title: Data driven attempt to create a clinical algorithm for identification of women with rheumatoid arthritis at high risk of osteoporosis.
abstract: OBJECTIVE
  To examine relations between osteoporosis and low bone mass and demographic and clinical variables in patients with rheumatoid arthritis (RA), in an attempt to develop a data driven clinical tool for identification of patients at high risk of osteoporosis.
  
  
  METHODS
  All patients were recruited from a county based register and were examined cross sectionally with a variety of clinical and health status measures as well as bone density measures (anteroposterior spine L2-4, total hip, and femoral neck). Associations between osteoporosis (T score < or = -2.5SD) and low bone mass (T score < or = -1SD), on the one hand, and demographic and clinical measures, on the other, were examined bivariately and by logistic regression analyses.
  
  
  RESULTS
  394 patients with a mean age of 54.8 years were examined. The percentages having osteoporosis/low bone mass were 16.8/45.8, 14.7/54.5 and 14.7/55.5 in spine L2-4, total hip, and femoral neck, respectively. Osteoporosis and low bone mass were bivariately related to age, body mass index (BMI), disease duration, disease process measures, presence of deformed joints, physical disability, current use of corticosteroids, and history of non-vertebral fracture. In multivariate analyses, age >60 years, low BMI, and current use of corticosteroids were consistently related to osteoporosis and to low bone mass at all sites. The presence of deformed joints was associated with osteoporosis at the total hip, and a history of previous non-vertebral fracture with osteoporosis at the femoral neck. The Modified Health Assessment Questionnaire (MHAQ) > or = 1.5 and non-vertebral fracture were also independently associated with low bone mass at the hip. The logistic regression analyses models could, however, only predict osteoporosis with a sensitivity of about 50-60% and a specificity of 80-90% at the various measurement sites, and low bone mass with a sensitivity and specificity of about 70%.
  
  
  CONCLUSIONS
  Consideration of demographic and disease markers may be of some help in predicting presence of osteoporosis or low bone mass, but a combination of markers cannot be used as a clinical tool with sufficient sensitivity and specificity for the identification of osteoporosis or low bone mass in patients with RA.
authors.affiliation: Oslo City Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway. t.k.kvien@ioks.uio.no
authors.forename: T K
authors.initials: TK
authors.lastname: Kvien
authors.forename: G
authors.initials: G
authors.lastname: Haugeberg
authors.forename: T
authors.initials: T
authors.lastname: Uhlig
authors.forename: J A
authors.initials: JA
authors.lastname: Falch
authors.forename: J I
authors.initials: JI
authors.lastname: Halse
authors.forename: W F
authors.initials: WF
authors.lastname: Lems
authors.forename: B A
authors.initials: BA
authors.lastname: Dijkmans
authors.forename: A D
authors.initials: AD
authors.lastname: Woolf
mesh_terms: D000203:Activities of Daily Living; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000150:complications* / Q000188:drug therapy / Q000473:pathology; D015992:Body Mass Index; D015519:Bone Density; D003430:Cross-Sectional Studies; D005260:Female; D005938:Glucocorticoids / Q000627:therapeutic use; D006801:Humans; D008875:Middle Aged; D010024:Osteoporosis / Q000175:diagnosis* / Q000209:etiology; D011237:Predictive Value of Tests; D012042:Registries; D012044:Regression Analysis; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index
chemical_list: D005938:Glucocorticoids