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The measurement of fibromyalgia severity: converting scores between the FIQR, the PSD and the FASmod

1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
Collaborator/s: C. Gioia1, M. Giovale2, M. Cirillo3, F. Nacci4, D. Santilli5, S. Barbagli6, A. Capacci7, G. Cavalli8, S. Bonazza9, L. Navarini10, S. Paolino11, V. Giorgi12

 

  1. Rheumatology Clinic, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, Jesi, Ancona, Italy.
  2. Rheumatology Clinic, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, Jesi, Ancona, Italy. dica.marco@yahoo.it
  3. Rheumatology Clinic, Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, Jesi, Ancona, Italy.
  4. Department of Clinical Internal Medicine, Anaesthesiology and Cardiovascular Sciences, Rheumatology Unit, Policlinico Umberto I, Sapienza University of Rome, Italy.
  5. Rheumatology Unit, AOU Pisana, Pisa, Italy.
  6. Department of Medical Specialties, Division of Rheumatology Asl 3, Genova, Italy.
  7. Rheumatology Unit, Department of Precision Medicine, University of Campania L. Vanvitelli, Naples, Italy.
  8. Rheumatology Unit, Department of Internal Medicine, University of Messina, Italy.
  9. Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence, AOU Careggi, Florence, Italy.
  10. Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties, University of Palermo, Italy.
  11. Unit of Rheumatology and Clinical Immunology, ASST Spedali Civili, Brescia, Italy.
  12. Department of Medical Sciences, University of Trieste, UCO Medicina Clinica (SSD Reumatologia), Trieste, Italy.
  13. Internal Medicine and Rheumatology Unit, Azienda Ospedaliero-Universitaria di Parma, Italy.
  14. Rheumatology Unit, Department of Medical Sciences, Azienda Ospedaliero-Universitaria Senese and University of Siena, Italy.
  15. UOC Reumatologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
  16. Unit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR), IRCCS San Raffaele Scientific Institute & Vita-Salute San Raffaele University, Milan, Italy.
  17. Rheumatology Unit, Department of Medical Sciences, University of Ferrara and Azienda Ospedaliera-Universitaria di Ferrara, Italy.
  18. Unit of Allergology, Clinical Immunology and Rheumatology, Università Campus Bio-Medico di Roma, Italy.
  19. Rheumatology Unit, Department of Medicine and Surgery, University of Perugia, Italy.
  20. Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari, Italy.
  21. Laboratory of Experimental Rheumatology and Division of Clinical Rheumatology, Department of Internal Medicine DiMI, University of Genova, IRCCS San Martino Polyclinic, Genova, Italy.
  22. National Data Bank for Rheumatic Diseases Wichita, Kansas, USA.
  23. Rheumatology Department, IRCCS Galeazzi-Sant’Ambrogio Hospital, Milan, Italy.

  1. Department of Clinical Internal Medicine, Anaesthesiology and Cardiovascular Sciences, Rheumatology Unit, Policlinico Umberto I, Sapienza University of Rome, Italy.
  2. Department of Medical Specialties, Division of Rheumatology Asl 3, Genova, Italy.
  3. Rheumatology Unit, Department of Internal Medicine, University of Messina, Italy.
  4. Department of Experimental and Clinical Medicine, Division of Rheumatology, University of Florence, AOU Careggi, Florence, Italy.
  5. Internal Medicine and Rheumatology Unit, Azienda Ospedaliero-Universitaria di Parma, Italy.
  6. Rheumatology Unit, Department of Medical Sciences, Azienda Ospedaliero-Universitaria Senese and University of Siena, Italy.
  7. UOC Reumatologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
  8. Unit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR), IRCCS San Raffaele Scientific Institute & Vita-Salute San Raffaele University, Milan, Italy.
  9. Rheumatology, Department of Medical Sciences, University of Ferrara and Azienda Ospedaliera-Universitaria di Ferrara, Italy.
  10. Unit of Allergology, Clinical Immunology and Rheumatology, Università Campus Bio-Medico di Roma, Italy.
  11. Laboratory of Experimental Rheumatology and division of Clinical Rheumatology, Department of Internal Medicine DiMI, University of Genova, IRCCS San Martino Polyclinic, Genova, Italy.
  12. Rheumatology Unit, Internal Medicine Department, ASST Fatebenefratelli-Sacco, Milan State University School of Medicine, Milan, Italy.

on behalf of the Società Italiana di Reumatologia (SIR)

CER15710
2023 Vol.41, N°6
PI 1225, PF 1229
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PMID: 36067219 [PubMed]

Received: 25/03/2022
Accepted : 24/06/2022
In Press: 06/09/2022
Published: 28/06/2023

Abstract

OBJECTIVES:
The revised Fibromyalgia Impact Questionnaire (FIQR) is a widely used fibromyalgia severity assessment tool that was introduced in 2009 prior to the publication of the American College of Rheumatology (ACR) preliminary fibromyalgia criteria in 2010 and its revision in 2016. In 2020, the modified Fibromyalgia Assessment Scale (FASmod) was published. The Polysymptomatic Distress scale (PSD) of the fibromyalgia criteria and FASmod include assessments of pain location severity and can be used for diagnosis as well as in non-fibromyalgia patients. The aim of this study is to provide equations for the conversion of the FIQR scores to PSD and FASmod as an aid to understanding and sharing fibromyalgia severity information.
METHODS:
3089 patients with fibromyalgia, diagnosed according to the ACR 2010/2011 criteria and belonging to the Italian Fibromyalgia Registry completed FIQR, FASmod and PSD questionnaires. Pearson’s correlation coefficient was used to test the correlations between indices. The least square regression approach was used to produce predictive equations for each scale based on the remaining scales.
RESULTS:
FIQR was correlated with PSD (r=0.714) and FASmod (r=0.801); PSD and FASmod showed the highest correlation (r=0.897), expected since they assess the same constructs. Predictive equations showing a linear model were effective in producing mean cohort values, but individual predictions deviated substantially, precluding prediction in the individual patient.
CONCLUSIONS:
Conversion equations that allow for interconversion of multiple scales fibromyalgia severity assessment scales are produced. These can be useful in obtaining mean values for cohorts but are not accurate enough for use in individual patients.

DOI: https://doi.org/10.55563/clinexprheumatol/31gsnd

Rheumatology Article