Journal Logo

Comprehensive Review

Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations

Edwards, Robert R.a,*; Dworkin, Robert H.b; Turk, Dennis C.c; Angst, Martin S.d; Dionne, Raymonde; Freeman, Roya; Hansson, Perf; Haroutounian, Simong; Arendt-Nielsen, Larsh; Attal, Nadinei; Baron, Ralfj; Brell, Joannak; Bujanover, Shayl; Burke, Laurie B.m,n; Carr, Danielo; Chappell, Amy S.p; Cowan, Penneyq; Etropolski, Milar; Fillingim, Roger B.s; Gewandter, Jennifer S.b; Katz, Nathaniel P.o,t; Kopecky, Ernest A.u; Markman, John D.b; Nomikos, Georgev; Porter, Lindaw; Rappaport, Bob A.x; Rice, Andrew S.C.y; Scavone, Joseph M.z; Scholz, Joachimaa; Simon, Lee; Smith, Shannon M.b; Tobias, Jeffreycc; Tockarshewsky, Tinadd; Veasley, Christineee; Versavel, Markff; Wasan, Ajay; Wen, Warrenhh; Yarnitsky, Davidii

Author Information
doi: 10.1097/j.pain.0000000000000602
  • Free


1. Introduction

Persistent pain is a serious therapeutic challenge and a public health epidemic; it is estimated to affect over 100 million American adults at any given time, is among the leading global causes of reduced quality of life,1 and carries direct and indirect costs of over 600 billion dollars annually in the U.S. alone.102 Patients are treated with a wide range of interventions, with analgesic medications among the most common treatments. However, long-term administration of analgesics such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioids involves risks of organ damage, overdose, and in some cases drug dependence and misuse syndromes.32,33,133,136,188 Such findings have stimulated intensive efforts to direct specific treatments to those patients who will demonstrate the most favorable risk-benefit profiles (ie, those who are most likely to experience meaningful analgesia and improvements in function, and least likely to experience serious side effects).

As has long been recognized, interpatient variability in analgesic outcomes (even for efficacious treatments) is impressively broad and can be the source of significant frustration in clinical trials as well as clinical practice.9,74,77 Numerous large, high-quality, randomized controlled trials (RCTs) of drugs for many chronic pain conditions have produced negative findings despite encouraging results from preclinical and early clinical studies. However, rather than a stark lack of efficacy, such results may indicate the presence of substantial patient heterogeneity, which obscures positive outcomes in certain subgroups of the study cohort. That is, within a diagnostic category (eg, postherpetic neuralgia [PHN], fibromyalgia [FM], osteoarthritis), multiple pain mechanisms and outcome-relevant patient characteristics may be active to varying degrees in different patients, leading to marked intersubject variation in treatment effects. This variability in phenotypic presentation of different pain syndromes is found to be greater between patients than between different pain syndromes (eg, Refs. 10,16,17), indicating that mechanistic etiologies and subsequent successful treatment are likely to be based at the level of the individual rather than at the level of the disease. In contrast to preclinical studies that focus on selective pharmacologic blockade of a single identified nociceptive mechanism, studies designed to facilitate phenotyping in clinical practice may need to assess (separately and in combination) numerous, multidimensional, potential contributors to the experience of pain. Collectively, this state of affairs has led to calls for personalized or tailored pain therapeutics, also termed precision medicine.16,74,202 Precision or personalized treatment approaches in pain medicine will presumably improve both clinical care of patients with persistent pain and the success rates for putative analgesic drugs in phase 2 and 3 RCTs (eg, trialists could perform baseline phenotyping and enrich the subsequent trial by selectively enrolling patients with phenotypes that are most likely to respond to the active agent being studied). A cornerstone of this approach is that the characteristics which render an individual patient, or subgroup of patients, more responsive to a specific treatment need to be identified.44 Similar profiling, or subgrouping, efforts are currently underway in other arenas of medicine as well; for example, this recent statement from a review of “individualized prediction of treatment effects” in the management of cardiovascular disease could have been easily drawn directly from the world of analgesic clinical trials:

The single estimate of effect provided in trials is an average group-level estimate, implicitly considering that every patient has an average risk, and the same average response to treatment. However, individual patients vary greatly in characteristics that affect the absolute benefit they will receive from treatment. Some will benefit more than average while others do not benefit or may even be harmed. Current practice is to administer the same treatment to a wide range of patients who are all presumed to resemble the “mean” patient behind the single point estimate of treatment effect. However, there are no average patients, and there is a wide range of treatment effects in individual patients.238

As noted in a recent review,74 this treatment-by-patient interaction is only one source of variability in observed RCT responses (others include within-patient variation over time), but it is clearly an important source of variability and potential negative impact on assay sensitivity. A challenging issue, and ongoing point of debate, is what measurable phenotypic characteristics of patients are most predictive of interpatient variability in analgesic treatment outcomes, and what measurement approaches are best suited to evaluate these characteristics. Although a great deal is known about the predictors of persistent pain and disability, less is known about the phenotypes that predict the responses to pain treatment, and we cannot assume that these factors, or factor combinations, are the same. Indeed, the absence of a unified conceptual model of pan phenotypes constitutes an important limitation within the field. We define phenotype as “The ensemble of observable characteristics displayed by an organism,” and note that while some definitions of phenotyping include the assessment of genetic features of an organism, we focus here exclusively on patient self-reported characteristics (eg, psychosocial functioning), patient-reported symptoms (eg, sleep disruption, neuropathic pain symptoms), and patients' verbal or behavioral responses to standardized provocation (eg, quantitative sensory testing [QST], which involves administration of precisely calibrated somatosensory stimuli). This necessarily limits the scope of the present review, and we realize that as our knowledge of the mechanisms underpinning the development and maintenance of chronic pain continues to grow, the importance of additional phenotypes may well become clearer. For example, neuroimaging-based markers of central sensitization provide crucial mechanistic and prognostic information regarding interindividual variability among patients with a variety of chronic pain syndromes (eg, chronic pelvic/abdominal pain37), and a recent functional MRI study of resting state connectivity revealed that pretreatment assessment of brain connectivity phenotypes among patients with FM was associated with subsequent response to oral analgesic medications and to placebo.211 We also recognize that all of the phenotypes discussed in the present review are shaped by genetic factors, as noted in recent reviews of the pain genetics literature,69,128 but a comprehensive treatment of pain genetics is beyond the scope of this article. See Table 1 for an index of the phenotypic domains covered here, as well as examples of specific measures.

Table 1
Table 1:
Core phenotyping domains and recommended measures.

2. Methods

In June 2013, the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT), a consortium of individual from academia, government agencies (eg, the U.S. Food and Drug Administration [FDA], National Institute on Drug Abuse, and Substance Abuse and Mental Health Services Administration), pharmaceutical companies, and patient advocacy and research organizations convened a 2-day meeting with the aim of developing recommendations for the domains and specific measures that should be applied in patient phenotyping for phase 2 and 3 analgesic clinical trials. Much of the evidence base derives from trials of analgesic medications, but these recommendations are envisioned as being generally applicable to nonpharmacologic trials as well. Meeting participants were selected for their international expertise in research, administration, policy, and clinical care related to measuring individual differences in patients with pain and/or conducting clinical trials. The meeting was intended to derive general recommendations that would be broadly applicable to numerous chronic pain conditions and treatment modalities; as a consequence, the composition of the meeting reflected a broad representation of relevant disciplines and perspectives (eg, anesthesiologists, neurologists, rheumatologists, psychologists, basic scientists, neuropathic pain experts, musculoskeletal pain experts, visceral pain experts), from a number of countries, while limiting the overall meeting size to promote fruitful and efficient discussion.

A set of background articles was circulated before the meeting to ensure that participants were familiar with relevant issues. In addition, background lectures were presented by several of the authors of this article (M.S.A., R.H.D., R.R.E., R.B.F., P.H., and S.H.) that covered a broad range of relevant clinical research design issues. After the meeting, additional literature searches were conducted, reviewed, and incorporated into the summary of the discussions and recommendations. Electronic versions of the manuscript were circulated to all authors and iteratively revised based on their input. Final agreement on the recommendations presented in this article was achieved through discussion at the meeting and iterative review of the draft manuscript by all of the authors. The final version of the manuscript was approved by all authors.

3. General considerations

Phase 2 and 3 RCTs assessing analgesics have traditionally been designed to demonstrate analgesic efficacy relative to placebo or an active comparator. However, such trials also represent a valuable opportunity to implement phenotyping methods that could promote rapid advances in the identification of patient subgroups, and subsequently, individualized pain management.

There are multiple benefits to developing a unified and standardized evidence-based set of recommendations for phase 2 and 3 trial phenotyping. Such benefits include the eventual refinement and standardized operational definition of a detailed pain taxonomy (which may cross current anatomically and etiologically based diagnostic boundaries90), the potential for pooling phenotypic and outcomes data across studies to achieve enhanced power for subgroup analysis, and the advancement of a science of personalized pain management (ie, by helping pain researchers to prioritize phenotyping targets from the nearly limitless array of potential contributors to interpatient variability in treatment outcomes). A recently proposed evidence-based, multidimensional approach to classifying chronic pain disorders has highlighted the momentum in the field away from traditional anatomically based clinical diagnosis90; this proposed taxonomy includes a dimension incorporating phenotypic neurobiological and psychosocial mechanisms, risk factors, and protective factors. Moreover, by identifying gaps in the evidence regarding prediction of pain trial outcomes, we believe that the present review will highlight important avenues for future pain research. Similar to previous IMMPACT meetings charged with developing recommendations for the use of specific measures in analgesic RCTs,75 presenters and meeting participants used a variety of criteria in evaluating potential phenotyping domains and instruments. These included (1) appropriateness of measure content; (2) reliability; (3) validity; (4) interpretability; (5) precision of scores; (6) respondent and administrator acceptability; and (7) respondent and administrator burden and feasibility. In addition, a central guiding criterion was (8) published evidence of predictive utility in one or more analgesic clinical trials (preferably RCTs, although the participants also considered evidence from longitudinal cohort studies).

Throughout the remainder of the article, when considering evidence of an association between phenotype and outcome in longitudinal treatment studies, we distinguish between 2 broad classes of effects. First, general predictive effects involve studies in which the phenotypic characteristic in question is either examined only within a single treatment group–as is often the case in prospective cohort studies of multidisciplinary pain management programs, for example, or is similarly associated with the outcomes from multiple treatments, potentially including placebo treatments. Second, treatment effect modification refers to findings in which a phenotypic characteristic is differentially associated with outcomes in different study treatment arms. Such effect-modification findings are also sometimes referred to as moderation (with the variables in question termed “moderator” variables18,245), and we use these terms interchangeably. This category of findings (ie, treatment effect modification or moderation) is far more conducive (than general predictive effects) to enhancing the assay sensitivity of analgesic trials, which relies on maximizing the separation between improvement in the active treatment group and that in the placebo group.74,77,78 For example, some evidence suggests that for many analgesic RCTs, a higher intensity of baseline pain is associated with an elevated probability of response to both active agent and placebo9,76,78; in this case, increasing the mean baseline pain intensity is unlikely to improve assay sensitivity. However, in the case of analgesic trials in patients with neuropathic pain conditions such as painful diabetic neuropathy and persistent postsurgical pain, more intense pain at baseline has been selectively associated with greater improvement in the active treatment arm vs placebo.78,272 Such findings highlight the sometimes selective nature of phenotyping effects and suggest, for these particular conditions, setting a trial entry criterion requiring a minimum pain intensity that is at least moderate in magnitude could increase power (or reduce the required sample size) by enhancing the effect size for the agent being studied.

Recent reviews have recommended careful attention to trial characteristics that might enhance assay sensitivity by, for example, reducing the magnitude of placebo effects.77 Here, we are concerned not with design features such as the length of the study, but with patient characteristics that might be selectively or differentially associated with greater responses to specific active treatment agents, or with reduced responsiveness to placebo treatments. Given that the mechanisms underlying placebo analgesia seem to differ from the analgesic effects produced by active agents such as opioids,8,23 it seems plausible that certain phenotypic characteristics might predict responses to one of those categories of treatment and not the other. Such a phenotype could then be used as part of the selection criteria for a phase 2 or 3 RCT to maximize the estimated standardized effect size (ie, the difference between the treatment and placebo group mean responses, divided by the SD of the outcome variable) of the trial. Note that many of the patient-level characteristics studied here are likely to be generally predictive of outcomes in a variety of diagnostic groups (eg, neuropathic pain conditions, musculoskeletal pain, visceral pain), although in some cases individual variables may offer more selective predictive value for specific treatments in particular chronic pain conditions. Collectively, a number of variables have been used to characterize or phenotype patients with a broad array of chronic pain diagnoses; these phenotyping variables include psychosocial factors, pain qualities and other symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge. The following sections of the article elaborate on phenotypic characteristics that have been studied as potential contributors to the assay sensitivity of putative pain-reducing treatments (Tables 1 and 2 for a summary of the recommended phenotyping measures).

Table 2
Table 2:
Selected supporting studies.
Table 2-A
Table 2-A:
Selected supporting studies.

In conducting this review, it is not our intention to criticize the existing pain RCT literature. Because of the enormous number of extraneous, uncontrolled (and potentially uncontrollable) factors that impact outcomes in analgesic RCTs, clinical trials examining group means in large numbers of subjects have been necessary to answer the straightforward question of whether there is a causal relationship between an intervention and an outcome. This approach has been crucial in identifying a number of medications (and, more generally, classes of medications) that, on average, produce significant benefit over placebo. We hope that this article will offer useful suggestions for further advancing the field by assisting investigators in selecting self-report phenotyping measures that have potential for influencing the outcomes of specific analgesic treatments. In general, this area of knowledge is not sufficiently mature to permit firm recommendations regarding patient selection at this point; rather, we offer suggestions for future trials intended to inform the field and eventually lead to such specific recommendations.

4. Phenotypic domains

4.1. Psychosocial factors

The overlap between affective disturbance and chronic pain is widely recognized.103,231 Across numerous studies, patients with a variety of chronically painful conditions generally have a several-fold increase in the risk of being diagnosed with a mood disorder. Longitudinal research also supports a strong bidirectional link between mood disorders and persistent pain; the development of an enduring pain condition confers a substantially increased risk for the subsequent diagnosis of an affective disorder, while psychosocial variables such as depression, anxiety, and distress are among the most potent and robust predictors of the transition from acute to chronic pain, especially musculoskeletal pain.82,157,175 Some evidence also suggests that high levels of negative affect and pain-specific distress are associated with reduced benefit from a variety of potentially pain-reducing treatments.82,249,250 This evidence is almost entirely from “general prediction” studies, that is, those studies that prospectively or retrospectively predict treatment responses in active treatment groups but not differences vs placebo or another active treatment. A number of relevant studies in this area involve studies of interpatient variability in pain trajectories following surgery. Recent findings related to persistent pain after joint replacement highlight the importance of assessing mental health, or psychosocial functioning, preoperatively,72,115,244 as patients with higher baseline levels of anxiety and depression report less benefit, more complications, and poorer function for years after total knee or total hip replacement.

One important note: before proceeding to recommend specific measures, we find it prudent to echo the sentiments of recent reviews that note that characterizing domains of variables as “psychological” or “psychosocial” refers principally to the method of assessment rather than the presumed underlying pathophysiologic mechanism that drives pain-related outcomes.69 For example, constructs such as somatic awareness and pain-related catastrophizing may partly reflect altered peripheral and central nervous system processing of sensory stimuli; these “psychological” features of patients are often significantly correlated with measures of somatosensory amplification on QST.

