title: Construction of an algorithm for quick detection of patients with low bone mineral density and its applicability in daily general practice.
abstract: OBJECTIVE
  To construct a quick algorithm to detect patients with low bone mineral density (BMD) and osteoporosis and determine its applicability in daily general practice.
  
  
  METHODS
  Cross-sectional study in all 9107 postmenopausal women, aged 50-80, registered at 12 general practice centers.
  
  
  METHODS
  All healthy women (5303) and 25% of the remaining group (943/3804) were invited to participate. Of 6246 invited women, 4725 (76%) participated. The women were questioned (state of health, medical history, family history, and food questionnaire) and examined [weight, height, body mass index (BMI), and BMD of the lumbar spine].
  
  
  METHODS
  Multivariable, stepwise backward and forward logistic regression analyses were performed, with BMD of the lumbar spine (L2-L4, cut-off points at 0.800 g/cm(2) for osteoporosis and 0.970 g/cm(2) for low BMD) as the dependent variable. An algorithm was constructed with those variables that correlated statistically significantly and clinically relevant with the presence of both osteoporosis and low BMD.
  
  
  RESULTS
  The prevalence of osteoporosis was 23%, that of low BMD was 65%. Only three variables (age, BMI, and fractures) were statistically significant and clinically relevant correlated with the presence of both osteoporosis and low BMD. Age (OR 2.70 for osteoporosis and OR 1.77 for low BMD) and fractures during the past five years (OR 3.60 for osteoporosis and OR 2.85 for low BMD) were found to be the key predictors. From the algorithm the absolute risks varied from 9% to 51% for osteoporosis and from 48% to 84% for low BMD. The corresponding relative risks varied from 1.0 to 5.7 and from 1.0 to 1.8.
  
  
  CONCLUSIONS
  Using an algorithm with age, BMI, and fracture history subgroups at high risk could be identified. However, in whatever combination, many women with osteoporosis could not be identified. Despite the differences in methods, we found predictors for osteoporosis which were comparable with the results of other cross-sectional studies, meaning that the first selection of patients at high risk for low BMD can be done adequately by both specialists and general practitioners.
authors: Department of General Practice, University of Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
authors: D J
authors: DJ
authors: van der Voort
authors: G J
authors: GJ
authors: Dinant
authors: P E
authors: PE
authors: Rinkens
authors: C J
authors: CJ
authors: van der Voort-Duindam
authors: J W
authors: JW
authors: van Wersch
authors: P P
authors: PP
authors: Geusens
mesh_terms: D000368:Aged; D000465:Algorithms; D015519:Bone Density; D003430:Cross-Sectional Studies; D005194:Family Practice; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D010024:Osteoporosis / Q000175:diagnosis*