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.

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.

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.

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.

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.

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.

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.

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.

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.

Establishing the interpretability and utility of the 4-item BriefPCS.

To reduce the patient burden associated with completing the 13-item Pain Catastrophizing Scale (PCS), the 4-item "BriefPCS" was developed. To date, no crosswalk has been developed that associates scores on the BriefPCS with PCS scores. Further, no study has compared the use of BriefPCS and PCS scores in a randomized clinical trial (RCT). We aimed to: (1) establish the interpretability of BriefPCS scores in reference to PCS scores, (2) compare the concurrent validity between the BriefPCS and PCS, and (3) asssess the use of BriefPCS in an RCT. First, we conducted equipercentile linking, created a crosswalk that associated scores of BriefPCS with PCS, and calculated differences between PCS and crosswalked PCS scores. Secondly, we compared Bootstrap correlation coefficients between PCS and self-reported measures of other domains. Lastly, we compared results from an RCT using BriefPCS scores versus PCS scores. Findings indicated that the correlation coefficient estimates with the BriefPCS and PCS scores were not significantly different. BriefPCS and PCS scores had similar ability to detect treatment-related changes. The BriefPCS scores validly, reliably, and accurately distinguish levels of pain catastrophizing. Additionally, the BriefPCS scores are sensitive to changes after behavioral interventions, with less respondent burden compared to the PCS scores.

Links between brain neuroimaging and blood inflammatory markers in urological chronic pelvic pain syndrome.

Urological chronic pelvic pain syndrome (UCPPS) is a debilitating painful condition with unclear etiology. Prior researchers have indicated that compared to healthy controls, patients with UCPPS demonstrated altered brain activity. Researchers have also shown that in UCPPS, several blood inflammatory markers relate to clinical variables of pain, fatigue, and pain widespreadness. However, how altered brain function in patients with UCPPS relates to blood inflammation remains unknown. To extend and connect prior findings of altered brain function and inflammatory factors in UCPPS, we conducted a secondary analysis of data from a cohort of UCPPS patients (N = 29) and healthy controls (N = 31) who provided both neuroimaging and blood data (National Institute of Health MAPP Research Network publicly available dataset). In our present study, we aimed to evaluate relationships between a priori-defined brain neuroimaging markers and inflammatory factors of interest and their relationships to pain-psychological variables. We hypothesized that two brain alterations of interest (i.e., PCC - left hippocampus functional connectivity and PCC - bilateral amygdala functional connectivity) would be correlated with four cytokine markers of interest: interleukin (IL) - 6, tumor necrosis factor-alpha (TNF-a), IL-8, and granulocyte-macrophage colony-stimulating factor (GM-CSF). In the UCPPS cohort, we identified a significant PCC - left hippocampus functional connectivity relationship with IL-6 (p = 0.0044). Additionally, in the UCPPS cohort, we identified a PCC - amygdala functional connectivity relationship with GM-CSF which did not meet our model's threshold for statistical significance (p = 0.0665). While these data are preliminary and cross-sectional, our findings suggest connections between brain function and levels of low-grade systemic inflammation in UCPPS. Thus, while further study is needed, our data indicate the potential for advancing the understanding of how brain functional circuits may relate to clinical symptoms and systemic inflammation.

The Stanford Medicine data science ecosystem for clinical and translational research.

Objective: To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research. Materials and Methods: The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training. Results: The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies. Discussion: Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users. Conclusion: Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure.

Preoperative Versus Perioperative Risk Factors for Delayed Pain and Opioid Cessation After Total Joint Arthroplasty: A Prospective Cohort Study.

INTRODUCTION: The evolution of pre- versus postoperative risk factors remains unknown in the development of persistent postoperative pain and opioid use. We identified preoperative versus comprehensive perioperative models of delayed pain and opioid cessation after total joint arthroplasty including time-varying postoperative changes in emotional distress. We hypothesized that time-varying longitudinal measures of postoperative psychological distress, as well as pre- and postoperative use of opioids would be the most significant risk factors for both outcomes. METHODS: A prospective cohort of 188 patients undergoing total hip or knee arthroplasty at Stanford Hospital completed baseline pain, opioid use, and emotional distress assessments. After surgery, a modified Brief Pain Inventory was assessed daily for 3 months, weekly thereafter up to 6 months, and monthly thereafter up to 1 year. Emotional distress and pain catastrophizing were assessed weekly to 6 months, then monthly thereafter. Stepwise multivariate time-varying Cox regression modeled preoperative variables alone, followed by all perioperative variables (before and after surgery) with time to postoperative opioid and pain cessation. RESULTS: The median time to opioid and pain cessation was 54 and 152 days, respectively. Preoperative total daily oral morphine equivalent use (hazard ratio-HR 0.97; 95% confidence interval-CI 0.96-0.98) was significantly associated with delayed postoperative opioid cessation in the perioperative model. In contrast, time-varying postoperative factors: elevated PROMIS (Patient-Reported Outcomes Measurement Information System) depression scores (HR 0.92; 95% CI 0.87-0.98), and higher Pain Catastrophizing Scale scores (HR 0.85; 95% CI 0.75-0.97) were independently associated with delayed postoperative pain resolution in the perioperative model. CONCLUSIONS: These findings highlight preoperative opioid use as a key determinant of delayed postoperative opioid cessation, while postoperative elevations in depressive symptoms and pain catastrophizing are associated with persistent pain after total joint arthroplasty providing the rationale for continued risk stratification before and after surgery to identify patients at highest risk for these distinct outcomes. Interventions targeting these perioperative risk factors may prevent prolonged postoperative pain and opioid use.