Collectively, while instruments assessing depression, anxiety, and distress have most often appeared as outcome measures in the pain RCT literature,75,228–230 emerging evidence suggests that pretreatment phenotyping of these patient symptoms can have important predictive effects.82 On the basis of a review of the literature of measures of emotional functioning used in phenotyping participants in analgesic trials, the Hospital Anxiety and Depression Scale (HADS) can be recommended as a core phenotyping measure for assessing general negative affect (see the background materials for IMMPACT-XVI at The HADS is a 14-item self-report questionnaire designed to assess symptoms of anxiety and depression in those with medical illness. It has well-established reliability and validity in the assessment of symptoms of depression and emotional distress, and it has been used in numerous clinical trials.180,218 It does not include somatic symptoms, such as fatigue and sleeplessness, which may otherwise be attributable to physical illness, and it has been standardized among large community samples. It has also been validated in several medical illness populations with good sensitivity and specificity for predicting DSM-IV major depression or generalized anxiety disorder diagnoses. Depending on the needs of the study and the degree of specificity required, HADS scores can be used to provide separate indices of anxious and depressive symptomatology,210 or a total HADS score may be used as an index of overall negative affect.130,250,251 There has, however, been some debate regarding the independence of the anxiety and depression subscales and the factor structure of the HADS.180

Importantly, several trials of opioid analgesics have noted that elevated pretreatment scores on the HADS are associated with reduced opioid analgesic benefit130,248,249 within the active treatment group. In addition, higher baseline HADS scores also predicted higher rates of medication misuse,248 an important outcome to consider in phase 2 and 3 trials of opioid analgesics. To date, the observed associations between baseline HADS scores and analgesic outcomes have been limited to the category of general predictive effects (eg, no study has yet shown that pretreatment HADS scores influence responses to an active agent but not placebo). Such findings are important, of course, and provide valuable information that is directly relevant to the clinical care setting (in which medications are not administered in a randomized blinded fashion); however, definitive conclusions about the potential for HADS scores to influence RCT assay sensitivity must await the results of effect-modification analyses. Issues of sample size also need to be considered, as many trials are not powered specifically to evaluate subgroup- or phenotype-specific outcomes. In the meantime, it is noteworthy that the predictive associations of HADS scores with pain-related outcomes extend beyond trials of opioid analgesics,29,48,142,234,250 although not all trials have shown a predictive effect of baseline HADS scores on pain treatment outcomes. For example, among patients with FM randomized to pregabalin,5 patients with high HADS scores benefitted as much as patients with lower HADS scores, suggesting that affective phenotypes may present drug-specific patterns of association (eg, high levels of distress may be prospectively associated with reduced opioid analgesia, but may have no impact on responses to other classes of medication).

The HADS is, of course, not the only psychometrically sound and widely used measure of emotional distress. Additional instruments such as the Depression, Anxiety, and Positive Outlook Scale,195 the Patient Health Questionnaire,7 the Generalized Anxiety Disorder 7-item Scale,174 or the Center for Epidemiological Studies–Depression scale (CES-D) are also likely to offer good phenotyping potential. The CES-D, a well-validated 20-item measure of depressive symptomatology, has been highly regarded by pain researchers, in part on the basis of its relative brevity, wide international use, and utility as a core measure in prospective studies of the transition from acute to chronic low back pain (LBP).194 Additional consideration should be afforded to the NIH-supported Patient-Reported Outcomes Measurement Information System (PROMIS) and the NIH Toolbox, a multidimensional set of brief measures assessing cognitive, emotional, motor, and sensory function across the lifespan. While still early in their developmental trajectory as phenotyping instruments, and while designed predominantly as outcome measures, they seem to be potentially valuable additions to the existing assessment tools.49 PROMIS pools self-report items tapping domains of physical, mental, and social health, into item banks and then uses item response theory and computer adaptive testing methods to provide precise measurement of individual symptom clusters, including domains of negative affect.192,193 To date, very few prospective studies of treatment for long-term pain have used a PROMIS scale as a phenotyping measure, although published protocols from some current trials suggest that PROMIS measures are beginning to be applied in these contexts (eg, a trial of spinal manipulation for LBP262). One recent observational cohort study of patients with LBP treated with epidural steroid injections reported that high basal levels of PROMIS-assessed negative affect were associated with reduced analgesic benefit, consistent with the previously cited HADS literature.139

In addition to measures of general negative affect, pain-specific cognitive and emotional processes have demonstrated importance in shaping pain outcomes and treatment responses. Catastrophizing is a pain-specific psychosocial construct comprising cognitive and emotional processes such as helplessness, pessimism, rumination about pain-related symptoms, and magnification of pain reports.82 While catastrophizing positively correlates with general measures of negative affect such as depressive symptoms and anxiety, it also shows a unique and specific influence on pain-related outcomes.82,145,196 Retrospective survey studies in patients with musculoskeletal pain have indicated that catastrophizing often emerges as one of the most important pretreatment variables predicting surgical outcomes,154,213 and a risk factor that impairs the effectiveness of pain-relieving interventions.123,138 Multiple RCTs in various neuropathic and musculoskeletal pain conditions have shown that patients with pain with high pretreatment catastrophizing report less benefit from topical analgesics,165 cortisone,163 an oral acetaminophen and tramadol combination,209 and psychosocial treatments such as cognitive behavioral therapy (CBT),68,232 although few of these studies tested for treatment effect modification. A recent study of patients with persistent temporomandibular joint pain, randomized to 6 weeks of either standard care or CBT and followed for 12 months, confirmed the long-term predictive effects of catastrophizing.158 Patients with high levels of pretreatment catastrophizing, and those whose catastrophizing scores did not change after treatment, were significantly more likely to be nonresponders at 1-year follow-up. Finally, while many of the above studies involve general outcome prediction, a recent RCT of transcutaneous electrical nerve stimulation (TENS) for postoperative pain reported strong effect-modification findings.200 Patients (n = 317) undergoing joint replacement surgery were randomized to receive TENS, placebo TENS, or standard care (no TENS) for 6 weeks. Those in the TENS group with high baseline catastrophizing scores showed less pain reduction and reduced range of motion at 6 weeks. In contrast, there was no predictive effect of catastrophizing in the other 2 groups (ie, those receiving placebo or standard care treatment).

When assessing catastrophizing, we recommend the use of the Pain Catastrophizing Scale,225 a 13-item, well-validated, self-report measure of catastrophic thinking associated with pain.82 The Pain Catastrophizing Scale has 3 subscales (Magnification, Rumination, and Helplessness), has good psychometric properties in patients with pain and pain-free controls,237 is the most commonly used measure of pain-related catastrophizing, and has been applied in samples of patients with neuropathic pain, musculoskeletal pain, visceral pain, and cancer-related pain.

4.1.1. Additional psychosocial factors for consideration

Expectations are a crucial component of placebo responses, but they also strongly influence the outcomes of active treatments, from surgery101,273 to opioid analgesics26 to complementary and alternative medicine approaches such as acupuncture.259 A recent analysis of multiple large acupuncture trials reveals that both patient and clinical expectations for treatment success are potent predictors of response258,259; we recommend that these be considered as phenotypic measures in clinical trials. Although many of the published studies of expectations use single-item assessments, multidimensional scales such as the Stanford Expectations of Treatment Scale270 may have the strongest psychometric properties. It is important to note that we are not recommending the manipulation of subject expectations, but rather their assessment as a potential contributor to trial outcomes and assay sensitivity.

Measures of somatization, somatic focus, or somatic awareness assess important phenotypic characteristics, particularly for patients with chronic pain conditions such as FM or temporomandibular joint disorders (TMDs).69,91,92 Findings from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, a large, high-quality, multisite prospective cohort study of the development of TMD, suggest that measures of somatic focus (eg, the somatization subscale of the Symptom Checklist-90 and the Pennebaker Inventory of Limbic Languidness) are among the strongest psychosocial predictors of the subsequent development of TMD.91 At present, there is a paucity of data from phase 2 and 3 analgesic RCTs pertaining to the use of somatization measures as phenotyping tools for the purpose of improving assay sensitivity in pain clinical trials, but we recommend considering the inclusion of such measures in a baseline phenotyping assessment. Collectively, while such factors have traditionally been most frequently studied in patients with chronic widespread or idiopathic pain disorders, abundant evidence also suggests their importance in shaping pain-related outcomes (including the transition from acute to chronic pain) for neuropathic pain conditions such as PHN73,140 or burning mouth syndrome.208

Finally, several studies suggest that the outcomes of various multidisciplinary or surgical treatments can be predicted by baseline assessment of neuropsychological measures that assess working memory, cognitive processing speed, and attention.13,110 Although we know of no phase 2 or 3 analgesic trials demonstrating similar predictive effects, the inclusion of such cognitive phenotyping measures may be considered in future work in this area. We should also note that the present article is only one in a long line of studies and classification systems that have suggested phenotyping, or clustering, patients on the basis of psychosocial characteristics, with the eventual goal of predicting treatment responses or other pain-related outcomes (eg, disability).24,25,45,132 Such efforts include measurement tools such as the West Haven-Yale Multidimensional Pain Inventory,144 which yields empirically validated subgroups of patients,206 the Örebro Musculoskeletal Pain Screening Questionnaire,31,156 which clusters patients according to their risk for developing persistent pain, the Treatment Outcomes in Pain Survey—Short Form (S-TOPS117), which phenotypes multiple physical and emotional pain-related domains, the STarT back tool, designed as a primary care screening instrument, which predicts recovery from acute back pain,124,257 as well as models such as the fear-avoidance model59,246 and the avoidance-endurance model.120,121 The comparison (and perhaps eventual integration) of these measures and models is unfortunately beyond the scope of the present work, but it is noteworthy that essentially all of these classification systems lean heavily on the assessment of negative affective symptoms (eg, depression, anxiety, distress) and maladaptive pain-related cognitions (eg, catastrophizing).205

4.2. Pain variability and pain qualities

There has recently been a great deal of interest in using electronic tools to perform real-time and more frequent assessment of pain than has traditionally been accomplished using assessment methods that require respondents to report retrospectively on pain levels over periods of time such as a week or month.172,214 As most pain conditions fluctuate spontaneously, sometimes over very short time scales, diary-based methods that record frequent current pain ratings have come into vogue, sometimes replacing recall-based questionnaires that query subjects about “usual” or “typical” or “average” pain levels. Previous IMMPACT reports have recommended further research on these real-time data capture methods for potential use as outcome measures in analgesic trials.77 The use of such methods also offers phenotyping opportunities, as patients differ widely in the degree of temporal variability in their ratings of pain intensity. To date, several RCTs have assessed baseline within-subject pain variability as a phenotypic predictor of trial outcomes in patients with musculoskeletal pain (eg, Ref. 119) as well as neuropathic pain.89

In an early study of patients with FM, pain variability was stable over time (ie, each subject tended to exhibit a characteristic degree of variability in pain intensity ratings that tended to remain the same over the course of the study, even if his or her mean pain intensity level changed), and individuals with greater variability were more likely to be classified as placebo responders (but were not more likely to respond to milnacipran, the active agent).119 Similar findings (ie, greater response to placebo, but not active treatment, among subjects with high baseline pain variability) are evident in RCTs in clinical trials of PHN and painful diabetic peripheral neuropathy (DPN89). Such effect-modification results might suggest that subjects with high pretreatment variability in pain intensity could be excluded from RCTs to minimize placebo responses and maximize assay sensitivity. In addition, a recent analysis of pooled data from 4 double-blind RCTs on the efficacy of topical capsaicin 8% vs an active control (capsaicin 0.04%) found that, despite the very different capsaicin concentrations, higher baseline pain variability was strongly associated with better responses in both groups.166 Collectively, while the limited research and conflicting findings prohibit firm recommendations about the use of pain variability as an inclusion or exclusion criterion for RCTs, we recommend considering an index of temporal variability in pain intensity as part of the baseline phenotyping of trial participants.

Other aspects of patient-reported pain symptoms are also potentially important targets of phenotyping. In this article, we leave aside consideration of patients' average baseline pain intensity, as this topic has been treated extensively in previous IMMPACT reviews.75,77,78,80 However, as the complex nature of pain symptomatology is increasingly recognized, there has been a rapid increase in the number of questionnaires that measure an array of pain quality descriptors (eg, “burning,” “shooting,” “aching”).79,134,155 Two of these scales in particular include a wide range of the most commonly used descriptors in samples of patients with pain, the revised Short Form McGill Pain Questionnaire (SF-MPQ-2), and the Pain Quality Assessment Scale (PQAS). Both measures are brief, psychometrically sound, and well validated in multiple neuropathic and nonneuropathic patient samples.79,134,159,186 Moreover, one recent PQAS study of effect modification in a sample of patients with neuropathic pain found that a number of PQAS items, assessed at baseline, were associated with response to pregabalin (but not with response to placebo) in an RCT.99 In particular, patients who rated their pain as paroxysmal, deep, electrical, and radiating (along with several other descriptors) reported greater analgesic benefit from pregabalin (but there was no association with placebo benefits), highlighting the potential predictive benefits of comprehensively phenotyping patients' self-report of pain qualities. Similarly, in an effect-modification study of individual differences in analgesic responses to intravenous lidocaine treatment, patients with a particular pain quality phenotype on the short form MPQ (ie, those reporting their pain as “heavy”) were disproportionately likely to obtain good analgesic responses,47 but this phenotype did not influence placebo responses. Collectively, on the basis of these initial effect-modification findings, we recommend the use of either the PQAS or the SF-MPQ-2 for a brief but comprehensive self-report evaluation of pain qualities.

4.2.1. Neuropathic pain symptom reporting instruments

Other self-report instruments targeting descriptions of pain types or pain qualities have been designed specifically to screen for and assess neuropathic pain, defined by an International Association for the Study of Pain publication as “pain arising as a direct consequence of a lesion or disease affecting the somatosensory system.”135 Neuropathic pain may affect up to 10% of the general population239 and is a common target of phase 2 and 3 analgesic trials. A review by the European Federation of Neurological Societies noted that the lack of specificity of instruments (for identifying neuropathic pain) such as the original McGill questionnaire has led to the development and validation of a number of largely self-report screening tools with improved sensitivity and specificity for identifying the presence of a neuropathic pain condition.60,61 As summarized by Haanpaa et al.,112 measures such as the Leeds assessment of neuropathic symptoms and signs (LANSS), Douleur neuropathique en 4 questions (DN4), and painDETECT questionnaire are generally relatively effective (often showing adequate sensitivities and specificities for identifying neuropathic pain), convenient, brief methods for assessing the presence and symptomatology of neuropathic pain conditions.97,113,116,233 However, we should note that recent reviews have identified some limitations in the reliability and validity of these measures, especially in the cross-cultural adaptations of the questionnaires as well as their transdiagnostic specificity for identifying neuropathic pain. For diagnostic purposes, self-report screening instruments should not replace a comprehensive clinical examination.169

While the screening tools described above have most often been used either as screening measures in epidemiologic studies or as outcome measures in clinical trials, Attal and colleagues9 have recently observed that a potentially more important contribution relates to phenotypic profiling to enhance therapeutic prediction. The examination of neuropathic symptom patterns with assessment tools permits the classification of patients into subgroups (eg, those with vs without mechanical allodynia), with the assumption that these subgroups have different underlying pain mechanisms and hence will respond differentially to interventions with varying mechanisms of action. Because these assessment methods capture various pain descriptors and qualities of neuropathic pain, they also can be used to characterize patients' sensory abnormalities. Some tools such as the Neuropathic Pain Symptom Inventory (NPSI) have been specifically validated for this purpose. So far, the NPSI and painDETECT have been most extensively used in neuropathic pain studies to subgroup patients according to their pattern of sensory abnormalities.10,17,21,97,148,160 These studies used a hierarchical cluster analysis or factor analysis to identify prevalent patterns or dimensions of sensory symptoms that occur commonly among patients with a variety of neuropathic pain conditions, including DPN, PHN, central pain syndromes, and painful radiculopathy. It is noteworthy that the painDETECT (and other similar instruments) has been used to subtype not just patients with neuropathic pain but also individuals with chronic musculoskeletal pain conditions such as FM,148 osteoarthritis,171 axial LBP,95 and persistent posttraumatic pelvic pain.105 To date, almost no phase 2 and 3 trials seem to have used prospective phenotyping as an inclusion criterion (for an exception, see Refs. 19,20), consistent with the relative infancy of this field. However, several encouraging trials that use post hoc clustering of patients have appeared in the literature over the past several years.

