title: Performance of risk indices for identifying low bone density in postmenopausal women.
abstract: OBJECTIVE
  To examine the ability of 4 published osteoporosis risk indices to identify women with low bone density.
  
  
  METHODS
  Subjects included postmenopausal women 45 years and older consecutively recruited from US clinics, women from general practice centers in The Netherlands (age range, 50-80 years), women in the Rotterdam Study (The Netherlands) 55 years and older, and women aged 55 to 81 years old screened for a clinical trial of alendronate. Bone mineral density (BMD) was measured at the femoral neck or lumbar spine; T scores represent the number of SDs below the mean for young healthy women. One risk index was calculated from age and weight; the other risk indices included up to 4 additional variables obtained by questionnaire. We calculated the sensitivity and specificity for identifying women with BMD T scores of -2.5 or less or -2.0 or less in the US clinic sample and created 3 risk categories, using each of the 4 indices.
  
  
  RESULTS
  Data were available for 1102 women from the US clinic sample, 3374 women in the Rotterdam Study, 23,833 women screened for a clinical trial of alendronate, and 4204 women from general practice centers in The Netherlands. Specificity for identifying BMD T scores of -2.5 or less ranged from 37% to 58% (depending on risk index) when sensitivity was approximately 90%. The prevalence of osteoporosis (defined as T scores < or = -2.5) differed widely across the 3 risk categories, ranging from 2% to 4% for the low-risk category to 47% to 61% for the high-risk category in the US clinic sample. For spine BMD in the US clinic sample, the prevalence of T scores of -2.5 or less ranged from 7% (low risk) to 38% (high risk). The large differences in prevalence across risk categories were consistent across the other 3 samples of postmenopausal women in the United States and The Netherlands for all 4 risk indices.
  
  
  CONCLUSIONS
  We recommend measuring BMD in women who are classified as having an increased risk of osteoporosis by using any of these risk indices because all 4 indices appear to predict low bone mass equally well. The Osteoporosis Self-assessment Tool index is easiest to calculate and therefore may be most useful in clinical practice.
authors.affiliation: Biomedical Research Institute, Limburg University, Diepenbeek, Belgium.
authors.forename: Piet
authors.initials: P
authors.lastname: Geusens
authors.forename: Marc C
authors.initials: MC
authors.lastname: Hochberg
authors.forename: Danny J M
authors.initials: DJ
authors.lastname: van der Voort
authors.forename: Huibert
authors.initials: H
authors.lastname: Pols
authors.forename: Marjolein
authors.initials: M
authors.lastname: van der Klift
authors.forename: Ethel
authors.initials: E
authors.lastname: Siris
authors.forename: Mary E
authors.initials: ME
authors.lastname: Melton
authors.forename: Jennifer
authors.initials: J
authors.lastname: Turpin
authors.forename: Christine
authors.initials: C
authors.lastname: Byrnes
authors.forename: Philip
authors.initials: P
authors.lastname: Ross
mesh_terms: D000368:Aged; D015519:Bone Density; D005260:Female; D005272:Femur Neck; D006801:Humans; D008403:Mass Screening; D008875:Middle Aged; D009426:Netherlands; D015663:Osteoporosis, Postmenopausal / Q000175:diagnosis* / Q000517:prevention & control*; D011237:Predictive Value of Tests; D015995:Prevalence; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014481:United States