SEARCH

SEARCH BY CITATION

Keywords:

  • Fibromyalgia;
  • Pain;
  • Treatment Outcome

Abstract

Objective

The objective of this study was to evaluate the ability of artificial neural networks (ANNs) to predict, on the basis of clinical variables, the response of persons with fibromyalgia syndrome (FMS) to a standard, 4-week interdisciplinary pain program.

Design

The design of this study is retrospective longitudinal.

Setting

Fibromyalgia outpatient clinic in a tertiary-care general hospital.

Subjects

The subjects of this study include outpatients with FMS.

Intervention

Multidisciplinary pain program including pain pharmacotherapy, cognitive-behavioral therapy, physical therapy, and occupational therapy.

Outcome Measures

Reliable change (RC) of scores on the Stanford Health Assessment Questionnaire (HAQ), and accuracy of ANNs in predicting RC at discharge or at 6-month follow-up as compared to Logistic Regression.

Results

ANN-based models using the sensory-discriminative and affective-motivational subscales of the McGill Pain Questionnaire, the HAQ disability index, and the anxiety subscale of Hospital Anxiety and Depression Scale at baseline as input variables correctly classified 81.81% of responders at discharge and 83.33% of responders at 6-month follow-up, as well as 100% of nonresponders at either evaluation time-point. Logistic regression analysis, which was used for comparison, could predict treatment outcome with accuracies of 86.11% and 61.11% at discharge and follow-up, respectively, based on baseline scores on the HAQ and the mental summary component of the Medical Outcomes Study—Short Form 36.

Conclusions

Properly trained ANNs can be a useful tool for optimal treatment selection at an early stage after diagnosis, thus contributing to minimize the lag until symptom amelioration and improving tertiary prevention in patients with FMS.