OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis

Cluster analysis in fibromyalgia: a systematic review

BACKGROUND

The multifaceted nature of Fibromyalgia syndrome (FM) symptoms has been explored through clusters analysis.

OBJECTIVE

To synthesize the cluster research on FM (variables, methods, patient subgroups, and evaluation metrics).

METHODS

We performed a systematic review following the PRISMA recommendations. Independent searches were performed on PubMed, Embase, Web of Science, and Cochrane Central, employing the terms "fibromyalgia" and "cluster analysis". We included studies dated to January 2024, using the cluster analysis to assess any physical, psychological, clinical, or biomedical variables in FM subjects, and descriptively synthesized the studies in terms of design, cluster method, and resulting patient profiles.

RESULTS

We included 39 studies. Most with a cross-sectional design aiming to classify subsets based on the severity, adjustment, symptomatic manifestations, psychological profiles, and response to treatment, based on demographic and clinical variables. Two to four different profiles were found according to the levels of severity and adjustment to FMS. According to symptom manifestation, two to three clusters described the predominance of pain versus fatigue, and thermal pain sensitivity (less versus more sensitive). Other clusters revealed profiles of personality (pathological versus non-pathological) and psychological vulnerability (suicidal ideation). Additionally, studies identified different responses to treatment (pharmacological and multimodal).

CONCLUSION

Several profiles exist within FMS population, which point out to the need for specific treatment options given the different profiles and an efficient allocation of healthcare resources. We notice a need towards more objective measures, and the validation of the cluster results. Further research might investigate some of the assumptions of these findings, which are further discussed in this paper.

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

Does Pain Acceptance Contribute to Improved Functionality through Walking in Women with Fibromyalgia? Looking at Depressive Comorbidity

In the last decade, research has pointed to physical exercise as an effective treatment in fibromyalgia patients. Some studies have highlighted the role of acceptance and commitment therapy in optimizing the benefits of exercise in patients. However, given the high comorbidity in fibromyalgia, it is necessary to value its possible influence on the effect of certain variables, such as acceptance, on the benefits of treatments, such as physical exercise. Our aim is to test the role of acceptance in the benefits of walking over functional limitation, further assessing whether this model is equally valid, considering depressive symptomatology as an additional differential diagnosis. A cross-sectional study with a convenience sample through contacting Spanish fibromyalgia associations was carried out. A total of 231 women with fibromyalgia (mean age 56.91 years) participated in the study. Data were analyzed with the Process program (Model 4, Model 58, Model 7). The results highlight the role of acceptance as a mediator between walking and functional limitation (B = -1.86, SE = 0.93, 95% CI = [-3.83, -0.15]). This model, when depression is incorporated as a moderator, is significant only in patients without depression, revealing the need for personalized treatments in fibromyalgia, considering their most prevalent comorbidity.

Differences in symptom experience among patients with systemic sclerosis: a cluster analytic approach to identifying subgroups

OBJECTIVES

Symptoms of people who have SSc are heterogeneous and difficult to address clinically. Because diverse symptoms often co-occur and may share common underlying mechanisms, identifying symptoms that cluster together may better target treatment approaches. We sought to identify and characterize patient subgroups based on symptom experience.

METHODS

An exploratory hierarchical agglomerative cluster analysis was conducted to identify subgroups from a large SSc cohort from a single US academic medical centre. Patient-reported symptoms of pain interference, fatigue, sleep disturbance, dyspnoea, depression and anxiety were used for clustering. A multivariate analysis of variance (MANOVA) was used to examine the relative contribution of each variable across subgroups. Analyses of variance were performed to determine participant characteristics based on subgroup assignment. Presence of symptom clusters were tallied within subgroup.

RESULTS

Participants (n = 587; 84% female, 41% diffuse cutaneous subtype, 59% early disease) divided into three subgroups via cluster analysis based on symptom severity: (i) no/minimal, (ii) mild, and (iii) moderate. Participants in mild and moderate symptoms subgroups had similar disease severity, but different symptom presentation. In the mild symptoms subgroup, pain, fatigue and sleep disturbance was the main symptom cluster. Participants in the moderate symptoms subgroup were characterized by co-occurring pain, fatigue, sleep disturbance, depression and anxiety.

CONCLUSION

Identification of distinct symptom clusters, particularly among SSc patients who experience mild and moderate symptoms, suggests potential differences in treatment approach and in mechanisms underlying symptom experience that require further study.

A comparison of pain, fatigue, and function between post-COVID-19 condition, fibromyalgia, and chronic fatigue syndrome: a survey study

ABSTRACT

A growing number of individuals report prolonged symptoms following acute Coronavirus-19 (COVID-19) infection, known as post-COVID-19 condition (post-COVID-19). While studies have emerged investigating the symptom sequelae of post-COVID-19, there has been limited investigation into the characterization of pain, fatigue, and function in these individuals, despite initial reports of a clinical phenotype similar to fibromyalgia syndrome (FMS) and chronic fatigue syndrome (CFS)/myalgic encephalomyelitis (ME). This study aimed to characterize multiple symptom domains in individuals reporting post-COVID-19 and compare its clinical phenotype with those with FMS and CFS. A total of 707 individuals with a single or comorbid diagnosis of post-COVID-19, FMS, and/or CFS completed multiple surveys assessing self-reported pain, fatigue, physical and cognitive function, catastrophizing, kinesiophobia, anxiety, depression, dyspnea, and sleep quality. In all 3 diagnoses, elevated pain, fatigue, anxiety, depression, catastrophizing, and kinesiophobia were reported. Physical and cognitive function were similarly impacted among individuals with post-COVID-19, FMS, and CFS; however, individuals with post-COVID-19 reported lower pain and fatigue than FMS and CFS. The comorbid diagnosis of post-COVID-19 with FMS and/or CFS further exacerbated pain, fatigue, and psychological domains when compared with post-COVID-19 alone. In summary, individuals with post-COVID-19 report a symptom phenotype similar to FMS and CFS, negatively impacting cognitive and physical function, but with less severe pain and fatigue overall. These findings may help direct future investigations of the benefit of a biopsychosocial approach to the clinical management of post-COVID-19.

International, multidisciplinary Delphi consensus recommendations on non-pharmacological interventions for fibromyalgia

OBJECTIVES

To develop evidence-based expert recommendations for non-pharmacological treatments for pain, fatigue, sleep problems, and depression in fibromyalgia.

METHODS

An international, multidisciplinary Delphi exercise was conducted. Authors of EULAR and the Canadian Fibromyalgia Guidelines Group, members of the American Pain Society and clinicians with expertise in fibromyalgia were invited. Participants were asked to select non-pharmacological interventions that could be offered for specific fibromyalgia symptoms and to classify them as either core or adjunctive treatments. An evidence summary was provided to aid the decision making. Items receiving >70% votes were accepted, those receiving <30% votes were rejected and those obtaining 30-70% votes were recirculated for up to two additional rounds.

