenotyping algorithm was 37%. The prevalence of chronic pain also increased with the increasing number (bimodal to quadrimodal) of phenotyping algorithms that comprised the multimodal phenotyping algorithms. The highest estimated chronic pain prevalence (47%) was the multimodal phenotyping algorithm that combined pain scores, diagnostic codes, analgesic medications, and pain interventions. However, this quadrimodal phenotyping algorithm yielded a 10% overestimation of chronic pain compared to the well-validated algorithm. CONCLUSION: This is the first empirical study to our knowledge that shows that established common modes of phenotyping chronic pain can lead to substantially varying estimates of the number of patients with chronic pain. These findings can be a reference for biases in case definitions for chronic pain and could be used to estimate the extent of possible misclassifications or corrections in using datasets that cannot include specific data elements.

The Problem of Pain in Lupus: Epidemiological Profiles of Patients Attending Multidisciplinary Pain Clinics.

INTRODUCTION: Patients with systemic lupus erythematosus (SLE) bear a significant burden of pain. We aimed to identify factors that distinguish patients with SLE referred to comprehensive pain clinics and those who are not. Characterizing this patient population will identify unmet needs in SLE management and inform efforts to improve pain care in rheumatology. METHODS: Among patients with SLE with ≥2 rheumatology clinic visits in a large hospital system from 1998 to 2023 (n = 1319), we examined factors that distinguished those who had at least one visit to multidisciplinary pain clinics (n = 77, 5.8%) from those who did not have any visits (n = 1242, 94.2%) with a focus on biopsychosocial and socioeconomic characteristics. We extracted demographic data and ICD-9/ICD-10 codes from the EHR. RESULTS: Patients with SLE attending the pain clinics exhibited characteristics including average older age (mean age ± SD: 54.1 ± 17.9 vs. 48.4 ± 19.9), a higher likelihood of relying on public health insurance (50.7% vs. 34.2%), and a greater representation of Black patients (9.1% vs. 4.4%) compared to SLE patients not seen in pain clinics. Nearly all patients seen at the pain clinics presented with at least one chronic overlapping pain condition (96.1% vs. 58.6%), demonstrated a higher likelihood of having a mental health diagnosis (76.7% vs. 42.4%), and exhibited a greater number of comorbidities (mean ± SD: 6.0 ± 3.0 vs. 2.9 ± 2.6) compared to those not attending the pain clinic. CONCLUSION: We found notable sociodemographic and clinical differences between these patient populations. Patients presenting with multiple comorbidities might benefit from further pain screening and referral to pain clinics to provide comprehensive care, and earlier referral could mitigate the development and progression of multimorbidities.

Utilizing a learning health system to capture real-world patient data: Application of the reliable change index to evaluate and improve the outcome of a pain rehabilitation program.

BACKGROUND AND OBJECTIVES: The learning healthcare system (LHS) has been developed to integrate patients' clinical data into clinical decisions and improve treatment outcomes. Having little guidance on this integration process, we aim to explain (a) an applicable analytic tool for clinicians to evaluate the clinical outcomes at a group and an individual level and (b) our quality improvement (QI) project, analyzing the outcomes of a new outpatient pain rehabilitation program ("Back-in-Action": BIA) and applying the analysis results to modify our clinical practice. METHODS: Through our LHS (CHOIR; https://choir.stanford.edu), we administered the Pain Catastrophizing Scale (PCS), Chronic Pain Acceptance Questionnaire (CPAQ), and Patient-Reported Outcomes Measures (PROMIS)® before and after BIA. After searching for appropriate analytic tools, we decided to use the Reliable Change Index (RCI) to determine if an observed change in the direction of better (improvement) or worse (deterioration) would be beyond or within the measurement error (no change). RESULTS: Our RCI calculations revealed that at least a 9-point decrease in the PCS scores and 10-point increase in the CPAQ scores would indicate reliable improvement. RCIs for the PROMIS measures ranged from 5 to 8 T-score points (i.e., 0.5-0.8 SD). When evaluating change scores of the PCS, CPAQ, and PROMIS measures, we found that 94% of patients showed improvement in at least one domain after BIA and 6% showed no reliable improvement. CONCLUSIONS: Our QI project revealed RCI as a useful tool to evaluate treatment outcomes at a group and an individual level, and RCI could be incorporated into the LHS to generate a progress report automatically for clinicians. We further explained how clinicians could use RCI results to modify a clinical practice, to improve the outcomes of a pain program, and to develop individualized care plans. Lastly, we suggested future research areas to improve the LHS application in pain practice.