In a phase 3 trial of tapentadol for chronic LBP, painDETECT and the NPSI were used to phenotype neuropathic pain symptoms at baseline, and some differentially large improvements were observed on several quality of life subscales among the group identified as likely having a neuropathic component to their pain.223 Even more recently, a pooled post hoc analysis (assessing potential effect-modification findings) was performed with baseline NPSI data from 4 large phase 3 trials of pregabalin.97 Cluster analysis produced subgroups of patients with specific patterns of neuropathic pain symptoms, and several of the NPSI-identified subgroups had greater pain improvement after taking pregabalin than did those who took placebo. Interestingly, a randomized, double-blind comparison of pregabalin and duloxetine in patients with diabetic neuropathic pain suggested that the cluster of patients with the lowest baseline NPSI scores (ie, the least neuropathic pain symptoms) responded better to duloxetine than to pregabalin (P = 0.002 for the comparison of 8-week pain reduction), while the cluster with the highest baseline NPSI scores reported equivalent benefit from the 2 medications.35 This type of effect-modification evidence is particularly tantalizing in the context of neuropathic pain, which almost certainly involves multiple mechanisms and broad interpatient variability. At least one trial has also investigated the predictive effects of specific NPSI dimensions in an effect-modification analysis.67 This RCT of the sodium channel blocker oxcarbazepine in patients with peripheral neuropathic pain noted that the subgroup of patients reporting “paroxsymal” and “burning” pain symptoms on the NPSI at baseline showed significantly better pain reduction with oxcarbazepine than placebo (P = 0.002 for the interaction of baseline phenotype with treatment67). Finally, a phase 3 trial of prolonged-release (PR) tapentadol (in which patients with a positive initial response to tapentadol PR were randomized to continuation of tapentadol PR or to tapentadol PR plus pregabalin) in patients with chronic LBP used neuropathic symptom profiling as an inclusion criterion. Only patients with a painDETECT score of 13 or above were enrolled; outcome analyses suggested strong improvements in painDETECT and NPSI scores in patients treated with tapentadol PR and with tapentadol PR plus pregabalin.19,20

Additional evidence for the potential benefits of these phenotyping measures derives from a recent retrospective study (general prediction) of outcomes following dorsal root entry zone lesioning114; patients with the highest baseline painDETECT scores reported the worst long-term outcomes. Moreover, data from a large uncontrolled general prediction study of high-concentration capsaicin patches showed that higher baseline painDETECT scores were associated with greater pain reduction after 12 weeks of treatment in patients with chronic neuropathic pain.126 Collectively, these findings strongly suggest that specific neuropathic pain phenotypes may be associated with differential responses to varying analgesic treatments. Patients with the greatest degree of pretreatment neuropathic pain symptoms might respond best to pregabalin, or topical capsaicin treatment, whereas those reporting the least baseline neuropathic symptoms might benefit most from duloxetine, for example, as in Ref. 35. Such conclusions are presently tentative at best and require replication and additional head-to-head comparisons of active treatments. Some of these neuropathic pain measures have been developed and tested for use in particular diagnostic groups of patients. In the domain of chronic back pain, the Standardized Evaluation of Pain (StEP), which consists of 6 interview questions and 10 physical tests, has been used to evaluate the neuropathic components of spinal pain and to distinguish axial LBP from back pain with signs of a radiculopathy.212 Indeed, StEP has recently been applied as part of a screening neurological examination to evaluate participant eligibility in an RCT focused on patients with radiculopathy-related neuropathic pain.187 Other validated tools such as the Michigan Neuropathy Screening Instrument are also increasingly used to assess specific neuropathic conditions (eg, distal peripheral neuropathy in diabetes).34

Given the generally positive evidence for their validity, their ease of use, and their reasonable sensitivity and specificity, use of the NPSI and/or painDETECT is recommended for screening for neuropathic pain phenotypes or characterizing/subgrouping sensory profiles of patients with neuropathic pain. In samples of patients with LBP, the StEP could be considered to identify radicular pain, although painDETECT and NPSI have been more widely used to phenotype neuropathic LBP in phase 2 and 3 trials.19,20,223 It is important to note that these recommended phenotypic measures assess constructs that overlap with other domains as well. Self-report of neuropathic pain symptoms on screening measures correlates with QST findings,97 catastrophizing,125 sleep disruption, and measures of emotional distress.12,34 It is presently unclear precisely how all of these potentially interrelated phenotyping measures might be studied as predictors of analgesic outcomes in a single trial; we strongly encourage additional clinical studies in this area.

4.3. Sleep and fatigue

Experimental, clinical, and epidemiologic studies have suggested that sleep disruption or deprivation has a variety of negative effects within the general population and in pain-specific samples, including enhanced pain sensitivity, reduced pain inhibition, elevated chronic pain severity and disability, and an increase in the frequency and impact of daily musculoskeletal pains.81,93,183 In longitudinal studies, individuals with sleep disturbance are at elevated long-term risk for developing clinically relevant pain, especially persistent musculoskeletal pain, and most researchers in the field have concluded that pain and sleep disruption exhibit reciprocal bidirectional influences.93 It is also clear that insomnia and its associated symptoms are a major contributor to poor pain-related quality of life; an IMMPACT survey found that trouble falling asleep, trouble staying asleep, and feeling tired are 3 of the top 10 importance-rated domains for individuals with persistent pain.230

While it has become clear that sleep and pain often improve together,70 the presence of concurrent changes over the course of treatment does not necessarily imply that pretreatment sleep phenotype predicts analgesic outcomes in an RCT. However, several interventional studies have provided general evidence for such an association. Among patients with chronic orofacial pain undergoing multidisciplinary pain management, participants with poorer sleep and more fatigue were less likely to be treatment responders at follow-up.110 It is also noteworthy that in preclinical studies, sleep-deprived animals derive reduced analgesic benefit from opioids and at least one controlled human study has shown similar effects, with fatigued/“sleepy” participants showing no effect of codeine on pain thresholds, in contrast to nonsleepy subjects.224 Interestingly, a post hoc analysis of data pooled from 16 placebo-controlled trials of pregabalin in patients with neuropathic pain conditions (ie, DPN or PHN) revealed that, among thousands of patients, one of the best predictors of pregabalin-associated pain reduction was a high degree of sleep disruption at baseline.242,243 This small set of apparently disparate findings again suggests that phenotypic measures of sleep disturbance are likely to have treatment-specific predictive effects (eg, patients with severe insomnia may benefit most from pregabalin and least from opioids). This is a fertile area for future research, as multiple reviews suggest that assessment of sleep-related factors may provide important predictive phenotypic information about individual patients with an array of acute and persistent pain conditions.93,96

For assessing sleep when phenotyping patients in clinical trials, self-report instruments such as the Pittsburgh Sleep Quality Index39 and the Insomnia Severity Index22 can be recommended. These are widely used instruments with good psychometric properties that have been validated in individuals with chronic pain disorders.221 The Pittsburgh Sleep Quality Index and Insomnia Severity Index have well-established cutoff criteria demarcating good from poor sleep and clinical insomnia, and these have been validated in a variety of neuropathic and musculoskeletal pain samples.93 An “objective” measurement of sleep may also be considered, as a patient's self-report may differ from polysomnography- or actigraphy-derived indices, especially in patients with persistent pain.184 Wrist actigraphs provide a 24-hour measure of motor activity that decreases sharply during sleep. They are convenient and unobtrusive and are increasingly being used in sleep and pain research, showing prospective associations with postsurgical pain261 and with daily variation in long-term pain.181

In addition to indices of sleep, a measure of fatigue should be administered; as noted by the Outcome Measures in Rheumatology (OMERACT) group, simple visual analog scales and several multiitem measures such as the Multidimensional Fatigue Inventory show good reliability and validity and have been widely recommended for use as outcome measures.147 These would be a reasonable choice for phenotyping fatigue; the Multidimensional Fatigue Inventory in particular has been used in multiple pharmacologic treatment studies of patients with chronic pain.6,170 Sleep disruption and fatigue often co-occur within symptom clusters in the context of a variety of persistent pain conditions,176,241 but to date, no published studies seem to have examined pretreatment fatigue phenotypes as predictors of analgesic outcomes.

4.4. Quantitative sensory testing and sensory profiling

Quantitative sensory testing refers to a set of psychophysical methods used to quantify somatosensory function. It is based on measurements (using standardized response scales) of responses to calibrated, graded, innocuous, or noxious stimuli (generally mechanical or thermal) and represents an extension and refinement of the bedside clinical examination of the sensory system. Quantitative sensory testing has been used for decades in a variety of research settings, often for the purpose of diagnosing and monitoring sensory neuropathies and pain disorders, as well as for the investigation of pain mechanisms, the characterization of somatosensory profiles in various pain disorders, and the elucidation of individual differences in pain sensitivity and pain modulation.4,15,15,62,161,190,203,204 Quantitative sensory testing allows the assessment of specific sensory modalities that correspond to distinct receptors, peripheral nerve fibers, and their corresponding central nervous system pathways which are common to many persistent pain conditions. It has been most widely used for testing of cutaneous sensations, but it has also been adapted to test sensations from deep tissue and viscera, allowing broad application to an array of pain conditions.4 Quantitative sensory testing may be used to quantify and monitor the presence and severity of either positive sensory phenomena (eg, allodynia and hyperalgesia) or negative sensory phenomena (eg, hypoesthesia and hypoalgesia). Collectively, the past 20 years have witnessed a veritable explosion of QST research, with large annual increases in the number of peer-reviewed QST publications appearing on PubMed.27

A handful of recent large studies have applied QST to patients with a variety of pain syndromes (often neuropathic pain conditions) to examine sensory profiles or subgroups.97,106,161 Many of these studies use the German Research Network on Neuropathic Pain (DFNS) testing protocol, which is highly standardized and reliable,104,191,203,204 and which includes the assessment of a broad variety of parameters, such as detection thresholds for thermal and mechanical stimuli, pain thresholds, temporal summation of mechanical noxious stimuli, and dynamic mechanical allodynia.

In general, the recent “profiling” studies of large groups of patients with neuropathic pain have determined that (1) the vast majority of subjects exhibit at least 1 sensory abnormality on QST,17 which is expected, given that many diagnostic criteria require positive or negative sensory symptoms/signs, (2) every somatosensory abnormality occurs with a non-zero frequency across every pain condition studied to date, and (3) no particular QST profile is unique to a given pain diagnosis.17,97,106,161 These observed “trans-etiological” patterns of sensory symptoms and deficits may reflect separate but overlapping pain mechanisms, which may eventually be a fruitful target for specific therapeutic approaches.

In addition to its utilization for characterizing and profiling, QST has also been applied in a number of predictive contexts. Preoperative individual differences in pain sensitivity and somatosensory function have shown prospective associations with acute and chronic postoperative pain in studies of postsurgical pain across a number of procedures from amputation to cesarean section to bunionectomy.109,143 Such findings highlight the potential value of QST in these settings (eg, patients with a particular QST profile might experience reduced risk for persistent postoperative pain if managed with particular pre-, peri-, or post-operative analgesic regimens), but it is presently unclear whether these results can be applied to the realm of phase 2 and 3 RCTs in patients with persistent pain. In the context of other conditions such as DPN and chemotherapy-induced neuropathy, QST has proven itself to be a sensitive predictor of clinical deterioration (eg, the development of foot ulcers in patients with diabetes) or the worsening of neuropathy.15

To date, relatively few phase 2 and 3 analgesic RCTs have used baseline phenotyping by QST to predict treatment response. However, some promising findings are emerging9,17 from the handful of recent diverse neuropathic pain trials recently examining pretreatment QST responses as predictors of response to therapy. These predictive studies are founded on the concept that if sensory symptom profiles reflect pain mechanisms, then patients with different sensory response characteristics are likely to respond differentially to particular treatments, allowing (eventually) the tailoring of mechanism-targeted treatments to individual patient phenotypes.16,202

Quantitative sensory testing was used in a study of patients with traumatic nerve injury and PHN who were treated with botulinum toxin. A good outcome (ie, a significant reduction in spontaneous pain and dynamic mechanical allodynia) correlated with the preservation of cutaneous thermosensation, documented by low warm and heat pain thresholds at baseline.201 Similar predictive results were observed in a study of motor cortex stimulation among patients with chronic neuropathic pain (eg, trigeminal neuralgia, poststroke pain).71 Participants with preserved thermal thresholds reported the largest percentage pain relief from motor cortex stimulation. Suggestive evidence that certain treatments are most effective in the context of thermal hyperalgesia has also come from a recent case report in which QST was performed in a patient with bilateral at-level pain following a spinal cord injury.256 On the right side of the body, the patient exhibited preserved thermosensation, and some evidence of cold hyperalgesia, while on the left side, there was a prominent loss of thermal and mechanical sensation. Interestingly, pregabalin treatment was highly effective for at-level pain on the right side but not the left side, suggesting a selective effectiveness for pain mediated by hypersensitivity processes.

Other trials have reported parallel results when considering mechanical, rather than thermal, QST measures. Among patients with PHN, those with mechanical allodynia had a better outcome with intravenous lidocaine than with placebo,14 a finding (ie, better response to active treatment among those with mechanical allodynia or hyperalgesia) that has been reproduced among patients with spinal cord injury pain treated with lamotrigine,94 and patients with HIV neuropathy treated with pregabalin.215 A recent investigation in patients with chronic visceral pain confirms that pretreatment hyperalgesia (in this case, hyperalgesia to cutaneous electrical stimulation) in the painful area was associated with better analgesic responses to pregabalin.185 No associations were detected with the magnitude of placebo analgesia, although other reports have described a general predictive capacity for QST-derived pain responses. For example, a recent RCT revealed that cold hyperalgesia was among the most potent predictors of placebo responses among patients with unilateral lateral epicondylalgia.57

To date, the majority of the positive findings involving QST-assessed phenotypes have been identified in post hoc analyses. However, some recent trials have begun to incorporate prespecified phenotypic hypotheses into their study designs. For example, a 2014 RCT of oxcarbazepine showed effect modification using elements of the multimodal DFNS QST paradigm.67 At baseline, patients were phenotyped with the DFNS paradigm into “irritable nociceptor” (ie, those with sensory gain, relative to reference data, on mechanical and/or thermal testing) and “nonirritable nociceptor” groups. The irritable nociceptor group derived substantially greater benefit from oxcarbazepine than their counterparts in the nonirritable nociceptor group, with no differences in placebo effects, which were minimal in both groups. The number needed to treat for 50% pain relief was 3.9 in the irritable nociceptor group, compared with a number needed to treat of 13 in the remainder of the sample.67 Together, these studies highlight the potential for tailoring specific treatments to particular subgroups of patients with differing sensory profiles and suggest that agents affecting sodium and calcium channels may exert their largest analgesic effects among patients with neuropathic pain who exhibit the greatest degree of hyperalgesia and allodynia in the painful area. Such a conclusion may only apply to systemic administration of these medications, as studies of topical lidocaine have yielded inconsistent results.122,252 Similarly, recent trials of topical capsaicin have noted varying patters of response, with one study reporting that patients without allodynia and hyperalgesia responded best to high-concentration topical capsaicin treatment,141 while another found that the presence of cold and pinprick hyperalgesia at baseline was predictive of a better analgesic response to 8% capsaicin.162

Studies of other treatments, in contrast, have occasionally reported that the least pain-sensitive, and most pain-tolerant, patients are most likely to benefit from multidisciplinary pain treatments83,107 and to derive the largest analgesic effects from oral opioid medications84,88,127 and implantable devices.41 Among these predictive studies, the diversity in QST methods, patient samples, and applied treatments makes it difficult to draw conclusions at present regarding which patient subtypes/profiles are most likely to respond to a specific intervention. This has led to calls for the careful standardization and integration of QST methods into multicenter clinical trials, which would subsequently allow reliable post hoc analysis of QST-derived predictors of response.9,17

For phase 2 trials, the DFNS QST battery can be recommended, when circumstances permit (one limiting factor is time, with the full battery taking 1-3 hours to administer, depending on the number of body regions tested203), with the possibility to add supplemental QST measures (eg, suprathreshold measures of response, capsaicin challenge, conditioned pain modulation [CPM]–see section 4.5). For phase 3 trials, it is recommended that the DFNS battery be considered, taking into account that implementation will be challenging in large multicenter trials. A desirable alternative for phase 3 trials, or large multicenter phase 2 trials, would be a “bedside” QST assessment, such as that recently reported in 3 large RCTS by Freeman and colleagues.97 Pretreatment phenotyping with such methods has yielded evidence of effect modification in multiple RCTs.67,97,215 Recent reviews have called for increased application and study of such brief, bedside QST protocols, which do not require specialized equipment, and which may be feasible additions to large multicenter trials.15,27,62 In addition, trial-to-trial variability of many QST responses is greater among patients with chronic pain than pain-free controls.263 Such variability should be examined as a potentially influential phenotypic factor (in much the same way that day-to-day variation in clinical pain intensity may be an important predictive variable, see section 4.2).