RESULTS

Seventeen experts participated (Europe (n = 10), North America (n = 6), and Israel (n = 1)) in the Delphi exercise and completed all three rounds. Aerobic exercise, education, sleep hygiene and cognitive behavioural therapy were recommended as core treatments for all symptoms. Mind-body exercises were recommended as core interventions for pain, fatigue and sleep problems. Mindfulness was voted core treatment for depression, and adjunctive treatment for other symptoms. Other interventions, namely music, relaxation, hot bath, and local heat were voted as adjunctive treatments, varying between symptoms.

CONCLUSIONS

This study provided evidence-based expert consensus recommendations on non-pharmacological treatments for fibromyalgia that may be used to individualise treatments in clinical practice targeting the diverse symptoms associated with fibromyalgia.

Efficacy of nonpharmacological interventions for individual features of fibromyalgia: a systematic review and meta-analysis of randomised controlled trials

ABSTRACT

Fibromyalgia is a highly heterogeneous condition, but the most common symptoms are widespread pain, fatigue, poor sleep, and low mood. Nonpharmacological interventions are recommended as first-line treatment of fibromyalgia. However which interventions are effective for the different symptoms is not well understood. The objective of this study was to assess the efficacy of nonpharmacological interventions on symptoms and disease-specific quality of life. Seven databases were searched from their inception until June 1, 2020. Randomised controlled trials comparing any nonpharmacological intervention to usual care, waiting list, or placebo in people with fibromyalgia aged >16 years were included without language restriction. Fibromyalgia Impact Questionnaire (FIQ) was the primary outcome measure. Standardised mean difference and 95% confidence interval were calculated using random effects model. The risk of bias was evaluated using the modified Cochrane tool. Of the 16,251 studies identified, 167 randomised controlled trials (n = 11,012) assessing 22 nonpharmacological interventions were included. Exercise, psychological treatments, multidisciplinary modality, balneotherapy, and massage improved FIQ. Subgroup analysis of different exercise interventions found that all forms of exercise improved pain (effect size [ES] -0.72 to -0.96) and depression (ES -0.35 to -1.22) except for flexibility exercise. Mind-body and strengthening exercises improved fatigue (ES -0.77 to -1.00), whereas aerobic and strengthening exercises improved sleep (ES -0.74 to -1.33). Psychological treatments including cognitive behavioural therapy and mindfulness improved FIQ, pain, sleep, and depression (ES -0.35 to -0.55) but not fatigue. The findings of this study suggest that nonpharmacological interventions for fibromyalgia should be individualised according to the predominant symptom.

Nociplastic pain concept, a mechanistic basis for pragmatic approach to fibromyalgia

Nociplastic pain (NP), as a mechanistic term, denotes pain arising from altered nociception without clear evidence of tissue or somatosensory damage. Fibromyalgia (FM), a prototypical NP condition, incorporates a broad continuum of phenotypes with a distinct neurobiological signature and shared NP attributes. The nociplastic concept may provide a new opportunity for early diagnosis of FM by identifying the characteristic NP features before a state of pain generalization and symptoms clustering. In this approach, even individual symptoms associated with NP features are worthy of attention to denote FM. It may provide a timely diagnosis of FM before clinical progression to a severe and hard-to-manage condition. Furthermore, collecting all various FM phenotypes under the nociplastic concept and not delimiting FM to the only typical presentation allows investigators to identify FM subgroups reflecting potentially distinct pathophysiologic mechanisms and biomarkers. This viewpoint can be served in future studies to develop individualized management. In this review, we postulate a novel approach to early FM diagnosis and management based on NP conceptualization and phenotype recognition. Key Points • FM as a NP condition represents overlapping clinical phenotypes and incomplete presentations especially in early stage of illness. • The mechanistic approach based on the NP features of FM can be implicated in the timely diagnosis and management of FM. • The NP-based approach to FM provides a broader viewpoint beyond FM delimitation to pain generalization and polysymptomatic complaints.

Intraindividual pain variability and phenotypes of pain in sickle cell disease: a secondary analysis from the Pain in Sickle Cell Epidemiology Study

ABSTRACT

Mean pain intensity alone is insufficient to describe pain phenotypes in sickle cell disease (SCD). The objective of this study was to determine impact of day-to-day intraindividual pain variability on patient outcomes in SCD. We calculated metrics of pain variability and pain intensity for 139 participants with <10% missing data in the first 28 days of the Pain in Sickle Cell Epidemiology Study. We performed Spearman rank correlations between measures of intraindividual pain variability and outcomes. We then used k-means clustering to identify phenotypes of pain in SCD. We found that pain variability was inversely correlated with health-related quality of life, except in those with daily or near-daily pain. Pain variability was positively correlated with affective coping, catastrophizing, somatic symptom burden, sickle cell stress, health care utilization, and opioid use. We found 3 subgroups or clusters of pain phenotypes in SCD. Cluster 1 included individuals with the lowest mean pain, lowest temporal instability and dependency, lowest proportion of days with pain and opioid use, and highest physical function. Cluster 2 included individuals with the highest mean pain, highest temporal dependency, highest proportion of days with pain and opioid use, and lowest physical function. Cluster 3 included individuals with high levels of mean pain, highest temporal instability, but with lower temporal dependency, proportion of days with pain and opioid use, and physical function compared with cluster 2. We conclude that intraindividual pain variability is associated with patient outcomes and psychological characteristics in SCD and is useful in delineating phenotypes of pain in SCD.

Coping and Beliefs as Predictors of Functioning and Psychological Adjustment in Fibromyalgia Subgroups

Objectives

Research has pointed to two profiles of persons with fibromyalgia according to differences in functionality, thus distinguishing between functional and dysfunctional patients. The role of psychological factors underlying such clusters is unclear. This study aims to explore the contribution of pain beliefs and coping on fibromyalgia clustering.

Methods

A cluster analysis was performed to classify 238 women with fibromyalgia using the Fibromyalgia Impact Questionnaire and the Beck Depression Inventory as clustering variables. Cluster differences in physical functioning, depression, pain beliefs, coping, and age were then calculated (Student's t-test). Finally, a binary logistic regression was conducted to study the unique contribution of age, beliefs, and coping on cluster classification.

Results

Two clusters were revealed. Cluster 1 had a poor adaptation to fibromyalgia regarding physical functioning and depression. They generally embraced less adaptive beliefs (i.e., disability, harm, emotion, and requests) and coping strategies (i.e., guarding, resting, and asking for assistance). Cluster 2 showed a better adaptation to fibromyalgia and adopted more favorable beliefs (i.e., control) and coping strategies (i.e., exercise and task persistence). Cluster differences in age were significant but small. The backward binary logistic regression suggested a final model with six predictors (guarding, task persistence, harm, emotion, solicitude, and age) that explained 31% of the variance of group membership. Discussion. These results suggest that only a subset of psychological variables uniquely and independently contribute to functional/dysfunctional group membership. The results support the need to address psychological components in the management of fibromyalgia and point to a subset of preferred target beliefs and coping strategies.

