A computer model identified four major classes of fibromyalgia on the basis of the pain and symptom severity felt by patients, the presence of specific coexisting conditions, and the use of clinical procedures, a study reports.
These findings support the idea that fibromyalgia is a spectrum of disease manifestations progressing over time, and underline the value of better characterizing this disease to provide improved and more individualized care to patients.
The study, “Characterizing classes of fibromyalgia within the continuum of central sensitization syndrome,” was published in the Journal of Pain Research.
Though chronic widespread pain and tenderness are accepted hallmarks of fibromyalgia, the disease has long been proposed as “a continuum of diseases rather than a single disease,” according to the authors of the study.
The significant variation in fibromyalgia symptoms from patient to patient makes it harder for doctors to diagnose and treat the disease.
One of the problems is the lack of a classification system for fibromyalgia that is based on a broad range of clinical and non-clinical variables. Recent studies have suggested that other factors beyond clinical manifestations are important to characterize the disease, including the number of healthcare resources needed by patients and how long they have been living with fibromyalgia.
Given that a better and broader understanding of this disease may improve its diagnosis and treatment, researchers aimed at providing “a first step toward systematically identifying and describing classes” of fibromyalgia.
To do this, they analyzed data from chronic pain patients with the goal of distinguishing and characterizing potential categories of fibromyalgia.
The study included data from 2,529 patients (76.3% female) who had 79,570 documented observations or clinical visits, taken from the ProCare Systems network of Michigan pain clinics.
Researchers used a computational model (clustering) to identify fibromyalgia “patterns,” or categories based on the following characteristics: presence of comorbidities, or coexisting conditions, as a measure of symptom severity; number of pain regions, as a measure of widespread pain; secondary diseases and conditions; and the type and number of healthcare interventions used in patients, as markers of treatment intensity. A survival analysis was also conducted to help clarify the classification procedures.
By weighting these factors, the model identified four main classes of fibromyalgia: class 1, regional fibromyalgia with classic symptoms (chronic widespread pain and joint tenderness); class 2, more generalized disease with increasing widespread pain and some additional symptoms; class 3 fibromyalgia with advanced and associated conditions, greater widespread pain compared with the prior classes, increased sleep disturbance, and chemical sensitivity (allergy to latex); and class 4, fibromyalgia secondary to other conditions, with the highest prevalence and severity of pain and other symptoms.
Most of the studied patients fell under class 1 (63%) or class 2 (23.7%), which reflected the major disease marks accepted for fibromyalgia.
However, the fact that two other classes were identified — class 3 (9.3% of patients) and class 4 (3% of patients) — adds to the concept of fibromyalgia as a spectrum of symptoms “and emphasizes the need for an individualized approach to diagnosis and treatment,” the researchers wrote.
In addition, they observed that some patients assigned to a class transitioned to the next class over time, which was interpreted as a sign of disease progression. In fact, the risk for worse outcomes was higher for patients in the later classes. In contrast, cases of reverse transition, where patients moved to a preceding class, were less frequent.
These observations also support the idea that fibromyalgia is “a condition of centralized pain, and that this pain becomes more centralized over the disease course.”
Researchers favor additional studies to deepen fibromyalgia characterization and better understand how patients progress through their disease. This information “may enhance diagnosis and help optimize treatment, potentially leading to improved patient outcomes and reductions in the health care system burden,” they said.
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