Protocol for a randomised trial of a self-directed digital pain management intervention (Empowered Relief) tailored to adults with chronic pain and prescription opioid misuse/disorder: the MOBILE Relief study.

INTRODUCTION: Chronic pain increases the risk of prescription opioid misuse or opioid use disorder (OUD). Non-pharmacological treatments are needed to dually address pain and opioid risks. The purpose of the Mobile and Online-Based Interventions to Lessen Pain (MOBILE Relief) study is to compare a one-session, video-based, on-demand digital pain relief skills intervention for chronic pain ('Empowered Relief' (ER); tailored to people at risk for opioid misuse or with opioid misuse/OUD) to a one-session digital health education intervention ('Living Better'; no pain management skills). METHODS AND ANALYSIS: MOBILE Relief is an international online randomised controlled clinical trial. Study participants are adults with chronic, non-cancer pain (≥6 months) with daily pain intensity ≥3/10, taking ≥10 morphine equivalent daily dose and score ≥6 on the Current Opioid Misuse Measure. Participants are recruited through clinician referrals and clinic advertisements. Study procedures include electronic eligibility screening, informed consent, automated 1:1 randomisation to the treatment group, baseline measures, receipt of assigned digital treatment and six post-treatment surveys spanning 3 months. Study staff will call participants at baseline and 1-month and 3 months post-treatment to verify the opioid prescription. The main statistical analyses will include analysis of covariance and mixed effects model for repeated measurements regression. MAIN OUTCOMES: Primary outcomes are self-reported pain catastrophising, pain intensity, pain interference, opioid craving and opioid misuse at 1-month and 3 months post-treatment. We will determine the feasibility of ER (≥50% participant engagement, ≥70% treatment appraisal ratings). We hypothesise the ER group will be superior to the Living Better group in the reduction of multiprimary pain outcomes at 1-month post-treatment and opioid outcomes at 1-month and 3 months post-treatment. ETHICS AND DISSEMINATION: The study protocol was approved by the Stanford University School of Medicine Institutional Review Board (IRB 61643). We will publish results in peer-reviewed journals; National Institute of Drug Abuse (funder) and MOBILE Relief participants will receive result summaries. TRIAL REGISTRATION NUMBER: NCT05152134.

Recent developments and future avenues for human corticospinal neuroimaging.

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Is There an Association Between Lateralization of Chronic Pain in the Body and Depression?

Depression commonly co-occurs with chronic pain and can worsen pain outcomes. Recent theoretical work has hypothesized that pain localized to the left hemibody is a risk factor for worse depression due to overlap in underlying neural substrates. This hypothesis has not been tested a priori. Using a large sample of treatment-seeking adults with mixed-etiology chronic pain (N = 1,185), our cross-sectional study tested whether patients with left-sided pain endorse worse depressive symptoms. We also examined differences in other pain-related functioning measures. We tested 4 comparisons based on painful body areas using the CHOIR body map: 1) only left-sided (OL) versus any right-sided pain; 2) only right-sided (OR) versus any left-sided pain; 3) OL versus OR versus bilateral pain; and 4) more left-sided versus more right-sided versus equal-sided pain. Analysis of variance models showed OL pain was not associated with worse depression (F = 5.50, P = .019). Any left-sided pain was associated with worse depression, though the effect was small (F = 8.58, P = .003, Cohens d = .29). Bilateral pain was associated with worse depression (F = 8.05, P < .001, Cohens d = .24-.33). Regardless of pain location, more body areas endorsed was associated with greater depression. Although a more rigorous assessment of pain laterality is needed, our findings do not support the hypothesis that left-lateralized pain is associated with worse depression. PERSPECTIVE: Pain lateralized to the left side of the body has been hypothesized as a risk factor for worse depression in chronic pain, despite never being tested in a large, real-world sample of patients with chronic pain. Findings showed that more widespread pain, not pain laterality, was associated with worse depression.