Implication of the Nociplastic Features for Clinical Diagnosis of Fibromyalgia: Development of the Preliminary Nociplastic-Based Fibromyalgia Features (NFF) Tool

OBJECTIVE

Nociplastic concept incorporates a broad continuum of pain phenotypes shared with clinical peculiarity. This study aimed to develop and validate a diagnostic tool, the preliminary Nociplastic-based Fibromyalgia Features (NFF), to detect fibromyalgia (FM) in patients with chronic pain.

METHODS

Items requiring yes or no responses and relating to the most relevant clinical nociplastic pain (NP) features of FM were compiled by a group of expert rheumatologists. The provisional list was tested in a prospective study on 185 consecutive patients with chronic pain (126 patients with FM and 59 patients with non-FM non-inflammatory chronic pain) diagnosed based on expert decision. Identification of the most discriminant combinations of items for FM and the calculation of their sensitivity and specificity were based on both univariate and multivariate (stepwise logistic regression) analyses. All participants were investigated through the final NFF, the 2011 American College of Rheumatology (ACR) criteria, and the 2016 ACR criteria. NFF performance was assessed with receiver operating characteristic curve analysis.

RESULTS

Based on multivariate analyses, we retained only seven items in the final version of the NFF. A cut-off score of 4 (corresponding to the number of positive items) gave the highest rate of correct identification of patients (85%), with a sensitivity of 82% and a specificity of 91%. The NFF showed the highest concordance rate with expert diagnosis (85%) and the lowest value (77%) with the ACR 2016 criteria.

CONCLUSION

The preliminary NFF with respect to the various aspects of NP showed good performance for detection of the FM in the clinical setting. This tool may provide a more pragmatic approach to the timely diagnosis of FM.

Hierarchical clustering by patient-reported pain distribution alone identifies distinct chronic pain subgroups differing by pain intensity, quality, and clinical outcomes

Background

In clinical practice, the bodily distribution of chronic pain is often used in conjunction with other signs and symptoms to support a diagnosis or treatment plan. For example, the diagnosis of fibromyalgia involves tallying the areas of pain that a patient reports using a drawn body map. It remains unclear whether patterns of pain distribution independently inform aspects of the pain experience and influence patient outcomes. The objective of the current study was to evaluate the clinical relevance of patterns of pain distribution using an algorithmic approach agnostic to diagnosis or patient-reported facets of the pain experience.

Methods and findings

A large cohort of patients (N = 21,658) completed pain body maps and a multi-dimensional pain assessment. Using hierarchical clustering of patients by body map selection alone, nine distinct subgroups emerged with different patterns of body region selection. Clinician review of cluster body maps recapitulated some clinically-relevant patterns of pain distribution, such as low back pain with radiation below the knee and widespread pain, as well as some unique patterns. Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality all varied significantly across cluster subgroups. Multivariate modeling demonstrated that cluster membership independently predicted pain intensity and neuropathic pain quality. In a subset of patients who completed 3-month follow-up questionnaires (N = 7,138), cluster membership independently predicted the likelihood of improvement in pain, physical function, and a positive overall impression of change related to multidisciplinary pain care.

Conclusions

This study reports a novel method of grouping patients by pain distribution using an algorithmic approach. Pain distribution subgroup was significantly associated with differences in pain intensity, impact, and clinically relevant outcomes. In the future, algorithmic clustering by pain distribution may be an important facet in chronic pain biosignatures developed for the personalization of pain management.

Fibromyalgia as a Heterogeneous Condition: Subgroups of Patients Based on Physical Symptoms and Cognitive-Affective Variables Related to Pain

Fibromyalgia (FM) is a chronic syndrome characterized by heterogeneous clinical manifestations, and knowing this variability can help to develop tailored treatments. To understand better the heterogeneity of FM the present cross-sectional study analyzed the role of several physical symptoms (pain, fatigue and poor sleep quality) and cognitive-affective variables related to pain (pain catastrophizing, pain vigilance, self-efficacy in pain management, and pain acceptance) in the configuration of clinical profiles. A sample of 161 women with FM fulfilled an interview and several self-report measures to explore physical symptoms, cognitive-affective variables, disability and psychopathology. To establish FM groups a hierarchical cluster analysis was performed. The findings revealed three clusters that differed in the grouping variables, Wilks' λ = .17, F(14, 304) = 31.50, p < .001, ηp2 = .59. Group 1 (n = 72) was characterized by high physical and psychological affectation, Group 2 (n = 19) by low physical affectation and high pain self-efficacy, and Group 3 (n = 70) by moderate physical affectation and low pain catastrophizing. The external validation of the clusters was confirmed, Wilks' λ = .72, F(4, 314) = 14.09, p < .001, ηp2 = .15, showing Group 1 the highest levels of FM impact and psychopathological distress. Considering the distinctive clinical characteristics of each subgroup therapeutic strategies addressed to the specific needs of each group were suggested. Assessing FM profiles may be key for a better understanding and approach of this syndrome.

Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients

ABSTRACT

Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions study) and a real-world clinical population of patients seeking treatment at duke innovative pain therapies. In each cohort, we applied a ROPA for cluster prediction, which requires only 4 input variables: pressure pain threshold and anxiety, depression, and somatization scales. In both complex persistent pain condition and duke innovative pain therapies, we distinguished 3 clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain.

Machine learning suggests sleep as a core factor in chronic pain

Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. Unsupervised machine learning analysis of parameters "pain intensity," "number of pain areas," "pain duration," "activity pain interference," and "affective pain interference," implemented as emergent self-organizing maps, identified 3 patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the 2 extreme pain phenotype clusters. Also important were the parameters "fear of pain," "self-rated poor health," and "systolic blood pressure." Decision trees trained with this information assigned patients to the extreme pain phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain.

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

There is tremendous interpatient variability in the response to analgesic therapy (even for efficacious treatments), which can be the source of great frustration in clinical practice. This has led to calls for “precision medicine” or personalized pain therapeutics (ie, empirically based algorithms that determine the optimal treatments, or treatment combinations, for individual patients) that would presumably improve both the clinical care of patients with pain and the success rates for putative analgesic drugs in phase 2 and 3 clinical trials. However, before implementing this approach, the characteristics of individual patients or subgroups of patients that increase or decrease the response to a specific treatment need to be identified. The challenge is to identify the measurable phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics. In this article, we present evidence on the most promising of these phenotypic characteristics for use in future research, including psychosocial factors, symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge. We provide evidence-based recommendations for core phenotyping domains and recommend measures of each domain.

Subgrouping a Large U.S. Sample of Patients with Fibromyalgia Using the Fibromyalgia Impact Questionnaire-Revised

Fibromyalgia (FM) is a heterogeneous and complex syndrome; different studies have tried to describe subgroups of FM patients, and a 4-cluster classification based on the Fibromyalgia Impact Questionnaire-Revised (FIQR) has been recently validated. This study aims to cross-validate this classification in a large US sample of FM patients. A pooled sample of 6280 patients was used. First, we computed a hierarchical cluster analysis (HCA) using FIQR scores at item level. Then, a latent profile analysis (LPA) served to confirm the accuracy of the taxonomy. Additionally, a cluster calculator was developed to estimate the predicted subgroup using an ordinal regression analysis. Self-reported clinical measures were used to examine the external validity of the subgroups in part of the sample. The HCA yielded a 4-subgroup distribution, which was confirmed by the LPA. Each cluster represented a different level of severity: “Mild–moderate”, “moderate”, “moderate–severe”, and “severe”. Significant differences between clusters were observed in most of the clinical measures (e.g., fatigue, sleep problems, anxiety). Interestingly, lower levels of education were associated with higher FM severity. This study corroborates a 4-cluster distribution based on FIQR scores to classify US adults with FM. The classification may have relevant clinical implications for diagnosis and treatment response.

Suicidal Ideation Profiles in Patients with Fibromyalgia Using Transdiagnostic Psychological and Fibromyalgia-Associated Variables

Several studies have emphasized the heterogeneity of fibromyalgia patients. Furthermore, fibromyalgia patients are considered a high-risk suicide group. The ideation-to-action framework proposes a set of transdiagnostic psychological factors involved in the development of suicidal ideation. The present study aims to explore the existence of different subgroups according to their vulnerability to suicidal ideation through these transdiagnostic psychological variables and a set of variables typically associated with fibromyalgia. In this cross-sectional study, 151 fibromyalgia patients were assessed through the Revised Fibromyalgia Impact Questionnaire, Beck Depression Inventory-II, Plutchik Suicide Risk Scale, Interpersonal Needs Questionnaire, Defeat Scale, Entrapment Scale, Psychache Scale, and Beck Hopelessness Scale. A K-means cluster analysis identified two clusters, one (45.70%) according to a low vulnerability, and a second (54.30%) with a high vulnerability to suicidal ideation. These clusters showed statistically significant differences in suicidal ideation and suicide risk. However, no differences were observed in most socio-demographic variables. In conclusion, fibromyalgia patients who present a clinical condition characterized by a moderate-high degree of physical dysfunction, overall disease impact and intensity of fibromyalgia-associated symptoms, along with a high degree of perceived burdensomeness, thwarted belongingness, defeat, entrapment, psychological pain and hopelessness, form a homogeneous group at high risk for suicidal ideation.

Clustering fibromyalgia patients: A combination of psychosocial and somatic factors leads to resilient coping in a subgroup of fibromyalgia patients

Background

Coping strategies and their efficacy vary greatly in patients suffering from fibromyalgia syndrome (FMS).

Objective

We aimed to identify somatic and psychosocial factors that might contribute to different coping strategies and resilience levels in FMS.

Subjects and methods

Standardized questionnaires were used to assess coping, pain, and psychological variables in a cohort of 156 FMS patients. Quantitative real-time polymerase chain reaction (qRT-PCR) determined gene expression of selected cytokines in white blood cells of 136 FMS patients and 25 healthy controls. Data of skin innervation, functional and structural sensory profiles of peripheral nociceptive nerve fibers of a previous study were included into the statistics. An exploratory factor analysis was used to define variance explaining factors, which were then included into cluster analysis.

Results

54.9% of the variance was explained by four factors which we termed (1) affective load, (2) coping, (3) pain, and (4) pro-inflammatory cytokines (p < 0.05). Considering differences in the emerged factors, coping strategies, cytokine profiles, and disability levels, 118 FMS patients could be categorized into four clusters which we named “maladaptive”, “adaptive”, “vulnerable”, and “resilient” (p < 0.05). The adaptive cluster had low scores in disability and in all symptom categories in contrast to the vulnerable cluster, which was characterized by high scores in catastrophizing and disability (p < 0.05). The resilient vs. the maladaptive cluster was characterized by better coping and a less pro-inflammatory cytokine pattern (p < 0.05).

Conclusion

Our data suggest that problem- and emotion-focused coping strategies and an anti-inflammatory cytokine pattern are associated with reduced disability and might promote resilience. Additional personal factors such as low anxiety scores, ability of acceptance, and persistence further favor a resilient phenotype. Individualized therapy should take these factors into account.

Assessing our approach to diagnosing Fibromyalgia

Introduction: Fibromyalgia represents the most prevalent of the group of conditions that are known as central sensitivity syndromes. Approximately 2-5% of the adult population in the United States is affected by Fibromyalgia. This pain amplification syndrome has an enormous economic impact as measured by work absenteeism, decreased work productivity, disability and injury compensation, and over-utilization of healthcare resources. Multiple studies have shown that early diagnosis of this condition can improve patient outlook, and redirect valuable health care resources toward more appropriate targeted therapy. Efforts have been made toward improving diagnostic accuracy through updated criteria. Areas Covered: Reviewed here are 1) reasons for the need for more accurate diagnosis of Fibromyalgia, (2) a review of the evolution of Fibromyalgia to current times, and (3) the proliferation of currently available diagnostic criteria and problems related to each of them. From initial literature review until October 2020, PubMed, Embase, and Scopus were searched for applicable literature. Expert Opinion: A discussion of ongoing efforts to obtain a biomarker to enhance diagnostic accuracy concludes this review. A need to include rheumatologists as part of the care team of patients with Fibromyalgia is emphasized.

The Co-Morbidity between Bipolar and Panic Disorder in Fibromyalgia Syndrome

About half of the patients with fibromyalgia (FM) had a lifetime major depression episode and one third had a panic disorder (PD). Because the co-morbidity between bipolar disorder (BD) and PD marks a specific subtype of BD we aimed to investigate if co-morbid BD/PD (comBD/PD) occurs more frequently than the single disorder in FM patients and evaluate the clinical significance and timing of this co-morbidity. Further, we explored the role of co-morbid subthreshold BD and PD. In 118 patients with FM, lifetime threshold and sub-threshold mood disorders and PD were diagnosed with Diagnostic and Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV-TR) Clinical Interview. Demographic and clinical variables were compared in co-morbid BD/PD (comBD/PD) and not co-morbid BD/PD (nocomBD/PD) subgroups. The co-morbidity BD/PD was seen in 46.6% of FM patients and in 68.6% when patients with minor bipolar (MinBD) and sub-threshold panic were included. These rates are higher than those of the general population and BD outpatients. There were no statistically significant differences between threshold and sub-threshold comBD/PD and nocom-BD/PD subgroups in demographic and clinical parameters. In the majority of patients (78.2%), the onset of comBD/PD preceded or was contemporary with FM. These findings support the hypothesis that comBD/PD is related to the development of FM in a subgroup of patients.

A Step Towards a Better Understanding of Pain Phenotypes: Latent Class Analysis in Chronic Pain Patients Receiving Multimodal Inpatient Treatment

Purpose

The number of non-responders to treatment among patients with chronic pain (CP) is high, although intensive multimodal treatment is broadly accessible. One reason is the large variability in manifestations of CP. To facilitate the development of tailored treatment approaches, phenotypes of CP must be identified. In this study, we aim to identify subgroups in patients with CP based on several aspects of self-reported health.

Patients and Methods

A latent class analysis (LCA) was carried out in retrospective data from 411 patients with CP of different origins. All patients experienced severe physical and psychosocial consequences and were therefore undergoing multimodal inpatient pain treatment. Self-reported measures of pain (visual analogue scales for pain intensity, frequency, and impairment; Pain Perception Scale), emotional distress (Patient Health Questionnaire, PHQ-9; Generalized Anxiety Disorder Scale, GAD-7) and physical health (Short Form Health Survey; SF-8) were collected immediately after admission and before discharge. Instruments assessed at admission were used as input to the LCA. Resulting classes were compared in terms of patient characteristics and treatment outcome.

Results

A model with four latent classes demonstrated the best model fit and interpretability. Classes 1 to 4 included patients with high (54.7%), extreme (17.0%), moderate (15.6%), and low (12.7%) pain burden, respectively. Patients in class 4 showed high levels of emotional distress, whereas emotional distress in the other classes corresponded to the levels of pain burden. While pain as well as physical and mental health improved in class 1, only the levels of depression and anxiety improved in patients in the other groups during multimodal treatment.

Conclusion

The specific needs of these subgroups should be taken into account when developing individualized treatment programs. However, the retrospective design limits the significance of the results and replication in prospective studies is desirable.

Metabolomics in Central Sensitivity Syndromes

Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties in central sensitization disorders may contribute to the identification of novel therapeutic targets and more precise treatment options. The analysis of small molecules in biological samples through metabolomics has improved greatly and may be the technology needed to identify key moieties in difficult to diagnose diseases. In this review, we discuss the current state of metabolomics as it relates to central sensitization disorders. From initial literature review until Feb 2020, PubMed, Embase, and Scopus were searched for applicable studies. We included cohort studies, case series, and interventional studies of both adults and children affected by central sensitivity syndromes. The majority of metabolomic studies addressing a CSS found significantly altered metabolites that allowed for differentiation of CSS patients from healthy controls. Therefore, the published literature overwhelmingly supports the use of metabolomics in CSS. Further research into these altered metabolites and their respective metabolic pathways may provide more reliable and effective therapeutics for these syndromes.

Clustering Clinical Data in R

We are currently witnessing a paradigm shift from evidence-based medicine to precision medicine, which has been made possible by the enormous development of technology. The advances in data mining algorithms will allow us to integrate trans-omics with clinical data, contributing to our understanding of pathological mechanisms and massively impacting on the clinical sciences. Cluster analysis is one of the main data mining techniques and allows for the exploration of data patterns that the human mind cannot capture.This chapter focuses on the cluster analysis of clinical data, using the statistical software, R. We outline the cluster analysis process, underlining some clinical data characteristics. Starting with the data preprocessing step, we then discuss the advantages and disadvantages of the most commonly used clustering algorithms and point to examples of their applications in clinical work. Finally, we briefly discuss how to perform validation of clusters. Throughout the chapter we highlight R packages suitable for each computational step of cluster analysis.

Fibromyalgia in the workplace: risk factors for sick leave are related to professional context rather than fibromyalgia characteristics- a French national survey of 955 patients

Background

Work and workplace factors are important in fibromyalgia management. We investigated factors associated with sick leave in professionally active women living with fibromyalgia.

Methods

A questionnaire for fibromyalgia patients in employment was developed by pain and occupational physicians and patients' organizations. Women in full-time work, screened for fibromyalgia with the FiRST questionnaire, were recruited for a national online survey. Sick leave over the preceding year was analyzed.

Results

In 5 months, we recruited 955 women, with a mean of 37 days of sick leave in the previous year: no sick leave (36%), up to 1 month (38%), 1 to 2 months (14%), more than 2 months (12%). In the groups displayed no differences in demographic characteristics, fibromyalgia symptoms, functional severity and psychological distress were observed. However, they differed in workplace characteristics, commute time, stress and difficulties at work, repetitive work, noisy conditions, career progression problems and lack of recognition, which were strong independent risk factors for longer sick leave. Sedentary positions, an extended sitting position, heavy loads, exposure to thermal disturbances and the use of vibrating tools did not increase the risk of sick leave.

Conclusions

Women with fibromyalgia frequently take sick leave, the risk factors for which are related to the workplace rather than fibromyalgia characteristics.

Perspective

This is the first study to assess the impact of occupational and clinical factors on sick leave in women living with fibromyalgia. Risk factors were found to be related to the workplace rather than fibromyalgia and personal characteristics. Workplace interventions should be developed for women with fibromyalgia.

Use of Cluster Analysis to Delineate Symptom Profiles in an Ehlers-Danlos Syndrome Patient Population

CONTEXT

The Ehlers-Danlos Syndromes (EDSs) are a set of rare heritable disorders of connective tissue, characterized by defects in the structure and synthesis of extracellular matrix elements that lead to a myriad of problems including joint hypermobility and skin abnormalities. Because EDS affects multiple organ systems, defining clear boundaries and recognizing overlapping clinical features shared by disease phenotypes is challenging.

OBJECTIVES

The objective of this study was to seek evidence of phenotypic subgroups of patients with distinctive symptom profiles and describe these resulting subgroups.

METHODS

Data were extracted from a repository assembled 2001-2013 by the National Institute on Aging Intramural Research Program. Agglomerative hierarchical clustering was used to form distinct subgroups of patients with respect to the domains of pain, physical and mental fatigue, daytime sleepiness, and nighttime sleep. Domains were selected based on literature review, clinician expertise, and guidance from patient advisors.

RESULTS

One hundred seventy-five patients met all inclusion criteria. Three subgroups were identified. The Pain Dominant subgroup (39 patients) had the highest mean pain values, but lowest mean values of other symptoms. The High Symptom Burden subgroup (71 patients) had high mean values in all domains. The Mental Fatigue subgroup (65 patients) had a high mean value for mental fatigue and daytime sleepiness, but a lower mean value for pain.

CONCLUSION

The subgroups aligned with clinical observation of the heterogeneous nature of EDS, with overlapping symptoms between subtypes and a wide divergence in degree of symptoms within subtypes. This exploratory study helps characterize the various phenotypes and comorbidities of patients with EDS.

Metabolic fingerprinting for diagnosis of fibromyalgia and other rheumatologic disorders

Diagnosis and treatment of fibromyalgia (FM) remains a challenge owing to the lack of reliable biomarkers. Our objective was to develop a rapid biomarker-based method for diagnosing FM by using vibrational spectroscopy to differentiate patients with FM from those with rheumatoid arthritis (RA), osteoarthritis (OA), or systemic lupus erythematosus (SLE) and to identify metabolites associated with these differences. Blood samples were collected from patients with a diagnosis of FM (n = 50), RA (n = 29), OA (n = 19), or SLE (n = 23). Bloodspot samples were prepared, and spectra collected with portable FT-IR and FT-Raman microspectroscopy and subjected to metabolomics analysis by ultra-HPLC (uHPLC), coupled to a photodiode array (PDA) and tandem MS/MS. Unique IR and Raman spectral signatures were identified by pattern recognition analysis and clustered all study participants into classes (FM, RA, and SLE) with no misclassifications (p < 0.05, and interclass distances > 2.5). Furthermore, the spectra correlated (r = 0.95 and 0.83 for IR and Raman, respectively) with FM pain severity measured with fibromyalgia impact questionnaire revised version (FIQR) assessments. Protein backbones and pyridine-carboxylic acids dominated this discrimination and might serve as biomarkers for syndromes such as FM. uHPLC-PDA-MS/MS provided insights into metabolites significantly differing among the disease groups, not only in molecular m/z+ and m/z- values but also in UV-visible chromatograms. We conclude that vibrational spectroscopy may provide a reliable diagnostic test for differentiating FM from other disorders and for establishing serologic biomarkers of FM-associated pain.

Long-term outcomes of adolescents with juvenile-onset fibromyalgia into adulthood and impact of depressive symptoms on functioning over time

Juvenile-onset fibromyalgia (JFM) is typically diagnosed in adolescence and characterized by widespread pain and marked functional impairment. The long-term impact of JFM into adulthood is poorly understood. The objectives of this study were to describe physical and psychosocial outcomes of youth diagnosed with JFM in early adulthood (∼8-year follow-up), examine longitudinal trajectories of pain and depressive symptoms from adolescence to young adulthood, and examine the impact of pain and depressive symptoms on physical functioning over time. Participants were 97 youth with JFM enrolled in a prospective longitudinal study in which pain symptoms, and physical and psychosocial functioning were assessed at 4 time points over approximately 8 years. At the time 4 follow-up (Mage = 24.2 years), the majority continued to suffer from pain and impairment in physical, social, and psychological domains. However, trajectories of pain and emotional symptoms showed varying patterns. Longitudinal analysis using growth mixture modeling revealed 2 pain trajectories (Steady Improvement and Rapid Rebounding Improvement), whereas depressive symptoms followed 3 distinct trajectories (Low-Stable, Improving, and Worsening). Membership in the Worsening Depressive symptoms group was associated with poorer physical functioning over time (P < 0.001) compared with the Low-Stable and Improving groups. This study offers evidence that although JFM symptoms persist for most individuals, pain severity tends to decrease over time. However, depressive symptoms follow distinct trajectories that indicate subgroups of JFM. In particular, JFM patients with worsening depressive symptoms showed decreasing physical functioning and may require more intensive and consistent intervention to prevent long-term disability.

Characterizing classes of fibromyalgia within the continuum of central sensitization syndrome

Background

While fibromyalgia (FM) is characterized by chronic widespread pain and tenderness, its presentation among patients as a continuum of diseases rather than a single disease contributes to the challenges of diagnosis and treatment. The purpose of this analysis was to distinguish and characterize classes of FM within the continuum using data from chronic pain patients.

Methods

FM patients were identified from administrative claims data from the ProCare Systems’ network of Michigan pain clinics between January 1999 and February 2015. Identification was based on either use of traditional criteria (ie, ICD-9 codes) or a predictive model indicative of patients having FM. Patients were classified based on similarity of comorbidities (symptom severity), region of pain (widespread pain), and type and number of procedures (treatment intensity) using unsupervised learning. Text mining and a review of physician notes were conducted to assist in understanding the FM continuum.

Results

A total of 2,529 FM patients with 79,570 observations or clinical visits were evaluated. Four main classes of FM patients were identified: Class 1) regional FM with classic symptoms; Class 2) generalized FM with increasing widespread pain and some additional symptoms; Class 3) FM with advanced and associated conditions, increasing widespread pain, increased sleep disturbance, and chemical sensitivity; and Class 4) FM secondary to other conditions.

Conclusion

FM is a disease continuum characterized by progressive and identifiable classifications. Four classes of FM can be differentiated by pain and symptom severity, specific comorbidities, and use of clinical procedures.

Challenges in fibromyalgia diagnosis: from meaning of symptoms to fibromyalgia labeling

Fibromyalgia (FM) is a contested illness with ill-defined boundaries. There is no clearly defined cut-point that separates FM from non-FM. Diagnosis of FM has been faced with several challenges that occur, including patients' health care-seeking behavior, symptoms recognition, and FM labeling by physicians. This review focuses on important but less visible factors that have a profound influence on under- or over-diagnosis of FM. FM shows different phenotypes and disease expression in patients and even in one patient over time. Psychosocial and cultural factors seem to be a contemporary ferment in FM which play a major role in physician diagnosis even more than having severe symptom levels in FM patients. Although the FM criteria are the only current methods which can be used for classification of FM patients in surveys, research, and clinical settings, there are several key pieces missing in the fibromyalgia diagnostic puzzle, such as invalidation, psychosocial factors, and heterogeneous disease expression. Regarding the complex nature of FM, as well as the arbitrary and illusory constructs of the existing FM criteria, FM diagnosis frequently fails to provide a clinical diagnosis fit to reality. A physicians' judgment, obtained in real communicative environments with patients, beyond the existing constructional scores, seems the only reliable way for more valid diagnoses. It plays a pivotal role in the meaning and conceptualization of symptoms and psychosocial factors, making diagnoses and labeling of FM. It is better to see FM as a whole, not as a medical specialty or constructional scores.

Longitudinal stability of fibromyalgia symptom clusters

Background

Using self-report questionnaires of key fibromyalgia symptom domains (pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety), we previously identified four unique symptom clusters. The purpose of this study was to examine the stability of fibromyalgia symptom clusters between baseline and 2-year follow-up.

Methods

Women with a diagnosis of fibromyalgia completed the Brief Pain Inventory, Profile of Mood States, Medical Outcomes Study Sleep measure, Multidimensional Fatigue Inventory, Multiple Ability Self-Report Questionnaire, Revised Fibromyalgia Impact Questionnaire, and the 36-Item Short Form Survey Instrument at baseline. Follow-up measures were completed approximately 2 years later. The hierarchical agglomerative clustering algorithm previously developed was applied; agreement between baseline and follow-up was assessed with the κ statistic.

Results

Among 433 participants, the mean age was 56 (range 20–85) years. The median Revised Fibromyalgia Impact Questionnaire total score was 57 (range 8–96). More than half of participants (58%) remained in the same cluster at follow-up as at baseline, which represented moderate agreement between baseline and follow-up (κ = 0.44, 95% confidence interval (CI) 0.37–0.50). Only two patients changed from high symptom intensity to low symptom intensity; similarly, only three moved from low to high.

Conclusions

Fibromyalgia patients classified into four unique symptom clusters based on the key domains of pain, fatigue, sleep disturbance, function, stiffness, dyscognition, depression, and anxiety showed moderate stability in cluster assignment after 2 years. Few patients moved between the two extremes of severity, and it was slightly more common to move to a lower symptom level than to worsen.

Trial registration

Not applicable.

Pain and Fatigue Variability Patterns Distinguish Subgroups of Fibromyalgia Patients

The current study examined between- and within-subject variability in pain-related symptoms as predictors of pain and fatigue, and identified patient subgroups on the basis of symptom variability characteristics. Two hundred fifty-six fibromyalgia (FM) patients completed daily diaries up to a period of 154 days and reported on symptoms of pain intensity, pain unpleasantness, fatigue, anxiety, and depressed mood. Measures of health status, quality of life, and somatic symptoms were obtained at baseline, and hierarchical linear modeling and cluster analyses were used. Significant intra- and interindividual variability in daily FM symptoms was observed. Higher levels of pain were associated with greater fluctuations in pain unpleasantness, fatigue, and depressed mood. Similar effects were observed for fatigue and individual variability in anxiety also emerged as a robust predictor. Three FM subgroups were revealed: low variability in symptoms (cluster 1), high symptom variability (cluster 2), and a mixed variability group characterized by low fluctuation in pain unpleasantness; moderate pain, fatigue, and depressed mood variability; and high anxiety variability (cluster 3). Cluster 3 exhibited lower social functioning and higher levels of pain, compared with cluster 1. These findings support the dynamic nature of FM pain and suggest the presence of FM subgroups on the basis of variation in mood and pain symptomatology.

PERSPECTIVE

FM patients show significant intra- and interindividual variability in pain, mood, and fatigue. Subgroups in mood and pain-related variability emerged, with phenotypic clusters differing across levels of pain intensity and social functioning. Better understanding of the processes affecting pain variability may facilitate targeted treatments for the control of pain.

Adaptation profiles comprising objective and subjective measures in fibromyalgia: the al-Ándalus project

Objectives

The aim of this study was to identify subgroups in terms of adaptation to FM and to test differences in FM severity between these subgroups.

Methods

The al-Ándalus project made it possible to perform a comprehensive population-based cross-sectional study in 486 FM patients including multiple assessments of modifiable (could be targeted in therapy) resilience and vulnerability factors, measured by objective and subjective assessments, related to psychological and physical function. FM severity was assessed by means of FM impact (total score of the Revised Fibromyalgia Impact Questionnaire) and distress (Polysymptomatic Distress Scale of the modified 2011 preliminary criteria for FM). Exploratory factor analysis, cluster analysis and analysis of variance were conducted.

Results

Factor analysis yielded eight factors: three included objective measures (declarative memory, active lifestyle and objective physical fitness) and five included subjective measures (fatigue, psychological distress, catastrophizing, resilience and subjective physical fitness). Cluster analysis based on these eight factors identified five profiles: Adapted (16%), Fit (18%), Poor performer (20%), Positive (20%) and Maladapted (26%). Most profile comparisons revealed different levels of FM severity varying from Adapted (the most favourable profile) to Maladapted (the most unfavourable profile) with Fit, Poor performer and Positive obtaining intermediate positions.

Conclusions

Heterogeneity of FM was shown by five clinically meaningful profiles of modifiable factors that were associated with FM severity. It is of clinical interest to examine whether these profiles are associated with FM prognosis and the effectiveness of interventions, which would enhance the development of customized interventions based on adaptation profiles in FM.

Somatic symptom presentations in women with fibromyalgia are differentially associated with elevated depression and anxiety

This study examined whether depression and anxiety differentially relate to fatigue, sleep disturbance, pain catastrophizing, fear of movement, and pain severity in women with fibromyalgia. Baseline data from the Fibromyalgia Activity Study with Transcutaneous Electrical Nerve Stimulation were analyzed. Of 191 participants, 50 percent reported high anxiety and/or depression (17% high anxiety, 9% high depression, and 24% both). Fatigue and sleep impairment were associated with high depression (p < 0.05). Pain severity, pain catastrophizing, and fear of movement were associated with high anxiety and high depression (p < 0.05). Possible implications for underlying mechanisms and the need for targeted treatments are discussed.

Unravelling Fibromyalgia-Steps Toward Individualized Management

The heterogeneity of the clinical presentation and the pathophysiologic mechanisms associated with fibromyalgia (FM), and the modest results on average for any therapy, call for a more individualized management strategy. Individualized treatment can be on the basis of subgrouping of patients according to associated conditions (mental health problems, chronic overlapping pain conditions, other somatic diseases) or on disease severity. Categorizing FM as mild, moderate, or severe can be on the basis of clinical assessment (eg, degree of daily functioning) or on questionnaires. Shared decision-making regarding treatment options can be directed according to patient preferences, comorbidities, and availability in various health care settings. The European League Against Rheumatism guidelines recommend a tailored approach directed by FM key symptoms (pain, sleep disorders, fatigue, depression, disability), whereas the German guidelines recommend management tailored to disease severity, with mild disease not requiring any specific treatment, and more severe disease requiring multicomponent therapy (combination of drug treatment with aerobic exercise and psychological treatments). When indicated, treatments should follow a stepwise approach beginning with easily available therapies such as aerobic exercise and amitriptyline. Successful application of a tailored treatment approach that is informed by individual patient characteristics should improve outcome of FM.

PERSPECTIVE

This article presents suggestions for an individualized treatment strategy for FM patients on the basis of subgroups and disease severity. Categorizing FM as mild, moderate, or severe can be on the basis of clinical assessment (eg, degree of daily functioning) or questionnaires. Subgroups can be defined according to mental health and somatic comorbidities.

Exploring symptoms of somatization in chronic widespread pain: latent class analysis and the role of personality

Chronic widespread musculoskeletal pain (CWP) is a condition manifesting varied co-symptomatology and considerable heterogeneity in symptom profiles. This poses an obstacle for disease definition and effective treatment. Latent class analysis (LCA) provides an opportunity to find subtypes of cases in multivariate data. In this study, LCA was used to investigate whether and how individuals with CWP could be classified according to 12 additional somatic symptoms (migraine headaches, insomnia, stiffness, etc.). In a second step, the role of psychological and coping factors for the severity of these co-symptoms was investigated. Data were available for a total of N = 3,057 individuals (mean age = 56.6 years), with 15.4% suffering from CWP. In the latter group, LCA resulted in a three-class solution (n_group1 = 123; n_group2 = 306; n_group3 = 43) with groups differing in a graded fashion (i.e., severity) rather than qualitatively for somatic co-symptom endorsements. A consistent picture emerged, with individuals in the first group reporting the lowest scores and individuals in group 3 reporting the highest. Additionally, more co-symptomatology was associated with higher rates of anxiety sensitivity and depression, as well as more extraversion and emotional instability. No group differences for any of the coping strategies could be identified. The findings suggest that CWP has several detectable subtypes with distinct psychological correlates. The identification of CWP subgroups is important for understanding disease mechanisms and refining prognosis as well as stratifying patients in clinical trials and targeting specific treatment at the subgroups most likely to respond.

Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions: the OPPERA study

The classification of most chronic pain disorders gives emphasis to anatomical location of the pain to distinguish one disorder from the other (eg, back pain vs temporomandibular disorder [TMD]) or to define subtypes (eg, TMD myalgia vs arthralgia). However, anatomical criteria overlook etiology, potentially hampering treatment decisions. This study identified clusters of individuals using a comprehensive array of biopsychosocial measures. Data were collected from a case-control study of 1031 chronic TMD cases and 3247 TMD-free controls. Three subgroups were identified using supervised cluster analysis (referred to as the adaptive, pain-sensitive, and global symptoms clusters). Compared with the adaptive cluster, participants in the pain-sensitive cluster showed heightened sensitivity to experimental pain, and participants in the global symptoms cluster showed both greater pain sensitivity and greater psychological distress. Cluster membership was strongly associated with chronic TMD: 91.5% of TMD cases belonged to the pain-sensitive and global symptoms clusters, whereas 41.2% of controls belonged to the adaptive cluster. Temporomandibular disorder cases in the pain-sensitive and global symptoms clusters also showed greater pain intensity, jaw functional limitation, and more comorbid pain conditions. Similar results were obtained when the same methodology was applied to a smaller case-control study consisting of 199 chronic TMD cases and 201 TMD-free controls. During a median 3-year follow-up period of TMD-free individuals, participants in the global symptoms cluster had greater risk of developing first-onset TMD (hazard ratio = 2.8) compared with participants in the other 2 clusters. Cross-cohort predictive modeling was used to demonstrate the reliability of the clusters.

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

There is tremendous interpatient variability in the response to analgesic therapy (even for efficacious treatments), which can be the source of great frustration in clinical practice. This has led to calls for "precision medicine" or personalized pain therapeutics (ie, empirically based algorithms that determine the optimal treatments, or treatment combinations, for individual patients) that would presumably improve both the clinical care of patients with pain and the success rates for putative analgesic drugs in phase 2 and 3 clinical trials. However, before implementing this approach, the characteristics of individual patients or subgroups of patients that increase or decrease the response to a specific treatment need to be identified. The challenge is to identify the measurable phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics. In this article, we present evidence on the most promising of these phenotypic characteristics for use in future research, including psychosocial factors, symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge. We provide evidence-based recommendations for core phenotyping domains and recommend measures of each domain.

Fibromyalgia: A Critical and Comprehensive Review

Fibromyalgia is a disorder that is part of a spectrum of syndromes that lack precise classification. It is often considered as part of the global overview of functional somatic syndromes that are otherwise medically unexplained or part of a somatization disorder. Patients with fibromyalgia share symptoms with other functional somatic problems, including issues of myalgias, arthralgias, fatigue and sleep disturbances. Indeed, there is often diagnostic and classification overlap for the case definitions of a variety of somatization disorders. Fibromyalgia, however, is a critically important syndrome for physicians and scientists to be aware of. Patients should be taken very seriously and provided optimal care. Although inflammatory, infectious, and autoimmune disorders have all been ascribed to be etiological events in the development of fibromyalgia, there is very little data to support such a thesis. Many of these disorders are associated with depression and anxiety and may even be part of what has been sometimes called affected spectrum disorders. There is no evidence that physical trauma, i.e., automobile accidents, is associated with the development or exacerbation of fibromyalgia. Treatment should be placed on education, patient support, physical therapy, nutrition, and exercise, including the use of drugs that are approved for the treatment of fibromyalgia. Treatment should not include opiates and patients should not become poly pharmacies in which the treatment itself can lead to significant morbidities. Patients with fibromyalgia are living and not dying of this disorder and positive outlooks and family support are key elements in the management of patients.

Resistance exercise improves physical fatigue in women with fibromyalgia: a randomized controlled trial

Background

Fibromyalgia (FM) affects approximately 1–3 % of the general population. Fatigue limits the work ability and social life of patients with FM. A few studies of physical exercise have included measures of fatigue in FM, indicating that exercise can decrease fatigue levels. There is limited knowledge about the effects of resistance exercise on multiple dimensions of fatigue in FM. The present study is a sub-study of a multicenter randomized controlled trial in women with FM. The purpose of the present sub-study was to examine the effects of a person-centered progressive resistance exercise program on multiple dimensions of fatigue in women with FM, and to investigate predictors of the potential change in fatigue.

Methods

A total of 130 women with FM (age 22–64 years) were included in this assessor-blinded randomized controlled multicenter trial examining the effects of person-centered progressive resistance exercise compared with an active control group. The intervention was performed twice a week for 15 weeks. Outcomes were five dimensions of fatigue measured with the Multidimensional Fatigue Inventory (MFI-20). Information about background was collected and the women also completed several health-related questionnaires. Multiple linear stepwise regression was used to analyze predictors of change in fatigue in the total population.

Results

A higher improvement was found at the post-treatment examination for change in the resistance exercise group, as compared to change in the active control group in the MFI-20 subscale of physical fatigue (resistance group Δ –1.7, SD 4.3, controls Δ 0.0, SD 2.7, p = 0.013), with an effect size of 0.33. Sleep efficiency was the strongest predictor of change in the MFI-20 subscale general fatigue (beta = −0.54, p = 0.031, R² = 0.05). Participating in resistance exercise (beta = 1.90, p = 0.010) and working fewer hours per week (beta = 0.84, p = 0.005) were independent significant predictors of change in physical fatigue (R² = 0.14).

Conclusions

Person-centered progressive resistance exercise improved physical fatigue in women with FM when compared to an active control group.

Trial registration

ClinicalTrials.gov NCT01226784. Registered 21 October 2010.

Using a biopsychosocial perspective in the treatment of fibromyalgia patients

Fibromyalgia (FM) is a complex illness that manifests in different ways across individuals. Given that there are currently no known cures for FM, like treatment for other chronic diseases, interventions focus on learning strategies to alleviate symptom severity, to cope with and manage residual symptoms of the illness and to maximize health-related quality of life despite symptoms. In this article, we highlight the need for providers to adopt a biopsychosocial perspective for understanding and addressing patients with FM, noting that biological, psychosocial and behavioral factors function interdependently to affect a person's experience and adaptation. A cognitive-behavioral approach, which incorporates a biopsychosocial perspective, is detailed, along with specific treatment considerations for helping patients with FM manage their symptoms.