Characterization and burden of localized back pain versus back pain with chronic overlapping pain conditions.

BACKGROUND: Chronic low back pain (cLBP) is the most common cause of years lived with disability (YLD). Chronic overlapping pain conditions (COPCs) is a relatively new taxonomy for widespread pain. Researchers have postulated that patients with COPCs have more pain-related impact than those with isolated pain conditions. We know little about the combination of COPCs with cLBP. This study aims to characterize patients with isolated cLBP compared to those with cLBP and associated COPCs across multiple domains of physical, psychological, and social functioning. METHODS: Using Stanford's CHOIR registry-based learning health system, we performed a cross-sectional study on patients with localized cLBP (group L) versus cLBP with COPCs (group W). We used demographic, PROMIS (Patient-Reported Outcomes Measurement Information System), and legacy survey data to characterize the physical, psychological, social, and global health outcomes. We further subdivided the COPCs into intermediate and severe based on the number of body regions involved. We used descriptive statistics and generalized linear regression models to characterize and compare the pain groups. RESULTS: Among 8783 patients with cLBP, 485 (5.5%) had localized cLBP (Group L) without widespread pain. Compared to Group L, patients in Group W were more likely to be females, younger, and reported longer duration of pain. Although the mean pain scores were significantly higher in group W, this difference did not appear clinically significant (average pain scores MD -0.73, 95% CI [-0.91 to -0.55]). Group W had significantly worse outcomes in all PROMIS outcomes. However, outcomes with large clinical differences (Cohen's d > 0.5) were fatigue (MD = -7.0, 95% CI [-8.0 to -6.1]); sleep impairment (MD = -6.2, 95% CI [-7.1 to -5.3]); sleep disturbance (MD = -5.3, 95% CI [-6.2 to -4.5]); pain behavior (MD = -2.2, 95% CI [-2.5 to -1.8]); physical function (MD = 4.0, 95% CI [3.2-5.0]); pain interference (MD = -3.4, 95% CI [-4.0 to -2.8]); and anxiety (MD = -4.9, 95% CI [-5.7 to -4.0]). Adjusted analysis controlling for age, gender, BMI category, and duration of pain confirmed worsening of all outcomes with more widespread pain. CONCLUSION: COPCs are a common presentation with cLBP. The combination of COPCs with cLBP is associated with significantly worse physical, psychological, social, and global health outcomes. This information may identify patients with COPCs and cLBP to optimally risk and treatment stratify their care and individualize their management.

A Longitudinal Investigation of the Impact of COVID-19 on Patients With Chronic Pain.

The COVID-19 pandemic prompted unexpected changes in the healthcare system. This current longitudinal study had 2 aims: 1) describe the trajectory of pandemic-associated stressors and patient-reported health outcomes among patients receiving treatment at a tertiary pain clinic over 2 years (May 2020 to June 2022); and 2) identify vulnerable subgroups. We assessed changes in pandemic-associated stressors and patient-reported health outcome measures. The study sample included 1270 adult patients who were predominantly female (74.6%), White (66.2%), non-Hispanic (80.6%), married (66.1%), not on disability (71.2%), college-educated (59.45%), and not currently working (57.9%). We conducted linear mixed effect modeling to examine the main effect of time with controlling for a random intercept. Findings revealed a significant main effect of time for all pandemic-associated stressors except financial impact. Over time, patients reported increased proximity to COVID-19, but decreased pandemic-associated stressors. A significant improvement was also observed in pain intensity, pain catastrophizing, and PROMIS-pain interference, sleep, anxiety, anger, and depression scores. Demographic-based subgroup analyses for pandemic-associated stressors revealed that younger adults, Hispanics, Asians, and patients receiving disability compensation were vulnerable groups either during the initial visit or follow-up visits. We observed additional differential pandemic effects between groups based on participant sex, education level, and working status. In conclusion, despite unanticipated changes in pain care services during the pandemic, patients receiving pain treatments adjusted to pandemic-related stressors and improved their health status over time. As the current study observed differential pandemic impacts on patient subgroups, future studies should investigate and address the unmet needs of vulnerable subgroups. PERSPECTIVE: Over a 2-year timeframe, the pandemic did not adversely influence physical and mental health among treatment-seeking patients with chronic pain. Patients reported small but significant improvements across indices of physical and psychosocial health. Differential impacts emerged among groups based on ethnicity, age, disability status, gender, education level, and working status.

Blinded Pain Cocktails: A Reliable and Safe Opioid Weaning Method.

Weaning opioids in patients with noncancerous chronic pain often poses a challenge when psychosocial factors complicate the patient's chronic pain syndrome and opioid use. A blinded pain cocktail protocol used to wean opioid therapy has been described since the 1970s. At the Stanford Comprehensive Interdisciplinary Pain Program, a blinded pain cocktail remains a reliably effective medication-behavioral intervention. This review (1) outlines psychosocial factors that may complicate opioid weaning, (2) describes clinical goals and how to use blinded pain cocktails in opioid tapering, and (3) summarizes the mechanism of dose-extending placebos and ethical justification of its use in clinical practice.

Pragmatic Comparative Effectiveness Trials and Learning Health Systems in Pain Medicine: Opportunities and Challenges.

Large randomized clinical trials or aggregates of clinical trials represent the highest levels of clinical evidence because they minimize different sources of confounding and bias. The current review provides an in-depth discussion of the challenges faced and methods we can use to overcome these obstacles to tailor novel designs of pragmatic effectiveness trials to pain medicine. The authors describe their experiences with an open-source learning health system to collect high-quality evidence and conduct pragmatic clinical trials within a busy academic pain center.