Quantification of the whole-body burden of radiographic osteoarthritis using factor analysis Public Deposited

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Creator
  • Schwartz, Todd
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics, School of Medicine, Thurston Arthritis Research Center
  • DeVellis, Robert F.
    • Affiliation: Gillings School of Global Public Health, School of Medicine, Thurston Arthritis Research Center, Department of Health Behavior
  • Conaghan, Philip G
    • Other Affiliation: Section of Musculoskeletal Disease, University of Leeds & NIHR Leeds Musculoskeletal Biomedical Research Unit, Chapel Allerton Hospital, Chapeltown Road, Leeds, LS7 4SA, UK
  • Nelson, Amanda E
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Kraus, Virginia B
    • Other Affiliation: Department of Medicine, Duke University Medical Center, 595 La Salle St, Durham, NC, 27710, USA
  • Jordan, Joanne
    • Affiliation: School of Medicine, Thurston Arthritis Research Center
  • Renner, Jordan B
    • Affiliation: School of Medicine, Department of Radiology, Thurston Arthritis Research Center
Abstract
  • Abstract Introduction Although osteoarthritis (OA) commonly involves multiple joints, no widely accepted method for quantifying whole-body OA burden exists. Therefore, our aim was to apply factor analytic methods to radiographic OA (rOA) grades across multiple joint sites, representing both presence and severity, to quantify the burden of rOA. Methods We used cross-sectional data from the Johnston County Osteoarthritis Project. The sample (n = 2092) had a mean age of 65 ± 11 years, body mass index (BMI) 31 ± 7 kg/m2, with 33% men and 34% African Americans. A single expert reader (intra-rater κ = 0.89) provided radiographic grades based on standard atlases for the hands (30 joints, including bilateral distal and proximal interphalangeal [IP], thumb IP, metacarpophalangeal [MCP] and carpometacarpal [CMC] joints), knees (patellofemoral and tibiofemoral, 4 joints), hips (2 joints), and spine (5 levels [L1/2 to L5/S1]). All grades were entered into an exploratory common factor analysis as continuous variables. Stratified factor analyses were used to look for differences by gender, race, age, and cohort subgroups. Results Four factors were identified as follows: IP/CMC factor (20 joints), MCP factor (8 joints), Knee factor (4 joints), Spine factor (5 levels). These factors had high internal consistency reliability (Cronbach's α range 0.80 to 0.95), were not collapsible into a single factor, and had moderate between-factor correlations (Pearson correlation coefficient r = 0.24 to 0.44). There were no major differences in factor structure when stratified by subgroup. Conclusions The 4 factors obtained in this analysis indicate that the variables contained within each factor share an underlying cause, but the 4 factors are distinct, suggesting that combining these joint sites into one overall measure is not appropriate. Using such factors to reflect multi-joint rOA in statistical models can reduce the number of variables needed and increase precision.
Date of publication
Identifier
  • doi:10.1186/ar3501
  • 22027269
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Amanda E Nelson et al.; licensee BioMed Central Ltd.
License
Journal title
  • Arthritis Research & Therapy
Journal volume
  • 13
Journal issue
  • 5
Page start
  • R176
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1478-6354
Bibliographic citation
  • Arthritis Research & Therapy. 2011 Oct 25;13(5):R176
Access
  • Open Access
Publisher
  • BioMed Central Ltd
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