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.

Neuropsychology of chronic back pain managed with long-term opioid use.

Chronic pain is commonly treated with long-term opioids, but the neuropsychological outcomes associated with stable long-duration opioid use remain unclear. Here, we contrasted the psychological profiles, brain activity, and brain structure of 70 chronic back pain patients on opioids (CBP+O, average opioid exposure 6.2 years) with 70 patients managing their pain without opioids. CBP+O exhibited moderately worse psychological profiles and small differences in brain morphology. However, CBP+O had starkly different spontaneous brain activity, dominated by increased mesocorticolimbic and decreased dorsolateral-prefrontal activity, even after controlling for pain intensity and duration. These differences strongly reflected cortical opioid and serotonin receptor densities and mapped to two antagonistic resting-state circuits. The circuits' dynamics were explained by mesocorticolimbic activity and reflected negative affect. We reassessed a sub-group of CBP+O after they briefly abstained from taking opioids. Network dynamics, but not spontaneous activity, reflected exacerbated signs of withdrawal. Our results have implications for the management and tapering of opioids in chronic pain.

[Evidence-based interventions to treat chronic low back pain: treatment selection for a personalized medicine approach : German version]. / Evidenzbasierte Interventionen zur Behandlung von chronischem Schmerz im unteren Rücken ­ Therapieauswahl für einen personalisierten Behandlungsansatz : Deutsche Fassung.

INTRODUCTION: Chronic low back pain (cLBP) is highly prevalent in the United States and globally, resulting in functional impairment and lowered quality of life. While many treatments are available for cLBP, clinicians have little information about which specific treatment(s) will work best for individual patients or subgroups of patients. The Back Pain Research Consortium, part of the National Institutes of Health Helping to End Addiction Long-termSM (HEAL) Initiative, will conduct a collaborative clinical trial, which seeks to develop a personalized medicine algorithm to optimize patient and provider treatment selection for patients with cLBP. OBJECTIVE: The primary objective of this article is to provide an update on evidence-based cLBP interventions and describe the process of reviewing and selecting interventions for inclusion in the clinical trial. METHODS: A working group of cLBP experts reviewed and selected interventions for inclusion in the clinical trial. The primary evaluation measures were strength of evidence and magnitude of treatment effect. When available in the literature, duration of effect, onset time, carryover effect, multimodal efficacy, responder subgroups, and evidence for the mechanism of treatment effect or biomarkers were considered. CONCLUSION: The working group selected 4 leading, evidence-based treatments for cLBP to be tested in the clinical trial and for use in routine clinical treatment. These treatments include (1) duloxetine, (2) acceptance and commitment therapy, (3) a classification-based exercise and manual therapy intervention, and (4) a self-management approach. These interventions each had a moderate to high level of evidence to support a therapeutic effect and were from different therapeutic classes.

Hierarchical Clustering Applied to Chronic Pain Drawings Identifies Undiagnosed Fibromyalgia: Implications for Busy Clinical Practice.

Currently-used assessments for fibromyalgia require clinicians to suspect a fibromyalgia diagnosis, a process susceptible to unintentional bias. Automated assessments of standard patient-reported outcomes (PROs) could be used to prompt formal assessments, potentially reducing bias. We sought to determine whether hierarchical clustering of patient-reported pain distribution on digital body map drawings predicted fibromyalgia diagnosis. Using an observational cohort from the University of Pittsburgh's Patient Outcomes Repository for Treatment registry, which contains PROs and electronic medical record data from 21,423 patients (March 17, 2016-June 25, 2019) presenting to pain management clinics, we tested the hypothesis that hierarchical clustering subgroup was associated with fibromyalgia diagnosis, as determined by ICD-10 code. Logistic regression revealed a significant relationship between the body map cluster subgroup and fibromyalgia diagnosis. The cluster subgroup with the most body areas selected was the most likely to receive a diagnosis of fibromyalgia when controlling for age, gender, anxiety, and depression. Despite this, more than two-thirds of patients in this cluster lacked a clinical fibromyalgia diagnosis. In an exploratory analysis to better understand this apparent underdiagnosis, we developed and applied proxies of fibromyalgia diagnostic criteria. We found that proxy diagnoses were more common than ICD-10 diagnoses, which may be due to less frequent clinical fibromyalgia diagnosis in men. Overall, we find evidence of fibromyalgia underdiagnosis, likely due to gender bias. Coupling PROs that take seconds to complete, such as a digital pain body map, with machine learning is a promising strategy to reduce bias in fibromyalgia diagnosis and improve patient outcomes. PERSPECTIVE: This investigation applies hierarchical clustering to patient-reported, digital pain body maps, finding an association between body map responses and clinical fibromyalgia diagnosis. Rapid, computer-assisted interpretation of pain body maps would be clinically useful in prompting more detailed assessments for fibromyalgia, potentially reducing gender bias.

A Randomized Controlled Neuroimaging Trial of Cognitive Behavioral Therapy for Fibromyalgia Pain.

OBJECTIVE: Fibromyalgia (FM) is characterized by pervasive pain-related symptomatology and high levels of negative affect. Mind-body treatments such as cognitive behavioral therapy (CBT) appear to foster improvement in FM via reductions in pain-related catastrophizing, a set of negative, pain-amplifying cognitive and emotional processes. However, the neural underpinnings of CBT's catastrophizing-reducing effects remain uncertain. This randomized controlled mechanistic trial was designed to assess CBT's effects on pain catastrophizing and its underlying brain circuitry. METHODS: Of 114 enrolled participants, 98 underwent a baseline neuroimaging assessment and were randomized to 8 weeks of individual CBT or a matched FM education control (EDU) condition. RESULTS: Compared with EDU, CBT produced larger decreases in pain catastrophizing post treatment (P < 0.05) and larger reductions in pain interference and symptom impact. Decreases in pain catastrophizing played a significant role in mediating those functional improvements in the CBT group. At baseline, brain functional connectivity between the ventral posterior cingulate cortex (vPCC), a key node of the default mode network (DMN), and somatomotor and salience network regions was increased during catastrophizing thoughts. Following CBT, vPCC connectivity to somatomotor and salience network areas was reduced. CONCLUSION: Our results suggest clinically important and CBT-specific associations between somatosensory/motor- and salience-processing brain regions and the DMN in chronic pain. These patterns of connectivity may contribute to individual differences (and treatment-related changes) in somatic self-awareness. CBT appears to provide clinical benefits at least partially by reducing pain-related catastrophizing and producing adaptive alterations in DMN functional connectivity.

SuRxgWell: study protocol for a randomized controlled trial of telemedicine-based digital cognitive behavioral intervention for high anxiety and depression among patients undergoing elective hip and knee arthroplasty surgery.

BACKGROUND: Mood disorders (anxiety, depression), sleep disorders, and catastrophizing lead to increased post-operative pain perception, increase in postoperative opioid consumption, decreased engagement with physical activity, and increased resource utilization in surgical patients. Psychosocial disorders significantly affect postoperative outcome. Unfortunately, studies focused on perioperative psychological assessment and treatment are scarce. We propose to test whether digital cognitive behavioral intervention (dCBI) can help surgical patients. dCBI such as RxWell™ is a proven treatment for mood disorders in medical patients such as reducing depression in patients with inflammatory bowel disease. We hypothesize that RxWell™ will also be effective in surgical patients. This study aims to test whether RxWell™ can improve preoperative mood disorders and subsequently reduce postoperative pain and opioid requirement in patients scheduled for primary total hip and knee arthroplasty (THA, TKA). We named the trial as the SuRxgWell trial. METHODS: This is a randomized, controlled trial that will enroll primary and unilateral THA or TKA patients with anxiety and/or depression symptoms before surgery to receive the SuRxgWell dCBI program and investigate its impact on postoperative outcomes including postoperative pain, anxiety, depression, sleep disorder, and catastrophizing. After signing an informed consent, subjects will be screened using the PROMIS questionnaires, and subjects with a T-score of ≥ 60 on the short Patient-Reported Outcomes Measurement Information System (PROMIS) 4a Anxiety and/or short PROMIS 4a Depression questionnaires will be randomized to either usual care (control group) or the cognitive behavioral intervention, RxWell™, plus usual care (intervention group). The control group will receive information on how to locate tools to address anxiety and depression, whereas the intervention group will have access to SuRxgWell 1 month prior to surgery and up to 3 months after surgery. The allocation will be 3:1 (intervention to control). Investigators will be blinded, but research coordinators approaching patients and research subjects will not. The primary outcome will be day of surgery anxiety or depression symptoms measured with the PROMIS Short Form v1.0 -Anxiety 4a/Depression and Generalized Anxiety Disorder Measure (GAD-7) and Patient Health Questionnaire (PHQ-8). Secondary end points include measuring other health-related quality of life outcomes including sleep disturbance, fatigue, ability to participate in social roles, pain interference, cognitive function, pain catastrophizing, and physical function. Other secondary outcomes include collecting data about preoperative and postoperative pain scores, and pain medication usage, and orthopedic functional recovery at baseline, day of surgery, and 1, 2, and 3 months after the surgery with the Pain Catastrophizing Scale, the Knee injury and Osteoarthritis Outcome Score (KOOS), and Hip injury and Osteoarthritis Outcome Score (HOOS). In addition, subjects will be asked to complete a GAD-7 and PHQ-8 questionnaires bi-weekly (via the RxWell™ app for the interventional group or REDCAP for the control group). Data about postsurgical complications, and resource utilization will also be recorded. We will also receive monthly reports measuring the usage and engagement of RxWell use for each participant randomized to that arm. The primary hypotheses will be assessed with intention-to-treat estimates, and differences in primary outcome will be tested using independent two sample t-tests. This trial is registered to the ClinicalTrials.gov database (NCT05658796) and supported by the DAPM, UPMC Health Plan, and the NIH. DISCUSSION: Our trial will evaluate the feasibility of digital cognitive behavioral intervention as a perioperative tool to improve anxiety and depression before and after major orthopedic surgery in comparison to education. If digital cognitive behavioral intervention proves to be effective, this might have important clinical implications, reducing the incidence of chronic postsurgical pain and improving outcomes.

Telemedicine-Based Digital Cognitive Behavioral Intervention for Perioperative Anxiety and Depression for Total Knee Arthroplasty.

Introduction: Preoperative anxiety and depression have been shown to increase postoperative pain and opioid consumption by up to 50% in patients undergoing primary unilateral Total Knee Arthroplasty (TKA). We hypothesized that the use of a telemedicine-based digital Cognitive Behavioral Intervention program (RxWell®) started one month prior to surgery would control anxiety and depression prior to surgery. Materials and methods: This was a randomized, controlled trial that enrolled patients undergoing primary unilateral TKA. At least a month prior to surgery, patients who gave consent to participate were asked to complete PROMIS® (Patient-Reported Outcomes Measurement Information System) emotional anxiety short form 8a and PROMIS® emotional depression short form-8a questionnaires. Patients with T-scores of ≥ 57 were randomized to either a no intervention (control group) or a RxWell® program (treatment group) for a month prior to surgery. The primary outcome of this proof-of-concept study was the ability of the RxWell® to normalize patients' PROMIS anxiety T scores. Results: T scores for anxiety and depression among patients randomized to the RxWell® group significantly decreased from 64.3 ± 3.0 at the time of randomization to 58.5 ± 2.6 prior to surgery (n=5, p=0.006), whereas no changes in T scores were recorded in the control group (59.4 ± 4.2 at the time of randomization vs. 57.7 ± 6.2; n=6, p=0.559). Conclusion: These preliminary data suggest that the use of a RxWell® program represents an effective approach to control anxiety and depression prior to surgery. In contrast, it seems that in the absence of treatment, anxiety level remains similar over a month prior to surgery.

Using Natural Language Processing and Machine Learning to Identify Opioids in Electronic Health Record Data.

Purpose: This study evaluates the utility of machine learning (ML) and natural language processing (NLP) in the processing and initial analysis of data within the electronic health record (EHR). We present and evaluate a method to classify medication names as either opioids or non-opioids using ML and NLP. Patients and Methods: A total of 4216 distinct medication entries were obtained from the EHR and were initially labeled by human reviewers as opioid or non-opioid medications. An approach incorporating bag-of-words NLP and supervised ML classification was implemented in MATLAB and used to automatically classify medications. The automated method was trained on 60% of the input data, evaluated on the remaining 40%, and compared to manual classification results. Results: A total of 3991 medication strings were classified as non-opioid medications (94.7%), and 225 were classified as opioid medications by the human reviewers (5.3%). The algorithm achieved a 99.6% accuracy, 97.8% sensitivity, 94.6% positive predictive value, F1 value of 0.96, and a receiver operating characteristic (ROC) curve with 0.998 area under the curve (AUC). A secondary analysis indicated that approximately 15-20 opioids (and 80-100 non-opioids) were needed to achieve accuracy, sensitivity, and AUC values of above 90-95%. Conclusion: The automated approach achieved excellent performance in classifying opioids or non-opioids, even with a practical number of human reviewed training examples. This will allow a significant reduction in manual chart review and improve data structuring for retrospective analyses in pain studies. The approach may also be adapted to further analysis and predictive analytics of EHR and other "big data" studies.

Cannabinoid Therapy: Attitudes and Experiences of People With Chronic Pain.

OBJECTIVE: Clinical trials of cannabinoids for chronic pain have mixed and often inconclusive results. In contrast, many prospective observational studies show the analgesic effects of cannabinoids. This survey study aimed to examine the experiences/attitudes of individuals with chronic pain who are currently taking, have previously taken, or never taken cannabinoids for chronic pain to inform future research. METHODS: This study is based on a cross-sectional, web-based survey of individuals with self-reported chronic pain. Participants were invited to participate through an email that was distributed to the listservs of patient advocacy groups and foundations that engage individuals with chronic pain. RESULTS: Of the 969 respondents, 444 (46%) respondents reported currently taking, 213 (22%) previously taken, and 312 (32%) never taken cannabinoids for pain. Participants reported using cannabinoids to treat a wide variety of chronic pain conditions. Those currently taking cannabinoids (vs previously) more frequently reported: (1) large improvements from cannabinoids in all pain types, including particularly difficult-to-treat chronic overlapping pain conditions (eg, pelvic pain), (2) improvements in comorbid symptoms (eg, sleep), and (3) lower interference from side effects. Those currently taking cannabinoids reported more frequent and satisfactory communication with clinicians regarding cannabinoid use. Those never taken cannabinoids reported a lack of suggestion/approval of a clinician (40%), illegality (25%), and lack of FDA regulation (19%) as reasons for never trying cannabinoids. CONCLUSION: These findings underscore the importance of conducting high-quality clinical trials that include diverse pain populations and clinically relevant outcomes that if successful, could support FDA approval of cannabinoid products. Clinicians could then prescribe and monitor these treatments similarly to other chronic pain medications.

Research objectives and general considerations for pragmatic clinical trials of pain treatments: IMMPACT statement.

ABSTRACT: Many questions regarding the clinical management of people experiencing pain and related health policy decision-making may best be answered by pragmatic controlled trials. To generate clinically relevant and widely applicable findings, such trials aim to reproduce elements of routine clinical care or are embedded within clinical workflows. In contrast with traditional efficacy trials, pragmatic trials are intended to address a broader set of external validity questions critical for stakeholders (clinicians, healthcare leaders, policymakers, insurers, and patients) in considering the adoption and use of evidence-based treatments in daily clinical care. This article summarizes methodological considerations for pragmatic trials, mainly concerning methods of fundamental importance to the internal validity of trials. The relationship between these methods and common pragmatic trials methods and goals is considered, recognizing that the resulting trial designs are highly dependent on the specific research question under investigation. The basis of this statement was an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) systematic review of methods and a consensus meeting. The meeting was organized by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership. The consensus process was informed by expert presentations, panel and consensus discussions, and a preparatory systematic review. In the context of pragmatic trials of pain treatments, we present fundamental considerations for the planning phase of pragmatic trials, including the specification of trial objectives, the selection of adequate designs, and methods to enhance internal validity while maintaining the ability to answer pragmatic research questions.