Investigating the associations between lumbar paraspinal muscle health and age, BMI, sex, physical activity, and back pain using an automated computer-vision model: a UK Biobank study.

BACKGROUND CONTEXT: The role of lumbar paraspinal muscle health in back pain (BP) is not straightforward. Challenges in this field have included the lack of tools and large, heterogenous datasets to interrogate the association between muscle health and BP. Computer-vision models have been transformative in this space, enabling the automated quantification of muscle health and the processing of large datasets. PURPOSE: To investigate the associations between lumbar paraspinal muscle health and age, sex, BMI, physical activity, and BP in a large, heterogenous dataset using an automated computer-vision model. DESIGN: Cross-sectional study. PATIENT SAMPLE: Participants from the UK Biobank with abdominal Dixon fat-water MRI (N=9,564) were included (41.8% women, mean [SD] age: 63.5 [7.6] years, BMI: 26.4 [4.1] kg/m2) of whom 6,953 reported no pain, 930 acute BP, and 1,681 chronic BP. OUTCOME MEASURES: Intramuscular fat (IMF) and average cross-sectional area (aCSA) were automatically derived using a computer-vision model for the left and right lumbar multifidus (LM), erector spinae (ES), and psoas major (PM) from the L1 to L5 vertebral levels. METHODS: Two-tailed partial Pearson correlations were generated for each muscle to assess the relationships between the muscle measures (IMF and aCSA) and age (controlling for BMI, sex, and physical activity), BMI (controlling for age, sex, and physical activity), and physical activity (controlling for age, sex, and BMI). One-way ANCOVA was used to identify sex differences in IMF and aCSA for each muscle while controlling for age, BMI, and physical activity. Similarly, one-way ANCOVA was used to identify between-group differences (no pain, acute BP, and chronic BP) for each muscle and along the superior-inferior expanse of the lumbar spine while controlling for age, BMI, sex, and physical activity (α=0.05). RESULTS: Females had higher IMF (LM mean difference [MD]=11.1%, ES MD=10.2%, PM MD=0.3%, p<.001) and lower aCSA (LM MD=47.6 mm2, ES MD=350.0 mm2, PM MD=321.5 mm2, p<.001) for all muscles. Higher age was associated with higher IMF and lower aCSA for all muscles (r≥0.232, p<.001) except for LM and aCSA (r≤0.013, p≥.267). Higher BMI was associated with higher IMF and aCSA for all muscles (r≥0.174, p<.001). Higher physical activity was associated with lower IMF and higher aCSA for all muscles (r≥0.036, p≤.002) except for LM and aCSA (r≤0.010, p≥.405). People with chronic BP had higher IMF and lower aCSA than people with no pain (IMF MD≤1.6%, aCSA MD≤27.4 mm2, p<.001) and higher IMF compared to acute BP (IMF MD≤1.1%, p≤.044). The differences between people with BP and people with no pain were not spatially localized to the inferior lumbar levels but broadly distributed across the lumbar spine. CONCLUSIONS: Paraspinal muscle health is associated with age, BMI, sex, and physical activity with the exception of the association between LM aCSA and age and physical activity. People with BP (chronic>acute) have higher IMF and lower aCSA than people reporting no pain. The differences were not localized but broadly distributed across the lumbar spine. When interpreting measures of paraspinal muscle health in the research or clinical setting, the associations with age, BMI, sex, and physical activity should be considered.

