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AB0943 Cluster Analysis of Symptom Characteristics in Patients with Fibromyalgia: Results From a Cross-Sectional Internet-Based Survey
  1. A. Draghessi1,
  2. F. Mozzani2,
  3. A. Corsi1,
  4. R. Catellani2,
  5. A. Ciapetti1,
  6. F. Salaffi1
  1. 1Rheumatology Department, Polytechnic University of Marche, Jesi (Ancona)
  2. 2Unit of Internal Medicine and Rheumatology, University Hospital, Parma, Italy

Abstract

Background Patients with fibromyalgia (FM) often present with varying combinations and degrees of severity of symptoms, which further complicates the ability to understand FM, obscures our understanding of the burden of FM in individual patients and complicates the development of appropriate comprehensive management strategies. Stratifying patients into more homogeneous subgroups may facilitate better understanding of FM.

Objectives To identify clusters within a heterogeneous sample of patients with FM using Web/Internet-based diary assessed with validated, self-report questionnaires.

Methods Symptom and physical data were available for patients responders to an Internet survey by using a simple clinical tools, such as the Fibromyalgia Impact Questionnaire (FIQ)(1), self-administered Fibromyalgia Activity Score (FAS)(2) and pain evaluated on the basis of the 16 non-articular sites listed on the Self-Assessment Pain Scale (SAPS) in a single measure. The Web portal allows authorized users to access the system via personal computer and Internet browser. Hierarchical agglomerative clustering was conducted to identify subgroups based on symptoms.

Results Out of 353 patients who completed the program, 85.3% were women, had a mean age of approximately 51 years old. Mean time since the onset of pain was 4.7 years, with a range of 1 to 18 years. The five highest scoring items (greater disease impact) were related to symptoms: sleep quality, fatigue/energy, pain, stiffness, tenderness level, balance problems and environmental sensitivity. A high proportion of respondents with FM reported experiencing pain in the neck (81.4%), upper back (70.1%), and lower back (83.2%). A three-cluster solution best fit the data and each clustering variable differed significantly (P<0.0001) among the three clusters. The three clusters divided the sample into severity levels: Cluster 1 (n° 117 patients) reflects the lowest average levels across all symptoms; Cluster 3 (n° 116 patients) reflects the highest average levels. Clusters 2 (n° 120 patients) capture moderate symptoms levels with lower levels of depression and anxiety than Cluster 3 (Figure).

Conclusions The results of this pilot study suggest that using the Web/Internet-based diary as patient terminal seems to provide a ubiquitous, easy-to-use, and cost efficient solution for patient-centered data acquisition in the management of FM. The data provides a snapshot of FM and help support the clinical impression that there are distinct subsets of patients with FM. Furthermore, the definition of clinically homogeneous subgroups need to be in different countries, contexts and patient samples.

References

  1. Salaffi F, Franchignoni F, Giordano A, et al. Psychometric characteristics of the Italian version of the revised Fibromyalgia Impact Questionnaire using classical test theory and Rasch analysis. Clinical and Experimental Rheumatology 2013.

  2. Salaffi F, Sarzi-Puttini P, Girolimetti R, et al. Development and validation of the self-administered Fibromyalgia Assessment Status: a disease-specific composite measure for evaluating treatment effect. Arthritis Research & Therapy; 11:R125 2009.

Disclosure of Interest None declared

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