4.5. Conditioned pain modulation and other indices of pain modulation

In addition to standard QST measures of pain and sensory thresholds, there has also been a good deal of interest in phenotyping individual variability in endogenous pain-modulatory processes.108,153 Pain facilitation is often assessed using temporal summation methods, and endogenous pain inhibition has been most commonly measured by applying Diffuse Noxious Inhibitory Control paradigms to humans. Diffuse Noxious Inhibitory Control is a physiological counterirritation phenomenon described over 30 years ago in animals.149–151 A noxious stimulus applied to one body region can reduce spinal neuronal responses to a heterotopically applied second noxious stimulus, often of a different modality. In humans, this “pain inhibits pain” phenomenon is now termed conditioned pain modulation and is measured psychophysically.264,265 Currently, the CPM concept is best viewed as the net effect of various facilitating and inhibiting systems exerting their activity at spinal or supraspinal levels. In most CPM paradigms, a phasic noxious stimulus is applied both alone and in conjunction with a tonic noxious conditioning stimulus applied to a distant body site, with the pain response to the phasic stimulus expected to be reduced when applied concurrently with the tonic noxious stimulus. Conditioned pain modulation seems to depend, at least in part, on opioid-mediated supraspinal mechanisms222 and may also involve serotonergic and noradrenergic pathways.268,269 It varies widely in magnitude across individuals and is a sensitive measure of deficits in pain modulation in FM and a variety of persistent pain disorders240 including long-term postsurgical pain.36,85,267

Because pain is modulated by monoaminergic descending pathways (some of which seem to be involved in CPM), it seems logical to assume that patients who differ in pretreatment CPM might respond differentially to medications acting on these targets. Yarnitsky and colleagues269 postulated that patients showing decrements in CPM should benefit more from serotonin–noradrenaline reuptake inhibitors (SNRIs), which augment descending inhibition by spinal monoamine reuptake inhibition, than patients whose CPM seems to be functioning effectively. They examined CPM in patients with DPN who were treated with duloxetine and found that CPM predicted the drug's efficacy; patients with less efficient pretreatment CPM derived substantial pain relief from duloxetine, while those with efficient baseline CPM did not benefit. Furthermore, for the low CPM group, duloxetine-related changes in pain intensity paralleled changes in CPM. The study did not include a placebo group and so it was not possible to examine whether CPM was a treatment effect modifier for duloxetine.

A more recent RCT, this one a placebo-controlled trial of tapentadol, focused on treatment-related changes in CPM.179 Twenty-four patients with DPN were randomized to receive either sustained-release tapentadol or placebo for 4 weeks. At baseline, these patients did not demonstrate a significant CPM response, but patients randomized to tapentadol subsequently developed significant CPM, the magnitude of which corresponded to the degree and temporal course of patients' reduction in their neuropathic pain. Other studies in NSAID-treated patients have similarly revealed predictive relationships between baseline CPM and analgesic outcomes, with a higher magnitude of pretreatment CPM predicting more pain relief in an open-label, general prediction study of a topical NSAID.58 Studies of nonpharmacologic analgesic interventions such as exercise also suggest significant associations between the magnitude of CPM and the magnitude of exercise-induced hypoalgesia.152,236

Interestingly, CPM may be somewhat specific in its treatment-predictive capacity; in contrast to the SNRI findings, a recent RCT in patients with chronic pancreatitis suggested that pretreatment CPM was not associated with the analgesic effectiveness of pregabalin185 and was in turn unaffected by subsequent pregabalin treatment.38 Such specificity is expected, given the overlap between CPM mechanisms and SNRI mechanisms.269 Accordingly, the committee recommends consideration of the inclusion of a measure of CPM in phase 2 and 3 analgesic trials, where pharmacologically appropriate. While there are dozens of published methods for assessing CPM,198,240 we recommend if possible implementing a version of the paradigm used by Yarnitsky and colleagues,269 in which a hot water bath was used as a conditioning stimulus and an individually tailored noxious contact thermal stimulus was used as the concurrent test stimulus. However, the availability of the required testing equipment may be limited, and use of alternative paradigms may be desirable or necessary, as noted in a recent review.266

Psychophysical assessment of pain facilitation is most often assessed using temporal summation paradigms, which involve applying a series of identical noxious stimuli and measuring the increase in the percept of pain intensity.4 Individuals differ broadly in their degree of temporal summation, and many groups of patients with persistent pain exhibit increased temporal summation relative to controls.268 Temporal summation of pain can be reduced by a variety of analgesic treatments, from ketamine3 to spinal cord stimulation87 to acupuncture271 to exercise.235 Recent studies of postoperative pain have highlighted the potential prognostic value of temporal summation for predicting the development of persistent postoperative pain189 and for profiling patients with various chronic pain syndromes including osteoarthritis and atypical odontalgia.86,197 However, no phase 2 or 3 studies to date have evaluated the prospective predictive effects of temporal summation phenotypes on treatment outcomes.

Finally, offset analgesia is a pain-modulatory process that has recently been used to profile patients with persistent pain.178 The phenomenon of offset analgesia is characterized by a disproportionately large decrease in perceived pain intensity following a relatively small decrease in noxious stimulus intensity. While offset analgesia is classified as an endogenous pain-inhibitory process, it is distinct from CPM,146 which suggests its potential utility as a unique pain-modulatory phenotyping measure. Offset analgesia is impaired (ie, the magnitude of the decrease in perceived pain intensity is lower than expected) in patients with chronic neuropathic pain177–179 and is unaffected by ketamine, tapentadol, or oral opioids.167,177–179 To date, as with temporal summation, offset analgesia has not been studied as a general predictor or effect modifier in any phase 2 or 3 trials.

4.6. Response to pharmacologic challenge

Although rarely studied in the context of Phase 2 and 3 clinical trials, valuable phenotypic information may be derived from careful assessment of a patient's response to a pharmacologic challenge. Here, we omit consideration of those studies in which early response to a medication (eg, at 2 weeks after initiating treatment) predicts long-term analgesic responses to that medication during a lengthy, sustained, treatment period. This phenomenon is well documented137,247 and is obviously clinically valuable, but it does not advance the goal of performing pretreatment phenotyping to select patients with good responses to a particular intervention.

A series of studies have examined the use of an intravenous infusion paradigm to predict the subsequent analgesic response to an oral analogue of the same drug class. As noted in a 2009 review of these studies53:

The rationale behind use of intravenous infusion tests is that they can quickly predict those patients who will respond to a subsequent course of oral medication, thereby eliminating the time and expense of a lengthy oral medication trial and reducing the risks of adverse effects associated with ineffective drug treatment. An infusion test can serve as a prognostic tool for a treatment associated with significant risk, such as implantable analgesic devices or oral opioid therapy. In these situations, a screening test with a high specificity and positive predictive value may prevent patients unlikely to respond to a high-risk therapy from receiving an unwarranted treatment. Intravenous infusion tests can also provide valuable information when the definitive treatment provides considerable relief to only a small subset of patients.

Overall, this review reported evidence for the potential predictive benefits of IV lidocaine and IV ketamine tests.

Several previous randomized trials in patients with neuropathic pain have reported that responses to acute IV lidocaine infusion are positively associated with the degree of analgesia obtained by mexiletine treatment.14,207 Similar findings were evident in open-label studies or retrospective chart reviews.46,227 Several other trials have used a low-dose IV ketamine probe to predict subsequent responses to dextromethorphan.54 A series of open-label, general prediction studies by Cohen and colleagues52 have suggested that response to an IV ketamine infusion is a significant predictor of intermediate-term relief with subsequent dextromethorphan treatment in patients with neuropathic pain, FM,55 and patients showing signs of opioid tolerance.56 For example, in Cohen et al.,56 0.1 mg/kg of ketamine was administered IV over 7 minutes, followed by a course of several months of oral dextromethorphan treatment. There was a strong association between short-term (measured over the course of minutes) pain relief with IV ketamine and subsequent pain relief with dextromethorphan over the course of several months' follow-up (r = 0.54, P < 0.001).

While the use of IV opioid infusions to predict long-term analgesic responses to oral opioid therapy is highly appealing,111 the limited extant data are mixed.53 Two open-label trials of oral morphine11 and transdermal fentanyl65,66 in a small number of patients with neuropathic pain have observed a moderate correlation between the acute analgesic effects of an IV opioid and the subsequent intermediate- or long-term analgesic effects of sustained treatment with that same opioid. However, a similarly designed small study in patients with phantom limb pain failed to detect a significant correlation between IV morphine's analgesic effects and patients' longer-term analgesic responses to a course of oral morphine treatment.129 Finally, an IV phentolamine test in neuropathic pain did not predict the analgesic response to transdermal clonidine.40,63 Taken together, it is difficult to provide definite estimates of the positive and negative predictive value of examined infusion paradigms as the number of studies and prospectively evaluated patients was small, heterogeneous pain conditions were explored, different study protocols were used, and variable criteria were applied to infer analgesic efficacy. The authors note here that the prognostic benefits of any acute, IV pharmacologic challenge are likely to be medication-specific, and in addition may be confounded by sensory cues or adverse effects (eg, nausea) associated with infusion of active medication but not placebo. In general, crossover RCTs involving multiple active treatments have tended to show no relationship between the degree of analgesia achieved by agents with different mechanisms of action (eg, no association between morphine and nortriptyline analgesia in,199 and no overlap in the variability in response to amitriptyline and maprotiline254 among patients with PHN).

More recent studies used pharmacological testing to predict subsequent responses to nonanalogue drug classes. Responses to topical lidocaine have been demonstrated to predict the subsequent response to high-concentration topical capsaicin.166 In a 12-week RCT of high-concentration topical capsaicin for PHN, before application of the capsaicin patch, patients received a brief administration of a local anesthetic cream (lidocaine 4%) on the affected area. The local anesthetic was used to mask the burning pain associated with the placement of the capsaicin patch, but when considered as a “challenge” it produced broad phenotypic variability in patient responses, which was prospectively associated with long-term capsaicin treatment response. Those whose PHN pain was alleviated with the topical anesthetic had a roughly 3-fold increase in the probability of being classified as a capsaicin responder over the course of the 12-week trial. Capsaicin has also been used as a means to identify the effective dose of specific analgesic agents,260 as well as a pharmacologic probe of local nociceptor function. For example, in a randomized, placebo-controlled trial of topical clonidine in patients with painful diabetic neuropathy, sensory profiles were assessed during screening with a topical capsaicin challenge.42 The increase in spontaneous pain after cutaneous capsaicin application was used as a phenotypic indicator of nociceptor function at baseline. While in the full sample, the primary end point (pain reduction) did not differ significantly between the clonidine and placebo groups, when patients were stratified post hoc according to their capsaicin response, clonidine significantly reduced pain in a subgroup of patients who rated the topical capsaicin challenge as painful. Moreover, the magnitude of separation between the clonidine- and placebo-treated patients became more pronounced with increasing capsaicin ratings, demonstrating evidence of effect modification. As the authors note, such findings “suggest that the analgesic effect of clonidine depends on the presence of functional capsaicin-responsive nociceptors in the skin, and raises the broader issue that neuropathic pain treatments may be guided by results of sensory testing.”42 In addition to assessing spontaneous pain following topical capsaicin, direct measurement of local neurovascular response to capsaicin is also possible using methods such as laser Doppler imaging, which may provide valuable phenotypic information distinct from self-reported pain.118 Overall, we recommend the consideration of specific pharmacologic challenge in applicable RCTs; for example, if mexiletine is an active agent being studied in patients with neuropathic pain, multiple RCTs have suggested that the results of an acute IV lidocaine challenge may be predictive in this context. However, given the relative scarcity of data, the small size of most published trials, and the potential risks associated with the infusion of some agents (eg, ketamine), it is not possible to propose firm recommendations at this time.

5. Conclusions

To date, phenotypic profiling in clinical trials has predominantly focused on characterizing the effects of treatments on an array of pain-related symptoms and signs. Recent years, though, have witnessed a growing interest in predictive phenotyping9,17,41,69; there seems to be great potential to advance the goal of tailored, or personalized, pain treatment. The tremendous heterogeneity among patients with persistent pain and the disappointing negative results of many analgesic trials may be harbingers of a future in which patients are comprehensively phenotyped (in addition to being diagnosed), and then they are managed according to an empirically supported algorithm that matches those patient profiles to the optimal combination of treatments. As an intermediate step to such “deep” phenotyping, we hope that our present recommendations may help investigators to select the most promising phenotyping measures for use in phase 2 and 3 analgesic trials (Tables 1 and 2). An additional potential benefit to human phenotyping studies has been highlighted by recent commentaries in this area that have called for back-translation of specific phenotypes (eg, QST-based sensory profiling) into animal research, which would allow more precise characterization of the pathophysiologic mechanisms that characterize specific subgroups of patients.16 Such work would have the potential to facilitate the identification of new drug targets, which could then be investigated using phenotype-tailored investigation of treatment outcomes.

Balanced against the benefits of phenotyping are the associated costs of additional assessment, as well as obstacles to the implementation of phenotyping protocols. These are rarely discussed in the scientific literature, but important barriers may include considerable costs of implementing phenotyping methods, training investigators, and maintaining phenotyping data in a multicenter trial, concerns that identification of treatment-responsive subgroups may lead to narrow regulatory approval (eg, the case of BiDil226), pragmatic considerations regarding the difficulties of administering, scoring, and interpreting phenotyping measures in clinical practice settings, and inadequate power to detect subgroup effects. In addition, limited research evaluating the temporal stability of some of the recommended phenotypes is available, and we know relatively little about the natural history of these phenotypic characteristics. We hope that ongoing open discussion of these issues may facilitate the design of future analgesic trials.

We appreciate that a substantial proportion of the studies cited in this article were performed in samples of patients with neuropathic pain, which, despite a substantial prevalence,239 is not the most commonly experienced type of pain in the general population. Neuropathic pain is frequently studied in phase 2 and 3 trials of analgesics, probably at least in part because it is presumed to be easier to identify a “pain mechanism” to target for a condition like PHN than for a condition such as nonspecific, axial, LBP.100,255 In addition, some have reported that placebo effects may be lower in magnitude in RCTs for some neuropathic pain than for musculoskeletal pain conditions,2,50 and this may have enhanced the appeal of testing putative analgesic compounds in phase 2 and 3 trials in patients with neuropathic pain. However, the phenotyping approach described here is presumed to be relatively general and applicable to numerous types of persistent pain conditions including those traditionally classified as neuropathic, musculoskeletal, or inflammatory. For example, QST phenotyping is increasingly being applied in osteoarthritis (OA), and multiple recent studies have suggested that indices of central pain modulation such as temporal summation are important predictors of OA treatment outcomes, especially joint replacement outcomes.189,216,217 Similar findings are evident in studies of chronic LBP, as QST-assessed indices of pain sensitivity and pain modulation show significant prospective associations with pain intensity and disability following treatment.182 Moreover, psychosocial factors such as depression, anxiety, distress, and catastrophizing seem to have fairly general effects, as these variables have been prospectively associated in recent studies with: greater physical disability and reduced treatment response among patients with RA treated with steroids,168 patients with chronic back pain undergoing acupuncture,28 patients with chronic neck pain treated with radiofrequency lesioning or facet blocks,219,220 patients with chronic pelvic pain undergoing surgery,131 patients with whiplash managed with multimodal rehabilitation,51 primary care patients experiencing back pain,173 patients with orofacial pain receiving injection therapies,164 patients with FM enrolled in an exercise program,43 patients with irritable bowel syndrome undergoing CBT,30 patients with neck pain treated with manual therapy,64 and many other combinations of nonneuropathic chronic pain with a variety of treatment approaches.

Overall, it is clear that many factors, not all of them captured by the sort of phenotyping recommended here (eg, genetic variation), may contribute to interindividual variability in analgesic outcomes. Perfect, or near-perfect, prediction of an individual patient's response to a given treatment seems to be an unattainable goal at present. However, the findings outlined in the present review, some of which have derived support from multiple studies (eg, patients with relatively higher baseline levels of neuropathic symptoms on self-report measures, compared with those with lower levels, seem to benefit most from pregabalin), indicate that there are reasonable grounds for proceeding with additional phenotyping work in phase 2 and 3 trials. A healthy degree of skepticism is warranted, of course, given the absence of replication of most findings as well as the retrospective nature of most results to date, but we believe that this area of work shows substantial promise. Large trials, meta-analyses, or pooled data sets that include multimodal phenotypic assessments (eg, Refs. 67,97, which include both QST and self-report measures of neuropathic pain) are likely to provide the most informative and actionable results, and we encourage investigators to publish comprehensive patient-level phenotyping data. In addition, while the vast majority of the studies cited here have evaluated pain intensity as the primary outcome, numerous surveys have noted that treatment-related improvements in a variety of domains (eg, sleep, mood, activity level) are important to patients with chronic pain.230 It may be that differing phenotypic factors are relatively more or less important in shaping differing domains of outcomes, suggesting that outcome-specific phenotyping may be necessary. While the present report focuses on “subjectively measured” phenotypes, more objectively measured patient characteristics (eg, MRI or other imaging findings, neurophysiological studies) are also likely to play an important predictive role. Moreover, crossover designs as well as trials that include head-to-head comparisons of active agents (eg, Ref. 35) may provide the most rapid advances in the development of tailored mechanism-based treatment algorithms. Other recent reviews, while noting that multiperiod crossover trials have rarely been conducted in the pain literature, have called for such studies to examine treatment-by-patient interactions.74 It is our hope that combining such designs with comprehensive, multimodal, pretreatment phenotyping may move the field further toward the eventual goal of providing empirically based, personalized pain medicine.