Empowered Relief, cognitive behavioral therapy, and health education for people with chronic pain: a comparison of outcomes at 6-month Follow-up for a randomized controlled trial.

Introduction: We previously conducted a 3-arm randomized trial (263 adults with chronic low back pain) which compared group-based (1) single-session pain relief skills intervention (Empowered Relief; ER); (2) 8-session cognitive behavioral therapy (CBT) for chronic back pain; and (3) single-session health and back pain education class (HE). Results suggested non-inferiority of ER vs. CBT at 3 months post-treatment on an array of outcomes. Methods: Here, we tested the durability of treatment effects at 6 months post-treatment. We examined group differences in primary and secondary outcomes at 6 months and the degree to which outcomes eroded or improved from 3-month to 6-month within each treatment group. Results: Empowered Relief remained non-inferior to CBT on most outcomes, whereas both ER and CBT remained superior to HE on most outcomes. Outcome improvements within ER did not decrease significantly from 3-month to 6-month, and indeed ER showed additional 3- to 6-month improvements on pain catastrophizing, pain bothersomeness, and anxiety. Effects of ER at 6 months post-treatment (moderate term outcomes) kept pace with effects reported by participants who underwent 8-session CBT. Conclusions: The maintenance of these absolute levels implies strong stability of ER effects. Results extend to 6 months post-treatment previous findings documenting that ER and CBT exhibit similarly potent effects on outcomes.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

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

ABSTRACT: In the traditional clinical research model, patients are typically involved only as participants. However, there has been a shift in recent years highlighting the value and contributions that patients bring as members of the research team, across the clinical research lifecycle. It is becoming increasingly evident that to develop research that is both meaningful to people who have the targeted condition and is feasible, there are important benefits of involving patients in the planning, conduct, and dissemination of research from its earliest stages. In fact, research funders and regulatory agencies are now explicitly encouraging, and sometimes requiring, that patients are engaged as partners in research. Although this approach has become commonplace in some fields of clinical research, it remains the exception in clinical pain research. As such, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials convened a meeting with patient partners and international representatives from academia, patient advocacy groups, government regulatory agencies, research funding organizations, academic journals, and the biopharmaceutical industry to develop consensus recommendations for advancing patient engagement in all stages of clinical pain research in an effective and purposeful manner. This article summarizes the results of this meeting and offers considerations for meaningful and authentic engagement of patient partners in clinical pain research, including recommendations for representation, timing, continuous engagement, measurement, reporting, and research dissemination.

Methods for pragmatic randomized clinical trials of pain therapies: IMMPACT statement.

ABSTRACT: Pragmatic, randomized, controlled trials hold the potential to directly inform clinical decision making and health policy regarding the treatment of people experiencing pain. Pragmatic trials are designed to replicate or are embedded within routine clinical care and are increasingly valued to bridge the gap between trial research and clinical practice, especially in multidimensional conditions, such as pain and in nonpharmacological intervention research. To maximize the potential of pragmatic trials in pain research, the careful consideration of each methodological decision is required. Trials aligned with routine practice pose several challenges, such as determining and enrolling appropriate study participants, deciding on the appropriate level of flexibility in treatment delivery, integrating information on concomitant treatments and adherence, and choosing comparator conditions and outcome measures. Ensuring data quality in real-world clinical settings is another challenging goal. Furthermore, current trials in the field would benefit from analysis methods that allow for a differentiated understanding of effects across patient subgroups and improved reporting of methods and context, which is required to assess the generalizability of findings. At the same time, a range of novel methodological approaches provide opportunities for enhanced efficiency and relevance of pragmatic trials to stakeholders and clinical decision making. In this study, best-practice considerations for these and other concerns in pragmatic trials of pain treatments are offered and a number of promising solutions discussed. The basis of these recommendations was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) meeting organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks.