Characterizing classes of fibromyalgia within the continuum of central sensitization syndrome

Abstract

BackgroundWhile 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.MethodsFM 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.ResultsA 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.ConclusionFM 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.

Reliability, Factor Structure and Predictive Validity of the Widespread Pain Index and Symptom Severity Scales of the 2010 American College of Rheumatology Criteria of Fibromyalgia

“…While the etiology of FMS remains unknown, it is widely acknowledged that central pain sensitization and impairments in endogenous pain inhibitory mechanisms play a crucial role in its pathogenesis [ 2 , 3 ]. This is expressed in hyperalgesia and allodynia, which together characterize FMS; moreover, patients exhibit reduced thresholds and tolerance to evoked pain, increased responses during protocols measuring pain sensitization, and exaggerated activity in the neuromatrix of pain during painful stimulation [ 4 , 5 , 6 , 7 , 8 , 9 ].…”

“…(5) Estimation of associations between WPI and SS scores, evoked pain measures (pain threshold and tolerance) and a marker of central pain sensitization. The most well-established hypothesis regarding FMS pathophysiology pertains to central nervous sensitization to pain [2,4]. However, to date, no study has analyzed the association of WPI and SS scores with parameters reflecting this phenomenon.…”

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

“…Finally, it seems clear that complex and multidimensional classification problems could take advantage of machine learning techniques applied to clinical data for supporting clinical decisions [ 47 , 48 , 49 , 50 , 51 ]. In the context of pain and pain chronification, machine learning approaches have recently been applied to several pain syndromes [ 24 , 52 , 53 , 54 ], including fibromyalgia [ 55 , 56 , 57 ] and chronic lower back pain [ 58 , 59 , 60 , 61 ]. While traditional statistical analyses commonly make some a priori assumptions about the data model (e.g., normality) and about the relationships among variables (e.g., linearity), machine learning prioritizes a “distribution-free” context.…”

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

“…KNN models could be better used to identify disease patterns [49]. In Fibromyalgia, these have the capability to classify pain, clinical usage, and symptom severity [36]. In pulmonary diseases, Bayesian models produced low accuracy range in between 62.3% and 76.1%, because these are not recommended in high dimensional data sets.…”