Conflict of interest statement

The views expressed in this article are those of the authors, none of whom have financial conflicts of interest specifically related to the issues discussed in this article. At the time of the meeting on which this article is based, several authors were employed by pharmaceutical companies and others had received consulting fees or honoraria from one or more pharmaceutical or device companies. Authors of this article who were not employed by industry or government at the time of the meeting received travel stipends, hotel accommodations, and meals during the meeting provided by the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership with the US Food and Drug Administration (FDA), which has received research contracts, grants, or other revenue from the FDA, multiple pharmaceutical and device companies, and other sources. Preparation of background literature reviews and draft manuscripts was supported by ACTTION. No official endorsement by the FDA, US National Institutes of Health, or the pharmaceutical and device companies that have provided unrestricted grants to support the activities of ACTTION should be inferred.


The authors thank Andrea Speckin and Valorie Thompson for their assistance in organizing the meeting, and Allison H. Lin, PharmD, PhD, for her valuable participation.


[1]. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015:60692–4.
[2]. Arakawa A, Kaneko M, Narukawa M. An investigation of factors contributing to higher levels of placebo response in clinical trials in neuropathic pain: a systematic review and meta-analysis. Clin Drug Investig 2015;35:67–81.
[3]. Arendt-Nielsen L, Mansikka H, Staahl C, Rees H, Tan K, Smart TS, Monhemius R, Suzuki R, Drewes AM. A translational study of the effects of ketamine and pregabalin on temporal summation of experimental pain. Reg Anesth Pain Med 2011;36:585–91.
[4]. Arendt-Nielsen L, Yarnitsky D. Experimental and clinical applications of quantitative sensory testing applied to skin, muscles and viscera. J Pain 2009;10:556–72.
[5]. Arnold LM, Crofford LJ, Martin SA, Young JP, Sharma U. The effect of anxiety and depression on improvements in pain in a randomized, controlled trial of pregabalin for treatment of fibromyalgia. Pain Med 2007;8:633–8.
[6]. Arnold LM, Wang F, Ahl J, Gaynor PJ, Wohlreich MM. Improvement in multiple dimensions of fatigue in patients with fibromyalgia treated with duloxetine: secondary analysis of a randomized, placebo-controlled trial. Arthritis Res Ther 2011;13:R86.
[7]. Asih S, Mayer TG, Bradford EM, Neblett R, Williams MJ, Hartzell MM, Gatchel R. The potential utility of the patient health questionnaire as a screener for psychiatric comorbidity in a chronic disabling occupational musculoskeletal disorder population. Pain Pract 2016;16:168–74.
[8]. Atlas LY, Whittington RA, Lindquist MA, Wielgosz J, Sonty N, Wager TD. Dissociable influences of opiates and expectations on pain. J Neurosci 2012;32:8053–64.
[9]. Attal N, Bouhassira D, Baron R, Dostrovsky J, Dworkin RH, Finnerup N, Gourlay G, Haanpaa M, Raja S, Rice AS, Simpson D, Treede RD. Assessing symptom profiles in neuropathic pain clinical trials: can it improve outcome? Eur J Pain 2011;15:441–3.
[10]. Attal N, Fermanian C, Fermanian J, Lanteri-Minet M, Alchaar H, Bouhassira D. Neuropathic pain: are there distinct subtypes depending on the aetiology or anatomical lesion? PAIN 2008;138:343–53.
[11]. Attal N, Guirimand F, Brasseur L, Gaude V, Chauvin M, Bouhassira D. Effects of IV morphine in central pain: a randomized placebo-controlled study. Neurology 2002;58:554–63.
[12]. Attal N, Lanteri-Minet M, Laurent B, Fermanian J, Bouhassira D. The specific disease burden of neuropathic pain: results of a French nationwide survey. PAIN 2011;152:2836–43.
[13]. Attal N, Masselin-Dubois A, Martinez V, Jayr C, Albi A, Fermanian J, Bouhassira D, Baudic S. Does cognitive functioning predict chronic pain? Results from a prospective surgical cohort. Brain 2014;137(pt 3):904–17.
[14]. Attal N, Rouaud J, Brasseur L, Chauvin M, Bouhassira D. Systemic lidocaine in pain due to peripheral nerve injury and predictors of response. Neurology 2004;62:218–25.
[15]. Backonja MM, Attal N, Baron R, Bouhassira D, Drangholt M, Dyck P, Edwards RR, Freeman R, Gracely R, Haanpaa MH, Hansson P, Hatem SM, Krumova EK, Jensen TS, Maier C, Mick G, Rice AS, Rolke R, Treede RD, Serra J, Toelle T, Tugnoli V, Walk D, Walalce MS, Ware M, Yarnitsky D, Ziegler D. Value of quantitative sensory testing in neurological and pain disorders: NeuPSIG consensus. PAIN 2013;154:1807–19.
[16]. Baron R, Dickenson AH. Neuropathic pain: precise sensory profiling improves treatment and calls for back-translation. PAIN 2014;155:2215–17.
[17]. Baron R, Forster M, Binder A. Subgrouping of patients with neuropathic pain according to pain-related sensory abnormalities: a first step to a stratified treatment approach. Lancet Neurol 2012;11:999–1005.
[18]. Baron R, Kenny D. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Personal Soc Psychol 1986;51:1173–82.
[19]. Baron R, Kern U, Muller M, Dubois C, Falke D, Steigerwald I. Effectiveness and tolerability of a moderate dose of tapentadol prolonged release for managing severe, chronic low back pain with a neuropathic component: an open-label continuation arm of a randomized phase 3b study. Pain Pract 2015;15:471–86.
[20]. Baron R, Martin-Mola E, Muller M, Dubois C, Falke D, Steigerwald I. Effectiveness and safety of tapentadol prolonged release (PR) versus a combination of tapentadol PR and pregabalin for the management of severe, chronic low back pain with a neuropathic component: a randomized, double-blind, phase 3b study. Pain Pract 2015;15:455–70.
[21]. Baron R, Tolle TR, Gockel U, Brosz M, Freynhagen R. A cross-sectional cohort survey in 2100 patients with painful diabetic neuropathy and postherpetic neuralgia: differences in demographic data and sensory symptoms. PAIN 2009;146:34–40.
[22]. Bastien CH, Vallieres A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2001;2:297–307.
[23]. Benedetti F, Amanzio M. Mechanisms of the placebo response. Pulm Pharmacol Ther 2013;26:520–3.
[24]. Bergstrom C, Hagberg J, Bodin L, Jensen I, Bergstrom G. Using a psychosocial subgroup assignment to predict sickness absence in a working population with neck and back pain. BMC Musculoskelet Disord 2011;12:81.
[25]. Bergstrom G, Bergstrom C, Hagberg J, Bodin L, Jensen I. A 7-year follow-up of multidisciplinary rehabilitation among chronic neck and back pain patients. Is sick leave outcome dependent on psychologically derived patient groups? Eur J Pain 2010;14:426–33.
[26]. Bingel U, Wanigasekera V, Wiech K, Ni MR, Lee MC, Ploner M, Tracey I. The effect of treatment expectation on drug efficacy: imaging the analgesic benefit of the opioid remifentanil. Sci Transl Med 2011;3:70ra14.
[27]. Birklein F, Sommer C. Pain: quantitative sensory testing—a tool for daily practice? Nat Rev Neurol 2013;9:490–2.
[28]. Bishop FL, Yardley L, Prescott P, Cooper C, Little P, Lewith GT. Psychological covariates of longitudinal changes in back-related disability in patients undergoing acupuncture. Clin J Pain 2015;31:254–64.
[29]. Blackburn J, Qureshi A, Amirfeyz R, Bannister G. Does preoperative anxiety and depression predict satisfaction after total knee replacement? Knee 2012;19:522–4.
[30]. Blanchard EB, Lackner JM, Gusmano R, Gudleski GD, Sanders K, Keefer L, Krasner S. Prediction of treatment outcome among patients with irritable bowel syndrome treated with group cognitive therapy. Behav Res Ther 2006;44:317–37.
[31]. Boersma K, Linton SJ. Screening to identify patients at risk: profiles of psychological risk factors for early intervention. Clin J Pain 2005;21:38–43.
[32]. Bohnert AS, Ilgen MA, Trafton JA, Kerns RD, Eisenberg A, Ganoczy D, Blow FC. Trends and regional variation in opioid overdose mortality among Veterans Health Administration patients, fiscal year 2001 to 2009. Clin J Pain 2014;7:605–12.
[33]. Bohnert AS, Valenstein M, Bair MJ, Ganoczy D, McCarthy JF, Ilgen MA, Blow FC. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA 2011;305:1315–21.
[34]. Bouhassira D, Letanoux M, Hartemann A. Chronic pain with neuropathic characteristics in diabetic patients: a French cross-sectional study. PLoS One 2013;8:e74195.
[35]. Bouhassira D, Wilhelm S, Schacht A, Perrot S, Kosek E, Cruccu G, Freynhagen R, Tesfaye S, Lledó A, Choy E, Marchettini P, Micó JA, Spaeth M, Skljarevski V, Tölle T. Neuropathic pain phenotyping as a predictor of treatment response in painful diabetic neuropathy: data from the randomized, double-blind, COMBO-DN study. PAIN 2014;155:2171–9.
[36]. Bouwense SA, Ahmed AU, ten Broek RP, Issa Y, van Eijck CH, Wilder-Smith OH, van Goor H. Altered central pain processing after pancreatic surgery for chronic pancreatitis. Br J Surg 2013;100:1797–804.
[37]. Bouwense SA, de Vries M, Schreuder LT, Olesen SS, Frokjaer JB, Drewes AM, van Goor H, Wilder-Smith OH. Systematic mechanism-orientated approach to chronic pancreatitis pain. World J Gastroenterol 2015;21:47–59.
[38]. Bouwense SA, Olesen SS, Drewes AM, Poley JW, van Goor H, Wilder-Smith OH. Effects of pregabalin on central sensitization in patients with chronic pancreatitis in a randomized, controlled trial. PLoS One 2012;7:e42096.
[39]. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28:193–213.
[40]. Byas-Smith MG, Max MB, Muir J, Kingman A. Transdermal clonidine compared to placebo in painful diabetic neuropathy using a two-stage “enriched enrollment” design. PAIN 1995;60:267–74.
[41]. Campbell CM, Jamison RN, Edwards RR. Psychological screening/phenotyping as predictors for spinal cord stimulation. Curr Pain Headache Rep 2013;17:307.
[42]. Campbell CM, Kipnes MS, Stouch BC, Brady KL, Kelly M, Schmidt WK, Petersen KL, Rowbotham MC, Campbell JN. Randomized control trial of topical clonidine for treatment of painful diabetic neuropathy. PAIN 2012;153:1815–23.
[43]. Campbell CM, McCauley L, Bounds SC, Mathur VA, Conn L, Simango M, Edwards RR, Fontaine KR. Changes in pain catastrophizing predict later changes in fibromyalgia clinical and experimental pain report: cross-lagged panel analyses of dispositional and situational catastrophizing. Arthritis Res Ther 2012;14:R231.
[44]. Carr DB. When bad evidence happens to good treatments. Reg Anesth Pain Med 2008;33:229–40.
[45]. Carragee EJ, Alamin TF, Miller JL, Carragee JM. Discographic, MRI and psychosocial determinants of low back pain disability and remission: a prospective study in subjects with benign persistent back pain. Spine J 2005;5:24–35.
[46]. Carroll IR, Kaplan KM, Mackey SC. Mexiletine therapy for chronic pain: survival analysis identifies factors predicting clinical success. J Pain Symptom Manage 2008;35:321–6.
[47]. Carroll IR, Younger JW, Mackey SC. Pain quality predicts lidocaine analgesia among patients with suspected neuropathic pain. Pain Med 2010;11:617–21.
[48]. Celestin J, Edwards RR, Jamison RN. Pretreatment psychosocial variables as predictors of outcomes following lumbar surgery and spinal cord stimulation: a systematic review and literature synthesis. Pain Med 2009;10:639–53.
[49]. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, Amtmann D, Bode R, Buysse D, Choi S, Cook K, Devellis R, DeWalt D, Fries JF, Gershon R, Hahn EA, Lai JS, Pilkonis P, Revicki D, Rose M, Weinfurt K, Hays R; PROMIS Cooperative Group. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol 2010;63:1179–94.
[50]. Cepeda MS, Berlin JA, Gao CY, Wiegand F, Wada DR. Placebo response changes depending on the neuropathic pain syndrome: results of a systematic review and meta-analysis. Pain Med 2012;13:575–95.
[51]. Chiarotto A, Fortunato S, Falla D. Predictors of outcome following a short multimodal rehabilitation program for patients with whiplash associated disorders. Eur J Phys Rehabil Med 2015;51:133–41.
[52]. Cohen SP, Chang AS, Larkin T, Mao J. The intravenous ketamine test: a predictive response tool for oral dextromethorphan treatment in neuropathic pain. Anesth Analg 2004;99:1753–9.
[53]. Cohen SP, Kapoor SG, Rathmell JP. Intravenous infusion tests have limited utility for selecting long-term drug therapy in patients with chronic pain: a systematic review. Anesthesiology 2009;111:416–31.
[54]. Cohen SP, Liao W, Gupta A, Plunkett A. Ketamine in pain management. Adv Psychosom Med 2011;30:139–61.
[55]. Cohen SP, Verdolin MH, Chang AS, Kurihara C, Morlando BJ, Mao J. The intravenous ketamine test predicts subsequent response to an oral dextromethorphan treatment regimen in fibromyalgia patients. J Pain 2006;7:391–8.
[56]. Cohen SP, Wang S, Chen L, Kurihara C, McKnight G, Marcuson M, Mao J. An intravenous ketamine test as a predictive response tool in opioid-exposed patients with persistent pain. J Pain Symptom Manage 2009;37:698–708.
[57]. Coombes BK, Bisset L, Vicenzino B. Cold hyperalgesia associated with poorer prognosis in lateral epicondylalgia: a 1-year prognostic study of physical and psychological factors. Clin J Pain 2015;31:30–5.
[58]. Cornelius M, Edwards RR. CPM and treatment response in patients with knee osteoarthrits. J Pain 2014;14:S96.
[59]. Crombez G, Eccleston C, Van Damme S, Vlaeyen JW, Karoly P. Fear-avoidance model of chronic pain: the next generation. Clin J Pain 2012;28:475–83.
[60]. Cruccu G, Sommer C, Anand P, Attal N, Baron R, Garcia-Larrea L, Haanpaa M, Jensen TS, Serra J, Treede RD. EFNS guidelines on neuropathic pain assessment: revised 2009. Eur J Neurol 2010;17:1010–18.
[61]. Cruccu G, Truini A. Neuropathic pain and its assessment. Surg Oncol 2010;19:149–54.
[62]. Cruz-Almeida Y, Fillingim RB. Can quantitative sensory testing move us closer to mechanism-based pain management? Pain Med 2014;15:61–72.
[63]. Davis KD, Treede RD, Raja SN, Meyer RA, Campbell JN. Topical application of clonidine relieves hyperalgesia in patients with sympathetically maintained pain. PAIN 1991;47:309–17.
[64]. De Pauw R, Kregel J, De Blaiser C, Van Akeleyen J, Logghe T, Danneels L, Cagnie B. Identifying prognostic factors predicting outcome in patients with chronic neck pain after multimodal treatment: a retrospective study. Man Ther 2015;20:592–7.
[65]. Dellemijn PL, van Duijn H, Vanneste JA. Prolonged treatment with transdermal fentanyl in neuropathic pain. J Pain Symptom Manage 1998;16:220–9.
[66]. Dellemijn PL, Vanneste JA. Randomised double-blind active-placebo-controlled crossover trial of intravenous fentanyl in neuropathic pain. Lancet 1997;349:753–8.
[67]. Demant DT, Lund K, Vollert J, Maier C, Segerdahl M, Finnerup NB, Jensen TS, Sindrup SH. The effect of oxcarbazepine in peripheral neuropathic pain depends on pain phenotype: a randomised, double-blind, placebo-controlled phenotype-stratified study. PAIN 2014;155:2263–73.
[68]. Desrochers G, Bergeron S, Khalife S, Dupuis MJ, Jodoin M. Provoked vestibulodynia: psychological predictors of topical and cognitive-behavioral treatment outcome. Behav Res Ther 2010;48:106–15.
[69]. Diatchenko L, Fillingim RB, Smith SB, Maixner W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat Rev Rheumatol 2013;9:340–50.
[70]. Doufas AG, Panagiotou OA, Ioannidis JP. Concordance of sleep and pain outcomes of diverse interventions: an umbrella review. PLoS One 2012;7:e40891.
[71]. Drouot X, Nguyen JP, Peschanski M, Lefaucheur JP. The antalgic efficacy of chronic motor cortex stimulation is related to sensory changes in the painful zone. Brain 2002;125(pt 7):1660–4.
[72]. Duivenvoorden T, Vissers MM, Verhaar JA, Busschbach JJ, Gosens T, Bloem RM, Bierma-Zeinstra SM, Reijman M. Anxiety and depressive symptoms before and after total hip and knee arthroplasty: a prospective multicentre study. Osteoarthritis Cartilage 2013;21:1834–40.
[73]. Dworkin RH, Hartstein G, Rosner HL, Walther RR, Sweeney EW, Brand L. A high-risk method for studying psychosocial antecedents of chronic pain: the prospective investigation of herpes zoster. J Abnorm Psychol 1992;101:200–5.
[74]. Dworkin RH, McDermott MP, Farrar JT, O'Connor AB, Senn S. Interpreting patient treatment response in analgesic clinical trials: implications for genotyping, phenotyping, and personalized pain treatment. PAIN 2014;155:457–60.
[75]. Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, Kerns RD, Stucki G, Allen RR, Bellamy N, Carr DB, Chandler J, Cowan P, Dionne R, Galer BS, Hertz S, Jadad AR, Kramer LD, Manning DC, Martin S, McCormick CG, McDermott MP, McGrath P, Quessy S, Rappaport BA, Robbins W, Robinson JP, Rothman M, Royal MA, Simon L, Stauffer JW, Stein W, Tollett J, Wernicke J, Witter J. Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. PAIN 2005;113:9–19.
[76]. Dworkin RH, Turk DC, Katz NP, Rowbotham MC, Peirce-Sandner S, Cerny I, Clingman CS, Eloff BC, Farrar JT, Kamp C, McDermott MP, Rappaport BA, Sanhai WR. Evidence-based clinical trial design for chronic pain pharmacotherapy: a blueprint for ACTION. PAIN 2011;152(3 suppl):S107–15.
[77]. Dworkin RH, Turk DC, Peirce-Sandner S, Burke LB, Farrar JT, Gilron I, Jensen MP, Katz NP, Raja SN, Rappaport BA, Rowbotham MC, Backonja MM, Baron R, Bellamy N, Bhagwagar Z, Costello A, Cowan P, Fang WC, Hertz S, Jay GW, Junor R, Kerns RD, Kerwin R, Kopecky EA, Lissin D, Malamut R, Markman JD, McDermott MP, Munera C, Porter L, Rauschkolb C, Rice AS, Sampaio C, Skljarevski V, Sommerville K, Stacey BR, Steigerwald I, Tobias J, Trentacosti AM, Wasan AD, Wells GA, Williams J, Witter J, Ziegler D. Considerations for improving assay sensitivity in chronic pain clinical trials: IMMPACT recommendations. PAIN 2012;153:1148–58.
[78]. Dworkin RH, Turk DC, Peirce-Sandner S, He H, McDermott MP, Farrar JT, Katz NP, Lin AH, Rappaport BA, Rowbotham MC. Assay sensitivity and study features in neuropathic pain trials: an ACTTION meta-analysis. Neurology 2013;81:67–75.
[79]. Dworkin RH, Turk DC, Revicki DA, Harding G, Coyne KS, Peirce-Sandner S, Bhagwat D, Everton D, Burke LB, Cowan P, Farrar JT, Hertz S, Max MB, Rappaport BA, Melzack R. Development and initial validation of an expanded and revised version of the Short-form McGill Pain Questionnaire (SF-MPQ-2). PAIN 2009;144:35–42.
[80]. Dworkin RH, Turk DC, Wyrwich KW, Beaton D, Cleeland CS, Farrar JT, Haythornthwaite JA, Jensen MP, Kerns RD, Ader DN, Brandenburg N, Burke LB, Cella D, Chandler J, Cowan P, Dimitrova R, Dionne R, Hertz S, Jadad AR, Katz NP, Kehlet H, Kramer LD, Manning DC, McCormick C, McDermott MP, McQuay HJ, Patel S, Porter L, Quessy S, Rappaport BA, Rauschkolb C, Revicki DA, Rothman M, Schmader KE, Stacey BR, Stauffer JW, von Stein T, White RE, Witter J, Zavisic S. Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. J Pain 2008;9:105–21.
[81]. Edwards RR, Almeida DM, Klick B, Haythornthwaite JA, Smith MT. Duration of sleep contributes to next-day pain report in the general population. PAIN 2008;137:202–7.
[82]. Edwards RR, Cahalan C, Mensing G, Smith M, Haythornthwaite JA. Pain, catastrophizing, and depression in the rheumatic diseases. Nat Rev Rheumatol 2011;7:216–24.
[83]. Edwards RR, Doleys DM, Lowery D, Fillingim RB. Pain tolerance as a predictor of outcome following multidisciplinary treatment for chronic pain: differential effects as a function of sex. PAIN 2003;106:419–26.
[84]. Edwards RR, Haythornthwaite JA, Tella P, Max MB, Raja S. Basal heat pain thresholds predict opioid analgesia in patients with postherpetic neuralgia. Anesthesiology 2006;104:1243–8.
[85]. Edwards RR, Mensing G, Cahalan C, Greenbaum S, Narang S, Belfer I, Schreiber KL, Campbell C, Wasan AD, Jamison RN. Alteration in pain modulation in women with persistent pain after lumpectomy: influence of catastrophizing. J Pain Symptom Manage 2013;46:30–42.
[86]. Egsgaard LL, Eskehave TN, Bay-Jensen AC, Hoeck HC, Arendt-Nielsen L. Identifying specific profiles in patients with different degrees of painful knee osteoarthritis based on serological biochemical and mechanistic pain biomarkers: a diagnostic approach based on cluster analysis. PAIN 2015;156:96–107.
[87]. Eisenberg E, Burstein Y, Suzan E, Treister R, Aviram J. Spinal cord stimulation attenuates temporal summation in patients with neuropathic pain. PAIN 2015;156:381–5.
[88]. Eisenberg E, Midbari A, Haddad M, Pud D. Predicting the analgesic effect to oxycodone by “static” and “dynamic” quantitative sensory testing in healthy subjects. PAIN 2010;151:104–9.
[89]. Farrar JT, Troxel AB, Haynes K, Gilron I, Kerns RD, Katz NP, Rappaport BA, Rowbotham MC, Tierney AM, Turk DC, Dworkin RH. Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: an ACTTION study. PAIN 2014;155:1622–31.
[90]. Fillingim RB, Bruehl S, Dworkin RH, Dworkin SF, Loeser JD, Turk DC, Widerstrom-Noga E, Arnold L, Bennett R, Edwards RR, Freeman R, Gewandter J, Hertz S, Hochberg M, Krane E, Mantyh PW, Markman J, Neogi T, Ohrbach R, Paice JA, Porreca F, Rappaport BA, Smith SM, Smith TJ, Sullivan MD, Verne GN, Wasan AD, Wesselmann U. The ACTTION-American Pain Society Pain Taxonomy (AAPT): an evidence-based and multidimensional approach to classifying chronic pain conditions. J Pain 2014;15:241–9.
[91]. Fillingim RB, Ohrbach R, Greenspan JD, Knott C, Diatchenko L, Dubner R, Bair E, Baraian C, Mack N, Slade GD, Maixner W. Psychological factors associated with development of TMD: the OPPERA prospective cohort study. J Pain 2013;14(12 suppl):T75–90.
[92]. Fillingim RB, Slade GD, Diatchenko L, Dubner R, Greenspan JD, Knott C, Ohrbach R, Maixner W. Summary of findings from the OPPERA baseline case-control study: implications and future directions. J Pain 2011;12(11 suppl):T102–7.
[93]. Finan PH, Goodin BR, Smith MT. The association of sleep and pain: an update and a path forward. J Pain 2013;14:1539–52.
[94]. Finnerup NB, Sindrup SH, Bach FW, Johannesen IL, Jensen TS. Lamotrigine in spinal cord injury pain: a randomized controlled trial. PAIN 2002;96:375–83.
[95]. Forster M, Mahn F, Gockel U, Brosz M, Freynhagen R, Tolle TR, Baron R. Axial low back pain: one painful area–many perceptions and mechanisms. PLoS One 2013;8:e68273.
[96]. Frange C, Hachul H, Tufik S, Andersen ML. Sleep deprivation and states of pain in human trials. PAIN 2014;155:1043–4.
[97]. Freeman R, Baron R, Bouhassira D, Cabrera J, Emir B. Sensory profiles of patients with neuropathic pain based on the neuropathic pain symptoms and signs. PAIN 2014;155:367–76.
[98]. Freynhagen R, Baron R, Gockel U, Tolle TR. painDETECT: a new screening questionnaire to identify neuropathic components in patients with back pain. Curr Med Res Opin 2006;22:1911–20.
[99]. Gammaitoni AR, Smugar SS, Jensen MP, Galer BS, Bolognese JA, Alon A, Hewitt DJ. Predicting response to pregabalin from pretreatment pain quality: clinical applications of the pain quality assessment scale. Pain Med 2013;14:526–32.
[100]. Gan EY, Tian EA, Tey HL. Management of herpes zoster and post-herpetic neuralgia. Am J Clin Dermatol 2013;14:77–85.
[101]. Gandhi R, Davey JR, Mahomed N. Patient expectations predict greater pain relief with joint arthroplasty. J Arthroplasty 2009;24:716–21.
[102]. Gaskin DJ, Richard P. The economic costs of pain in the United States. J Pain 2012;13:715–24.
[103]. Gatchel RJ, Peng YB, Peters ML, Fuchs PN, Turk DC. The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychol Bull 2007;133:581–624.
[104]. Geber C, Klein T, Azad S, Birklein F, Gierthmuhlen J, Huge V, Lauchart M, Nitzsche D, Stengel M, Valet M, Baron R, Maier C, Tölle T, Treede RD. Test-retest and interobserver reliability of quantitative sensory testing according to the protocol of the German Research Network on Neuropathic Pain (DFNS): a multi-centre study. PAIN 2011;152:548–56.
[105]. Gerbershagen HJ, Dagtekin O, Isenberg J, Martens N, Ozgur E, Krep H. Chronic pain and disability after pelvic and acetabular fractures—assessment with the Mainz Pain Staging System. J Trauma 2010;69:128–36.
[106]. Gierthmuhlen J, Maier C, Baron R, Tolle T, Treede RD, Birbaumer N, Huge V, Koroschetz J, Krumova EK, Lauchart M, Maihöfner C, Richter H, Westermann A; German Research Network on Neuropathic Pain (DFNS) study group. Sensory signs in complex regional pain syndrome and peripheral nerve injury. PAIN 2012;153:765–74.
[107]. Granot M, Zimmer EZ, Friedman M, Lowenstein L, Yarnitsky D. Association between quantitative sensory testing, treatment choice, and subsequent pain reduction in vulvar vestibulitis syndrome. J Pain 2004;5:226–32.
[108]. Granovsky Y, Yarnitsky D. Personalized pain medicine: the clinical value of psychophysical assessment of pain modulation profile. Rambam Maimonides Med J 2013;4:e0024.
[109]. Grosen K, Fischer IW, Olesen AE, Drewes AM. Can quantitative sensory testing predict responses to analgesic treatment? Eur J Pain 2013;17:1267–80.
[110]. Grossi ML, Goldberg MB, Locker D, Tenenbaum HC. Reduced neuropsychologic measures as predictors of treatment outcome in patients with temporomandibular disorders. J Orofac Pain 2001;15:329–39.
[111]. Gustorff B. Intravenous opioid testing in patients with chronic non-cancer pain. Eur J Pain 2005;9:123–5.
[112]. Haanpaa M, Attal N, Backonja M, Baron R, Bennett M, Bouhassira D, Cruccu G, Hansson P, Haythornthwaite JA, Iannetti GD, Jensen TS, Kauppila T, Nurmikko TJ, Rice AS, Rowbotham M, Serra J, Sommer C, Smith BH, Treede RD. NeuPSIG guidelines on neuropathic pain assessment. PAIN 2011;152:14–27.
[113]. Hallstrom H, Norrbrink C. Screening tools for neuropathic pain: can they be of use in individuals with spinal cord injury? PAIN 2011;152:772–9.
[114]. Haninec P, Kaiser R, Mencl L, Waldauf P. Usefulness of screening tools in the evaluation of long-term effectiveness of DREZ lesioning in the treatment of neuropathic pain after brachial plexus injury. BMC Neurol 2014;14:225.
[115]. Hanusch BC, O'Connor DB, Ions P, Scott A, Gregg PJ. Effects of psychological distress and perceptions of illness on recovery from total knee replacement. Bone Joint J 2014;96-B:210–16.
[116]. Haroun OM, Hietaharju A, Bizuneh E, Tesfaye F, Brandsma JW, Haanpaa M, Rice AS, Lockwood DW. Investigation of neuropathic pain in treated leprosy patients in Ethiopia: a cross-sectional study. PAIN 2012;153:1620–4.
[117]. Haroutiunian S, Donaldson G, Yu J, Lipman AG. Development and validation of shortened, restructured Treatment Outcomes in Pain Survey instrument (the S-TOPS) for assessment of individual pain patients' health-related quality of life. PAIN 2012;153:1593–601.
[118]. Haroutounian S, Nikolajsen L, Finnerup NB, Jensen TS. Topical capsaicin response as a phenotypic measure in patients with pain. Pain Med 2015;16:823–5.
[119]. Harris RE, Williams DA, McLean SA, Sen A, Hufford M, Gendreau RM, Gracely RH, Clauw DW. Characterization and consequences of pain variability in individuals with fibromyalgia. Arthritis Rheum 2005;52:3670–4.
[120]. Hasenbring MI, Chehadi O, Titze C, Kreddig N. Fear and anxiety in the transition from acute to chronic pain: there is evidence for endurance besides avoidance. Pain Manag 2014;4:363–74.
[121]. Hasenbring MI, Hallner D, Klasen B, Streitlein-Bohme I, Willburger R, Rusche H. Pain-related avoidance versus endurance in primary care patients with subacute back pain: psychological characteristics and outcome at a 6-month follow-up. PAIN 2012;153:211–17.
[122]. Herrmann DN, Pannoni V, Barbano RL, Pennella-Vaughan J, Dworkin RH. Skin biopsy and quantitative sensory testing do not predict response to lidocaine patch in painful neuropathies. Muscle Nerve 2006;33:42–8.
[123]. Hill JC, Lewis M, Sim J, Hay EM, Dziedzic K. Predictors of poor outcome in patients with neck pain treated by physical therapy. Clin J Pain 2007;23:683–90.
[124]. Hill JC, Whitehurst DG, Lewis M, Bryan S, Dunn KM, Foster NE, Konstantinou K, Main CJ, Mason E, Somerville S, Sowden G, Vohora K, Hay EM. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 2011;378:1560–71.
[125]. Hochman JR, Davis AM, Elkayam J, Gagliese L, Hawker GA. Neuropathic pain symptoms on the modified painDETECT correlate with signs of central sensitization in knee osteoarthritis. Osteoarthritis Cartilage 2013;21:1236–42.
[126]. Hoper J, Helfert S, Heskamp ML, Maihofner CG, Baron R. High concentration capsaicin for treatment of peripheral neuropathic pain: effect on somatosensory symptoms and identification of treatment responders. Curr Med Res Opin 2014;30:565–74.
[127]. Hsu YW, Somma J, Hung YC, Tsai PS, Yang CH, Chen CC. Predicting postoperative pain by preoperative pressure pain assessment. Anesthesiology 2005;103:613–18.
[128]. Huijnen IP, Rusu AC, Scholich S, Meloto CB, Diatchenko L. Subgrouping of low back pain patients for targeting treatments: evidence from genetic, psychological, and activity-related behavioral approaches. Clin J Pain 2015;31:123–32.
[129]. Huse E, Larbig W, Flor H, Birbaumer N. The effect of opioids on phantom limb pain and cortical reorganization. PAIN 2001;90:47–55.
[130]. Jamison RN, Edwards RR, Liu X, Ross EL, Michna E, Warnick M, Wasan AD. Relationship of negative affect and outcome of an opioid therapy trial among low back pain patients. Pain Pract 2013;13:173–81.