Real-world data and evidence in pain research: a qualitative systematic review of methods in current practice.

The use of routinely collected health data (real-world data, RWD) to generate real-world evidence (RWE) for research purposes is a growing field. Computerized search methods, large electronic databases, and the development of novel statistical methods allow for valid analysis of data outside its primary clinical purpose. Here, we systematically reviewed the methodology used for RWE studies in pain research. We searched 3 databases (PubMed, EMBASE, and Web of Science) for studies using retrospective data sources comparing multiple groups or treatments. The protocol was registered under the DOI:10.17605/OSF.IO/KGVRM. A total of 65 studies were included. Of those, only 4 compared pharmacological interventions, whereas 49 investigated differences in surgical procedures, with the remaining studying alternative or psychological interventions or epidemiological factors. Most 39 studies reported significant results in their primary comparison, and an additional 12 reported comparable effectiveness. Fifty-eight studies used propensity scores to account for group differences, 38 of them using 1:1 case:control matching. Only 17 of 65 studies provided sensitivity analyses to show robustness of their findings, and only 4 studies provided links to publicly accessible protocols. RWE is a relevant construct that can provide evidence complementary to randomized controlled trials (RCTs), especially in scenarios where RCTs are difficult to conduct. The high proportion of studies reporting significant differences between groups or comparable effectiveness could imply a relevant degree of publication bias. RWD provides a potentially important resource to expand high-quality evidence beyond clinical trials, but rigorous quality standards need to be set to maximize the validity of RWE studies.

The Back Pain Consortium (BACPAC) Research Program Data Harmonization: Rationale for Data Elements and Standards.

OBJECTIVE: One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses. METHODS: Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and nontabular data (eg, imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium. RESULTS: Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected by use of questionnaires across projects. Other nonrequired domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed on the basis of the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging, and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC. CONCLUSIONS: BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs.

Back Pain Consortium (BACPAC): Protocol and Pilot Study Results for a Randomized Comparative-Effectiveness Trial of Antidepressants, Fear Avoidance Rehabilitation, or the Combination for Chronic Low Back Pain and Comorbid High Negative Affect.

OBJECTIVE: Patients with chronic low back pain (CLBP) and comorbid depression or anxiety disorders are highly prevalent. Negative affect (NA) refers to a combination of negative thoughts, emotions, and behaviors. Patients with CLBP with high NA have greater pain, worse treatment outcomes, and greater prescription opioid misuse. We present the protocol for SYNNAPTIC (SYNergizing Negative Affect & Pain Treatment In Chronic pain). DESIGN: A randomized comparative-effectiveness study of antidepressants, fear-avoidance rehabilitation, or their combination in 300 patients with CLBP with high NA. In the antidepressant- or rehabilitation-only arms, SYNNAPTIC includes an adaptive design of re-randomization after 4 months for nonresponders. SETTING: A multisite trial conducted in routine pain clinical treatment settings: pain clinics and physical and occupational therapy treatment centers. METHODS: Inclusion criteria include CLBP with elevated depression and anxiety symptoms. Antidepressant and rehabilitation treatments follow validated and effective protocols for musculoskeletal pain in patients with high NA. Power and sample size are based on superior outcomes of combination therapy with these same treatments in a 71-subject 4-arm pilot randomized controlled trial. CONCLUSIONS: SYNNAPTIC addresses the lack of evidence-based protocols for the treatment of the vulnerable subgroup of patients with CLBP and high NA. We hypothesize that combination therapy of antidepressants plus fear-avoidance rehabilitation will be more effective than each treatment alone. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT04747314.

The Back Pain Consortium (BACPAC) Research Program: Structure, Research Priorities, and Methods.

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-TermSM (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science.

Biobehavioral Assessments in BACPAC: Recommendations, Rationale, and Methods.

The Biobehavioral Working Group of BACPAC was charged to evaluate a range of psychosocial, psychophysical, and behavioral domains relevant to chronic low back pain, and recommend specific assessment tools and procedures to harmonize biobehavioral data collection across the consortium. Primary references and sources for measure selection were the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials, the Minimum Data Set from the National Institutes of Health (NIH) Research Task Force on Standards for Chronic Low Back Pain, the Patient-Reported Outcomes Measurement Information System, and NeuroQOL. The questionnaire's recommendations supplemented the NIH HEAL Common Data Elements and BACPAC Minimum Data Set. Five domains were identified for inclusion: Pain Characteristics and Qualities; Pain-Related Psychosocial/Behavioral Factors; General Psychosocial Factors; Lifestyle Choices; and Social Determinants of Health/Social Factors. The Working Group identified best practices for required and optional Quantitative Sensory Testing of psychophysical pain processing for use in BACPAC projects.

Expectations for Improvement: A Neglected but Potentially Important Covariate or Moderator for Chronic Pain Clinical Trials.

Variability in pain-related outcomes can hamper assay sensitivity of chronic pain clinical trials. Expectations of outcome in such trials may account for some of this variability, and thereby impede development of novel pain treatments. Measurement of participants' expectations prior to initiating study treatment (active or placebo) is infrequent, variable, and often unvalidated. Efforts to optimize and standardize measurement, analysis, and management of expectations are needed. In this Focus Article, we provide an overview of research findings on the relationship between baseline expectations and pain-related outcomes in clinical trials of pharmacological and nonpharmacological pain treatments. We highlight the potential benefit of adjusting for participants' expectations in clinical trial analyses and draw on findings from patient interviews to discuss critical issues related to measurement of expectations. We conclude with suggestions regarding future studies focused on better understanding the utility of incorporating these measures into clinical trial analyses. PERSPECTIVE: This focus article provides an overview of the relationship between participants' baseline expectations and pain-related outcomes in the setting of clinical trials of chronic pain treatments. Systematic research focused on the measurement of expectations and the impact of adjusting for expectations in clinical trial analyses may improve assay sensitivity.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations.

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Evidence-based interventions to treat chronic low back pain: treatment selection for a personalized medicine approach.

Introduction: Chronic low back pain (cLBP) is highly prevalent in the United States and globally, resulting in functional impairment and lowered quality of life. While many treatments are available for cLBP, clinicians have little information about which specific treatment(s) will work best for individual patients or subgroups of patients. The Back Pain Research Consortium, part of the National Institutes of Health Helping to End Addiction Long-termSM (HEAL) Initiative, will conduct a collaborative clinical trial, which seeks to develop a personalized medicine algorithm to optimize patient and provider treatment selection for patients with cLBP. Objective: The primary objective of this article is to provide an update on evidence-based cLBP interventions and describe the process of reviewing and selecting interventions for inclusion in the clinical trial. Methods: A working group of cLBP experts reviewed and selected interventions for inclusion in the clinical trial. The primary evaluation measures were strength of evidence and magnitude of treatment effect. When available in the literature, duration of effect, onset time, carryover effect, multimodal efficacy, responder subgroups, and evidence for the mechanism of treatment effect or biomarkers were considered. Conclusion: The working group selected 4 leading, evidence-based treatments for cLBP to be tested in the clinical trial and for use in routine clinical treatment. These treatments include (1) duloxetine, (2) acceptance and commitment therapy, (3) a classification-based exercise and manual therapy intervention, and (4) a self-management approach. These interventions each had a moderate to high level of evidence to support a therapeutic effect and were from different therapeutic classes.