title: An artificial neural network approach for predicting functional outcome in fibromyalgia syndrome after multidisciplinary pain program.
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.
  
  
  METHODS
  The design of this study is retrospective longitudinal.
  
  
  METHODS
  Fibromyalgia outpatient clinic in a tertiary-care general hospital.
  
  
  METHODS
  The subjects of this study include outpatients with FMS.
  
  
  METHODS
  Multidisciplinary pain program including pain pharmacotherapy, cognitive-behavioral therapy, physical therapy, and occupational therapy.
  
  
  METHODS
  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.
authors.affiliation: Department of Neurosciences, School of Medicine and Dentistry, University of the Basque Country, Leioa, Spain.
authors.forename: Monika
authors.initials: M
authors.lastname: Salgueiro
authors.forename: Xabier
authors.initials: X
authors.lastname: Basogain
authors.forename: Antonio
authors.initials: A
authors.lastname: Collado
authors.forename: Xavier
authors.initials: X
authors.lastname: Torres
authors.forename: Juan
authors.initials: J
authors.lastname: Bilbao
authors.forename: Francisco
authors.initials: F
authors.lastname: Doñate
authors.forename: Luciano
authors.initials: L
authors.lastname: Aguilera
authors.forename: Jon Jatsu
authors.initials: JJ
authors.lastname: Azkue
mesh_terms: D000328:Adult; D015928:Cognitive Behavioral Therapy; D005260:Female; D005356:Fibromyalgia / Q000523:psychology* / Q000628:therapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009788:Occupational Therapy; D010146:Pain / Q000188:drug therapy; D026741:Physical Therapy Modalities; D020127:Recovery of Function; D016896:Treatment Outcome; D055815:Young Adult
keywords: Fibromyalgia; Pain; Treatment Outcome