[131]. Jarrell J, Ross S, Robert M, Wood S, Tang S, Stephanson K, Giamberardino MA. Prediction of postoperative pain after gynecologic laparoscopy for nonacute pelvic pain. Am J Obstet Gynecol 2014;211:360–8.
[132]. Jarvik JG, Hollingworth W, Heagerty PJ, Haynor DR, Boyko EJ, Deyo RA. Three-year incidence of low back pain in an initially asymptomatic cohort: clinical and imaging risk factors. Spine 2005;30:1541–8.
[133]. Jena AB, Goldman D, Weaver L, Karaca-Mandic P. Opioid prescribing by multiple providers in Medicare: retrospective observational study of insurance claims. BMJ 2014;348:g1393.
[134]. Jensen MP, Lin CP, Kupper AE, Galer BS, Gammaitoni AR. Cognitive testing and revision of the pain quality assessment scale. Clin J Pain 2013;29:400–10.
[135]. Jensen TS, Baron R, Haanpaa M, Kalso E, Loeser JD, Rice AS, Treede RD. A new definition of neuropathic pain. PAIN 2011;152:2204–5.
[136]. Jones CM, Mack KA, Paulozzi LJ. Pharmaceutical overdose deaths, United States, 2010. JAMA 2013;309:657–9.
[137]. Kalso E, Simpson KH, Slappendel R, Dejonckheere J, Richarz U. Predicting long-term response to strong opioids in patients with low back pain: findings from a randomized, controlled trial of transdermal fentanyl and morphine. BMC Med 2007;5:39.
[138]. Karels CH, Bierma-Zeinstra SM, Burdorf A, Verhagen AP, Nauta AP, Koes BW. Social and psychological factors influenced the course of arm, neck and shoulder complaints. J Clin Epidemiol 2007;60:839–48.
[139]. Karp JF, Yu L, Friedly J, Amtmann D, Pilkonis PA. Negative affect and sleep disturbance may be associated with response to epidural steroid injections for spine-related pain. Arch Phys Med Rehabil 2014;95:309–15.
[140]. Katz J, McDermott MP, Cooper EM, Walther RR, Sweeney EW, Dworkin RH. Psychosocial risk factors for postherpetic neuralgia: a prospective study of patients with herpes zoster. J Pain 2005;6:782–90.
[141]. Katz NP, Mou J, Paillard FC, Turnbull B, Trudeau J, Stoker M. Predictors of response in patients with post-herpetic neuralgia and HIV-associated neuropathy treated with the 8% capsaicin patch (Qutenza(R)). Clin J Pain 2015;31:859–66.
[142]. Keeley P, Creed F, Tomenson B, Todd C, Borglin G, Dickens C. Psychosocial predictors of health-related quality of life and health service utilisation in people with chronic low back pain. PAIN 2008;135:142–50.
[143]. Kehlet H, Jensen TS, Woolf CJ. Persistent postsurgical pain: risk factors and prevention. Lancet 2006;367:1618–25.
[144]. Kerns RD, Turk DC, Rudy TE. The West Haven-Yale Multidimensional Pain Inventory (WHYMPI). PAIN 1985;23:345–56.
[145]. Khan RS, Ahmed K, Blakeway E, Skapinakis P, Nihoyannopoulos L, Macleod K, Sevdalis N, Ashrafian H, Platt M, Darzi A, Athanasiou T. Catastrophizing: a predictive factor for postoperative pain. Am J Surg 2011;201:122–31.
[146]. King CD. Conditioned pain modulation and offset analgesia: different avenues to inhibit pain. PAIN 2014;155:2444–5.
[147]. Kirwan JR, Hewlett S. Patient perspective: reasons and methods for measuring fatigue in rheumatoid arthritis. J Rheumatol 2007;34:1171–3.
[148]. Koroschetz J, Rehm SE, Gockel U, Brosz M, Freynhagen R, Tolle T, Baron R. Fibromyalgia and neuropathic pain–differences and similarities. A comparison of 3057 patients with diabetic painful neuropathy and fibromyalgia. BMC Neurol 2011;11:55.
[149]. Le Bars D, Dickenson AH, Besson JM. Diffuse noxious inhibitory controls (DNIC). I. Effects on dorsal horn convergent neurones in the rat. PAIN 1979;6:283–304.
[150]. Le Bars D, Dickenson AH, Besson JM. Diffuse noxious inhibitory controls (DNIC). II. Lack of effect on non-convergent neurones, supraspinal involvement and theoretical implications. PAIN 1979;6:305–27.
[151]. Le Bars D, Villanueva L, Bouhassira D, Willer JC. Diffuse noxious inhibitory controls (DNIC) in animals and in man. Patol Fiziol Eksp Ter 1992;4:55–65.
[152]. Lemley KJ, Hunter SK, Bement MK. Conditioned pain modulation predicts exercise-induced hypoalgesia in healthy adults. Med Sci Sports Exerc 2015;47:176–84.
[153]. Lewis GN, Rice DA, McNair PJ. Conditioned pain modulation in populations with chronic pain: a systematic review and meta-analysis. J Pain 2012;13:936–44.
[154]. Lewis GN, Rice DA, McNair PJ, Kluger M. Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis. Br J Anaesth 2015;114:551–61.
[155]. Lin CP, Kupper AE, Gammaitoni AR, Galer BS, Jensen MP. Frequency of chronic pain descriptors: implications for assessment of pain quality. Eur J Pain 2011;15:628–33.
[156]. Linton SJ, Boersma K. Early identification of patients at risk of developing a persistent back problem: the predictive validity of the Orebro Musculoskeletal Pain Questionnaire. Clin J Pain 2003;19:80–6.
[157]. Linton SJ, Nicholas MK, Macdonald S, Boersma K, Bergbom S, Maher C. The role of depression and catastrophizing in musculoskeletal pain. Eur J Pain 2011;15:416–22.
[158]. Litt MD, Porto FB. Determinants of pain treatment response and nonresponse: identification of TMD patient subgroups. J Pain 2013;14:1502–13.
[159]. Lovejoy TI, Turk DC, Morasco BJ. Evaluation of the psychometric properties of the revised short-form McGill Pain Questionnaire. J Pain 2012;13:1250–7.
[160]. Mahn F, Hullemann P, Gockel U, Brosz M, Freynhagen R, Tolle TR, Baron R. Sensory symptom profiles and co-morbidities in painful radiculopathy. PLoS One 2011;6:e18018.
[161]. Maier C, Baron R, Tolle TR, Binder A, Birbaumer N, Birklein F, Gierthmühlen J, Flor H, Geber C, Huge V, Krumova EK, Landwehrmeyer GB, Magerl W, Maihöfner C, Richter H, Rolke R, Scherens A, Schwarz A, Sommer C, Tronnier V, Uçeyler N, Valet M, Wasner G, Treede RD. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): somatosensory abnormalities in 1236 patients with different neuropathic pain syndromes. PAIN 2010;150:439–50.
[162]. Mainka T, Malewicz NM, Baron R, Enax-Krumova EK, Treede RD, Maier C. Presence of hyperalgesia predicts analgesic efficacy of topically applied capsaicin 8% in patients with peripheral neuropathic pain. Eur J Pain 2016;20:116–29.
[163]. Makarawung DJ, Becker SJ, Bekkers S, Ring D. Disability and pain after cortisone versus placebo injection for trapeziometacarpal arthrosis and de Quervain syndrome. Hand (N Y) 2013;8:375–81.
[164]. Manfredini D, Favero L, Del Giudice A, Masiero S, Stellini E, Guarda-Nardini L. Axis II psychosocial findings predict effectiveness of TMJ hyaluronic acid injections. Int J Oral Maxillofac Surg 2013;42:364–8.
[165]. Mankovsky T, Lynch M, Clark A, Sawynok J, Sullivan MJ. Pain catastrophizing predicts poor response to topical analgesics in patients with neuropathic pain. Pain Res Manag 2012;17:10–14.
[166]. Martini CH, Yassen A, Krebs-Brown A, Passier P, Stoker M, Olofsen E, Dahan A. A novel approach to identify responder subgroups and predictors of response to low- and high-dose capsaicin patches in postherpetic neuralgia. Eur J Pain 2013;17:1491–501.
[167]. Martucci KT, Eisenach JC, Tong C, Coghill RC. Opioid-independent mechanisms supporting offset analgesia and temporal sharpening of nociceptive information. PAIN 2012;153:1232–43.
[168]. Matcham F, Norton S, Scott DL, Steer S, Hotopf M. Symptoms of depression and anxiety predict treatment response and long-term physical health outcomes in rheumatoid arthritis: secondary analysis of a randomized controlled trial. Rheumatology (Oxford) 2016;55:268–78.
[169]. Mathieson S, Maher CG, Terwee CB, Folly dC, Lin CW. Neuropathic pain screening questionnaires have limited measurement properties. A systematic review. J Clin Epidemiol 2015;68:957–66.
[170]. Mease PJ, Palmer RH, Wang Y. Effects of milnacipran on the multidimensional aspects of fatigue and the relationship of fatigue to pain and function: pooled analysis of 3 fibromyalgia trials. J Clin Rheumatol 2014;20:195–202.
[171]. Moreton BJ, Tew V, das NR, Wheeler M, Walsh DA, Lincoln NB. Pain phenotype in patients with knee osteoarthritis: classification and measurement properties of painDETECT and self-report Leeds assessment of neuropathic symptoms and signs scale in a cross-sectional study. Arthritis Care Res (Hoboken) 2015;67:519–28.
[172]. Morren M, van Dulmen S, Ouwerkerk J, Bensing J. Compliance with momentary pain measurement using electronic diaries: a systematic review. Eur J Pain 2009;13:354–65.
[173]. Morso L, Kent P, Albert HB, Hill JC, Kongsted A, Manniche C. The predictive and external validity of the STarT Back Tool in Danish primary care. Eur Spine J 2013;22:1859–67.
[174]. Narayana A, Katz N, Shillington AC, Stephenson JJ, Harshaw Q, Frye CB, Portenoy RK. National Breakthrough Pain Study: prevalence, characteristics, and associations with health outcomes. PAIN 2015;156:252–9.
[175]. Nicholas MK, Linton SJ, Watson PJ, Main CJ. Early identification and management of psychological risk factors (“yellow flags”) in patients with low back pain: a reappraisal. Phys Ther 2011;91:737–53.
[176]. Nickel JC, Tripp DA. Clinical and psychological parameters associated with pain pattern phenotypes in women with interstitial cystitis/bladder pain syndrome. J Urol 2015;193:138–44.
[177]. Niesters M, Aarts L, Sarton E, Dahan A. Influence of ketamine and morphine on descending pain modulation in chronic pain patients: a randomized placebo-controlled cross-over proof-of-concept study. Br J Anaesth 2013;110:1010–16.
[178]. Niesters M, Hoitsma E, Sarton E, Aarts L, Dahan A. Offset analgesia in neuropathic pain patients and effect of treatment with morphine and ketamine. Anesthesiology 2011;115:1063–71.
[179]. Niesters M, Proto PL, Aarts L, Sarton EY, Drewes AM, Dahan A. Tapentadol potentiates descending pain inhibition in chronic pain patients with diabetic polyneuropathy. Br J Anaesth 2014;113:148–56.
[180]. Norton S, Cosco T, Doyle F, Done J, Sacker A. The Hospital Anxiety and Depression Scale: a meta confirmatory factor analysis. J Psychosom Res 2013;74:74–81.
[181]. O'Brien EM, Waxenberg LB, Atchison JW, Gremillion HA, Staud RM, McCrae CS, Robinson ME. Intraindividual variability in daily sleep and pain ratings among chronic pain patients: bidirectional association and the role of negative mood. Clin J Pain 2011;27:425–33.
[182]. O'Neill S, Manniche C, Graven-Nielsen T, Arendt-Nielsen L. Association between a composite score of pain sensitivity and clinical parameters in low-back pain. Clin J Pain 2014;30:831–8.
[183]. Okifuji A, Hare BD. Do sleep disorders contribute to pain sensitivity? Curr Rheumatol Rep 2011;13:528–34.
[184]. Okifuji A, Hare BD. Nightly analyses of subjective and objective (actigraphy) measures of sleep in fibromyalgia syndrome: what accounts for the discrepancy? Clin J Pain 2011;27:289–96.
[185]. Olesen SS, Graversen C, Bouwense SA, van Goor H, Wilder-Smith OH, Drewes AM. Quantitative sensory testing predicts pregabalin efficacy in painful chronic pancreatitis. PLoS One 2013;8:e57963.
[186]. Ortner CM, Turk DC, Theodore BR, Siaulys MM, Bollag LA, Landau R. The Short-Form McGill Pain Questionnaire-Revised to evaluate persistent pain and surgery-related symptoms in healthy women undergoing a planned cesarean delivery. Reg Anesth Pain Med 2014;39:478–86.
[187]. Ostenfeld T, Krishen A, Lai RY, Bullman J, Green J, Anand P, Scholz J, Kelly M. A randomized, placebo-controlled trial of the analgesic efficacy and safety of the p38 MAP kinase inhibitor, losmapimod, in patients with neuropathic pain from lumbosacral radiculopathy. Clin J Pain2015;31:283–93.
[188]. Paulozzi LJ. Prescription drug overdoses: a review. J Saf Res 2012;43:283–9.
[189]. Petersen KK, Arendt-Nielsen L, Simonsen O, Wilder-Smith O, Laursen MB. Presurgical assessment of temporal summation of pain predicts the development of chronic postoperative pain 12 months after total knee replacement. PAIN 2015;156:55–61.
[190]. Pfau DB, Geber C, Birklein F, Treede RD. Quantitative sensory testing of neuropathic pain patients: potential mechanistic and therapeutic implications. Curr Pain Headache Rep 2012;16:199–206.
[191]. Pfau DB, Krumova EK, Treede RD, Baron R, Toelle T, Birklein F, Eich W, Geber C, Gerhardt A, Weiss T, Magerl W, Maier C. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): reference data for the trunk and application in patients with chronic postherpetic neuralgia. PAIN 2014;155:1002–15.
[192]. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS(R)): depression, anxiety, and anger. Assessment 2011;18:263–83.
[193]. Pilkonis PA, Choi SW, Salsman JM, Butt Z, Moore TL, Lawrence SM, Zill N, Cyranowski JM, Kelly MA, Knox SS, Cella D. Assessment of self-reported negative affect in the NIH Toolbox. Psychiatry Res 2013;206:88–97.
[194]. Pincus T, Santos R, Breen A, Burton AK, Underwood M. A review and proposal for a core set of factors for prospective cohorts in low back pain: a consensus statement. Arthritis Rheum 2008;59:14–24.
[195]. Pincus T, Williams AC, Vogel S, Field A. The development and testing of the depression, anxiety, and positive outlook scale (DAPOS). PAIN 2004;109:181–8.
[196]. Pinto PR, McIntyre T, Almeida A, Araujo-Soares V. The mediating role of pain catastrophizing in the relationship between presurgical anxiety and acute postsurgical pain after hysterectomy. PAIN 2012;153:218–26.
[197]. Porporatti AL, Costa YM, Stuginski-Barbosa J, Bonjardim LR, Conti PC, Svensson P. Quantitative methods for somatosensory evaluation in atypical odontalgia. Braz Oral Res 2015;29:1–7.
[198]. Pud D, Granovsky Y, Yarnitsky D. The methodology of experimentally induced diffuse noxious inhibitory control (DNIC)-like effect in humans. PAIN 2009;144:16–19.
[199]. Raja SN, Haythornthwaite JA, Pappagallo M, Clark MR, Travison TG, Sabeen S, Royall RM, Max MB. Opioids versus antidepressants in postherpetic neuralgia: a randomized, placebo-controlled trial. Neurology 2002;59:1015–21.
[200]. Rakel BA, Zimmerman MB, Geasland K, Embree J, Clark CR, Noiseux NO, Callaghan JJ, Herr K, Walsh D, Sluka KA. Transcutaneous electrical nerve stimulation for the control of pain during rehabilitation after total knee arthroplasty: a randomized, blinded, placebo-controlled trial. PAIN 2014;155:2599–611.
[201]. Ranoux D, Attal N, Morain F, Bouhassira D. Botulinum toxin type A induces direct analgesic effects in chronic neuropathic pain. Ann Neurol 2008;64:274–83.
[202]. Reimer M, Helfert SM, Baron R. Phenotyping neuropathic pain patients: implications for individual therapy and clinical trials. Curr Opin Support Palliat Care 2014;8:124–9.
[203]. Rolke R, Baron R, Maier C, Tolle TR, Treede RD, Beyer A, Binder A, Birbaumer N, Birklein F, Bötefür IC, Braune S, Flor H, Huge V, Klug R, Landwehrmeyer GB, Magerl W, Maihöfner C, Rolko C, Schaub C, Scherens A, Sprenger T, Valet M, Wasserka B. Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values. PAIN 2006;123:231–43.
[204]. Rolke R, Magerl W, Campbell KA, Schalber C, Caspari S, Birklein F, Treede RD. Quantitative sensory testing: a comprehensive protocol for clinical trials. Eur J Pain 2006;10:77–88.
[205]. Rusu AC, Boersma K, Turk DC. Subgroups of pain patients—the potential of customizing treatments. In: Hasenbring M, Rusu AC, Turk DC, editors. From acute to chronic back pain: Risk factors, mechanisms, and clinical implications. London, United Kingdom: Oxford University Press, 2012. p. 485–511.
[206]. Rusu AC, Hasenbring M. Multidimensional Pain Inventory derived classifications of chronic pain: evidence for maladaptive pain-related coping within the dysfunctional group. PAIN 2008;134:80–90.
[207]. Sakurai M, Kanazawa I. Positive symptoms in multiple sclerosis: their treatment with sodium channel blockers, lidocaine and mexiletine. J Neurol Sci 1999;162:162–8.
[208]. Schiavone V, Adamo D, Ventrella G, Morlino M, De Notaris EB, Ravel MG, Kusmann F, Piantadosi M, Pollio A, Fortuna G, Mignogna MD. Anxiety, depression, and pain in burning mouth syndrome: first chicken or egg? Headache 2012;52:1019–25.
[209]. Schiphorst Preuper HR, Geertzen JH, van Wijhe M, Boonstra AM, Molmans BH, Dijkstra PU, Reneman MF. Do analgesics improve functioning in patients with chronic low back pain? an explorative triple-blinded RCT. Eur Spine J 2014;23:800–6.
[210]. Schlereth T, Heiland A, Breimhorst M, Fechir M, Kern U, Magerl W, Birklein F. Association between pain, central sensitization and anxiety in postherpetic neuralgia. Eur J Pain 2015;19:193–201.
[211]. Schmidt-Wilcke T, Ichesco E, Hampson JP, Kairys A, Peltier S, Harte S. Resting state connectivity correlates with drug and placebo response in fibromyalgia patients. Neuroimage Clin 2014;6:252–61.
[212]. Scholz J, Mannion RJ, Hord DE, Griffin RS, Rawal B, Zheng H, Scoffings D, Phillips A, Guo J, Laing RJ, Abdi S, Decosterd I, Woolf CJ. A novel tool for the assessment of pain: validation in low back pain. Plos Med 2009;6:e1000047.
[213]. Schreiber KL, Kehlet H, Belfer I, Edwards RR. Predicting, preventing and managing persistent pain after breast cancer surgery: the importance of psychosocial factors. Pain Manag 2014;4:445–59.
[214]. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008;4:1–32.
[215]. Simpson DM, Schifitto G, Clifford DB, Murphy TK, Durso-De Cruz E, Glue P, Whalen E, Emir B, Scott GN, Freeman R; 1066 HIV Neuropathy Study Group. Pregabalin for painful HIV neuropathy: a randomized, double-blind, placebo-controlled trial. Neurology 2010;74:413–20.
[216]. Skou ST, Graven-Nielsen T, Rasmussen S, Simonsen OH, Laursen MB, Arendt-Nielsen L. Widespread sensitization in patients with chronic pain after revision total knee arthroplasty. PAIN 2013;154:1588–94.
[217]. Skou ST, Graven-Nielsen T, Rasmussen S, Simonsen OH, Laursen MB, Arendt-Nielsen L. Facilitation of pain sensitization in knee osteoarthritis and persistent post-operative pain: a cross-sectional study. Eur J Pain 2014;18:1024–31.
[218]. Smarr KL, Keefer AL. Measures of depression and depressive symptoms: Beck Depression Inventory-II (BDI-II), Center for Epidemiologic Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9). Arthritis Care Res (Hoboken) 2011;63(suppl 11):S454–66.
[219]. Smith AD, Jull G, Schneider G, Frizzell B, Hooper RA, Sterling M. A comparison of physical and psychological features of responders and non-responders to cervical facet blocks in chronic whiplash. BMC Musculoskelet Disord 2013;14:313.
[220]. Smith AD, Jull GA, Schneider GM, Frizzell B, Hooper RA, Sterling MM. Low pain catastrophization and disability predict successful outcome to radiofrequency neurotomy in individuals with chronic whiplash. Pain Pract 2016;16:311–9.
[221]. Smith MT, Wegener ST. Measures of sleep. Arthritis Care Res 2003;49(suppl 5):S184–96.
[222]. Sprenger C, Bingel U, Buchel C. Treating pain with pain: supraspinal mechanisms of endogenous analgesia elicited by heterotopic noxious conditioning stimulation. PAIN 2011;152:428–39.
[223]. Steigerwald I, Muller M, Davies A, Samper D, Sabatowski R, Baron R. Effectiveness and safety of tapentadol prolonged release for severe, chronic low back pain with or without a neuropathic pain component: results of an open-label, phase 3b study. Curr Med Res Opin 2012;28:911–36.
[224]. Steinmiller CL, Roehrs TA, Harris E, Hyde M, Greenwald MK, Roth T. Differential effect of codeine on thermal nociceptive sensitivity in sleepy versus nonsleepy healthy subjects. Exp Clin Psychopharmacol 2010;18:277–83.
[225]. Sullivan MJ, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol Assess 1995;7:524–32.
[226]. Temple R, Stockbridge NL. BiDil for heart failure in black patients: the U.S. Food and Drug Administration perspective. Ann Intern Med 2007;146:57–62.
[227]. Trentin L, Visentin M. The predictive lidocaine test in treatment of neuropathic pain. Minerva Anestesiol 2000;66:157–61.
[228]. Turk DC, Dworkin RH. What should be the core outcomes in chronic pain clinical trials? Arthritis Res Ther 2004;6:151–4.
[229]. Turk DC, Dworkin RH, Allen RR, Bellamy N, Brandenburg N, Carr DB, Cleeland C, Dionne R, Farrar JT, Galer BS, Hewitt DJ, Jadad AR, Katz NP, Kramer LD, Manning DC, McCormick CG, McDermott MP, McGrath P, Quessy S, Rappaport BA, Robinson JP, Royal MA, Simon L, Stauffer JW, Stein W, Tollett J, Witter J. Core outcome domains for chronic pain clinical trials: IMMPACT recommendations. PAIN 2003;106:337–45.
[230]. Turk DC, Dworkin RH, Revicki D, Harding G, Burke LB, Cella D, Cleeland CS, Cowan P, Farrar JT, Hertz S, Max MB, Rappaport BA. Identifying important outcome domains for chronic pain clinical trials: an IMMPACT survey of people with pain. PAIN 2008;137:276–85.
[231]. Turk DC, Okifuji A. Psychological factors in chronic pain: evolution and revolution. J Consult Clin Psychol 2002;70:678–90.
[232]. Turner JA, Holtzman S, Mancl L. Mediators, moderators, and predictors of therapeutic change in cognitive-behavioral therapy for chronic pain. PAIN 2007;127:276–86.
[233]. Unal-Cevik I, Sarioglu-Ay S, Evcik D. A comparison of the DN4 and LANSS questionnaires in the assessment of neuropathic pain: validity and reliability of the Turkish version of DN4. J Pain 2010;11:1129–35.
[234]. Utrillas-Compaired A, De la Torre-Escuredo BJ, Tebar-Martinez AJ, Asunsolo-Del Barco A. Does preoperative psychologic distress influence pain, function, and quality of life after TKA? Clin Orthop Relat Res 2014;472:2457–65.
[235]. Vaegter HB, Handberg G, Graven-Nielsen T. Isometric exercises reduce temporal summation of pressure pain in humans. Eur J Pain 2015;19:973–83.
[236]. Vaegter HB, Handberg G, Graven-Nielsen T. Similarities between exercise-induced hypoalgesia and conditioned pain modulation in humans. PAIN 2014;155:158–67.
[237]. Van Damme S, Crombez G, Bijttebier P, Goubert L, Van Houdenhove B. A confirmatory factor analysis of the Pain Catastrophizing Scale: invariant factor structure across clinical and non-clinical populations. PAIN 2002;96:319–24.
[238]. van der LJ, Ridker PM, van der GY, Visseren FL. Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects. Eur Heart J 2014;35:837–43.
[239]. van Hecke O, Austin SK, Khan RA, Smith BH, Torrance N. Neuropathic pain in the general population: a systematic review of epidemiological studies. PAIN 2014;155:654–62.
[240]. van Wijk G, Veldhuijzen DS. Perspective on diffuse noxious inhibitory controls as a model of endogenous pain modulation in clinical pain syndromes. J Pain 2010;11:408–19.
[241]. Vincent A, Hoskin TL, Whipple MO, Clauw DJ, Barton DL, Benzo RP, Williams DA. OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis. Arthritis Res Ther 2014;16:463.
[242]. Vinik A, Emir B, Cheung R, Whalen E. Relationship between pain relief and improvements in patient function/quality of life in patients with painful diabetic peripheral neuropathy or postherpetic neuralgia treated with pregabalin. Clin Ther 2013;35:612–23.
[243]. Vinik A, Emir B, Parsons B, Cheung R. Prediction of pregabalin-mediated pain response by severity of sleep disturbance in patients with painful diabetic neuropathy and post-herpetic neuralgia. Pain Med 2014;15:661–70.
[244]. Vissers MM, Bussmann JB, Verhaar JA, Busschbach JJ, Bierma-Zeinstra SM, Reijman M. Psychological factors affecting the outcome of total hip and knee arthroplasty: a systematic review. Semin Arthritis Rheum 2012;41:576–88.
[245]. Vlaeyen JW, Morley S. Cognitive-behavioral treatments for chronic pain: what works for whom? Clin J Pain 2005;21:1–8.
[246]. Volders S, Boddez Y, De Peuter S, Meulders A, Vlaeyen JW. Avoidance behavior in chronic pain research: a cold case revisited. Behav Res Ther 2015;64:31–7.
[247]. Wang F, Ruberg SJ, Gaynor PJ, Heinloth AN, Arnold LM. Early improvement in pain predicts pain response at endpoint in patients with fibromyalgia. J Pain 2011;12:1088–94.
[248]. Wasan AD, Michna E, Edwards RR, Katz JN, Nedeljkovic SS, Dolman AJ, Janfaza D, Isaac Z, Jamison RN. Psychiatric comorbidity is prospectively associated with diminished opioid analgesia and increased opioid misuse in patients with chronic low back pain. Anesthesiology 2015;123:861–72.
[249]. Wasan AD, Davar G, Jamison R. The association between negative affect and opioid analgesia in patients with discogenic low back pain. PAIN 2005;117:450–61.
[250]. Wasan AD, Jamison RN, Pham L, Tipirneni N, Nedeljkovic SS, Katz JN. Psychopathology predicts the outcome of medial branch blocks with corticosteroid for chronic axial low back or cervical pain: a prospective cohort study. BMC Musculoskelet Disord 2009;10:22.
[251]. Wasan AD, Kong J, Pham LD, Kaptchuk TJ, Edwards R, Gollub RL. The impact of placebo, psychopathology, and expectations on the response to acupuncture needling in patients with chronic low back pain. J Pain 2010;11:555–63.
[252]. Wasner G, Kleinert A, Binder A, Schattschneider J, Baron R. Postherpetic neuralgia: topical lidocaine is effective in nociceptor-deprived skin. J Neurol 2005;252:677–86.
[253]. Waterman C, Victor TW, Jensen MP, Gould EM, Gammaitoni AR, Galer BS. The assessment of pain quality: an item response theory analysis. J Pain 2010;11:273–9.
[254]. Watson CP, Chipman M, Reed K, Evans RJ, Birkett N. Amitriptyline versus maprotiline in postherpetic neuralgia: a randomized, double-blind, crossover trial. PAIN 1992;48:29–36.
[255]. Watson CP, Gilron I, Sawynok J. A qualitative systematic review of head-to-head randomized controlled trials of oral analgesics in neuropathic pain. Pain Res Manag 2010;15:147–57.
[256]. Westermann A, Krumova EK, Pennekamp W, Horch C, Baron R, Maier C. Different underlying pain mechanisms despite identical pain characteristics: a case report of a patient with spinal cord injury. PAIN 2012;153:1537–40.
[257]. Wideman TH, Hill JC, Main CJ, Lewis M, Sullivan MJ, Hay EM. Comparing the responsiveness of a brief, multidimensional risk screening tool for back pain to its unidimensional reference standards: the whole is greater than the sum of its parts. PAIN 2012;153:2182–91.
[258]. Witt CM, Martins F, Willich SN, Schutzler L. Can I help you? Physicians' expectations as predictor for treatment outcome. Eur J Pain 2012;16:1455–66.
[259]. Witt CM, Schutzler L, Ludtke R, Wegscheider K, Willich SN. Patient characteristics and variation in treatment outcomes: which patients benefit most from acupuncture for chronic pain? Clin J Pain 2011;27:550–5.
[260]. Wong W, Wallace MS. Determination of the effective dose of pregabalin on human experimental pain using the sequential up-down method. J Pain 2014;15:25–31.
[261]. Wright CE, Bovbjerg DH, Montgomery GH, Weltz C, Goldfarb A, Pace B, Silerstein JH. Disrupted sleep the night before breast surgery is associated with increased postoperative pain. J Pain Symptom Manage 2009;37:352–62.
[262]. Xia T, Wilder DG, Gudavalli MR, DeVocht JW, Vining RD, Pohlman KA, Kawchuk GN, Long CR, Goertz CM. Study protocol for patient response to spinal manipulation—a prospective observational clinical trial on physiological and patient-centered outcomes in patients with chronic low back pain. BMC Complement Altern Med 2014;14:292.
[263]. Yang G, Baad-Hansen L, Wang K, Xie QF, Svensson P. A study on variability of quantitative sensory testing in healthy participants and painful temporomandibular disorder patients. Somatosens Mot Res 2014;31:62–71.
[264]. Yarnitsky D. Conditioned pain modulation (the diffuse noxious inhibitory control-like effect): its relevance for acute and chronic pain states. Curr Opin Anaesthesiol 2010;23:611–15.
[265]. Yarnitsky D, Arendt-Nielsen L, Bouhassira D, Edwards RR, Fillingim RB, Granot M, Hansson P, Lautenbacher S, Marchand S, Wilder-Smith O. Recommendations on terminology and practice of psychophysical DNIC testing. Eur J Pain 2010;14:339.
[266]. Yarnitsky D, Bouhassira D, Drewes AM, Fillingim RB, Granot M, Hansson P, Landau R, Marchand S, Matre D, Nilsen KB, Stubhaug A, Treede RD, Wilder-Smith OH. Recommendations on practice of conditioned pain modulation [CPM] testing. Eur J Pain 2015;19:805–6.
[267]. Yarnitsky D, Crispel Y, Eisenberg E, Granovsky Y, Ben Nun A, Sprecher E, Best LA, Granot M. Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk. PAIN 2008;138:22–8.
[268]. Yarnitsky D, Granot M, Granovsky Y. Pain modulation profile and pain therapy: between pro- and anti-nociception. PAIN 2014;155:663–5.
[269]. Yarnitsky D, Granot M, Nahman-Averbuch H, Khamaisi M, Granovsky Y. Conditioned pain modulation predicts duloxetine efficacy in painful diabetic neuropathy. PAIN 2012;153:1193–8.
[270]. Younger J, Gandhi V, Hubbard E, Mackey S. Development of the Stanford Expectations of Treatment Scale (SETS): a tool for measuring patient outcome expectancy in clinical trials. Clin Trials 2012;9:767–76.
[271]. Zheng Z, Feng SJ, Costa C, Li CG, Lu D, Xue CC. Acupuncture analgesia for temporal summation of experimental pain: a randomised controlled study. Eur J Pain 2010;14:725–31.
[272]. Ziegler D, Pritchett YL, Wang F, Desaiah D, Robinson MJ, Hall JA. Impact of disease characteristics on the efficacy of duloxetine in diabetic peripheral neuropathic pain. Diabetes Care 2007;30:664–9.
[273]. Zywiel MG, Mahomed A, Gandhi R, Perruccio AV, Mahomed NN. Measuring expectations in orthopaedic surgery: a systematic review. Clin Orthop Relat Res 2013;471:3446–56.

Phenotype; Central pain modulation; Neuropathic; Quantitative sensory testing; Psychosocial; Sleep

© 2016 International Association for the Study of Pain