title: A risk assessment tool (OsteoRisk) for identifying Latin American women with osteoporosis.
abstract: OBJECTIVE
  To develop a simple and easy-to-use tool for identifying osteoporotic women (femoral neck bone mineral density [BMD] T-scores<or=-2.5) in Latin America.
  
  
  METHODS
  Retrospective study involving review of medical records.
  
  
  METHODS
  Osteoporosis clinics in 6 Latin American countries.
  
  
  METHODS
  Postmenopausal women ages >or=50 in Latin America who had femoral neck BMD measurements.
  
  
  RESULTS
  A risk index was developed from 1,547 patients based on least square regression using age, weight, history of fractures, and other variables as predictors for BMD T-score. The final model was simplified by reducing the number of predictors; sensitivity and specificity were evaluated before and after reducing the number of predictors to assess performance of the index. The final model included age, weight, country, estrogen use, and history of fractures as significant predictors for T-score. The resulting scoring index achieved 91% sensitivity and 47% specificity. Simplifying the index by using only age and weight yielded similar performance (sensitivity, 92%; specificity, 45%). Three risk categories were identified based on OsteoRisk, the index using only age and body weight: high-risk patients (index <=-2; 65.6% were osteoporotic), moderate-risk patients (-2< index <=1; 26.7% were osteoporotic), and low-risk patients (index>1; 8% were osteoporotic). Similar results were seen in a validation sample of 279 women in Brazil.
  
  
  CONCLUSIONS
  Age and weight alone performed well for predicting the risk of osteoporosis among postmenopausal women. The OsteoRisk is an easy-to-use tool that effectively targets the vast majority of osteoporotic patients in Latin America for evaluation with BMD.
authors.affiliation: Merck & Company, Inc., Whitehouse Station, NJ 08999, USA. shuvayu_sen@merck.com <shuvayu_sen@merck.com>
authors.forename: Shuvayu S
authors.initials: SS
authors.lastname: Sen
authors.forename: Vincent P
authors.initials: VP
authors.lastname: Rives
authors.forename: Osvaldo D
authors.initials: OD
authors.lastname: Messina
authors.forename: Jorge
authors.initials: J
authors.lastname: Morales-Torres
authors.forename: Gregorio
authors.initials: G
authors.lastname: Riera
authors.forename: Juan M
authors.initials: JM
authors.lastname: Angulo-Solimano
authors.forename: João F M
authors.initials: JF
authors.lastname: Neto
authors.forename: Alberto
authors.initials: A
authors.lastname: Frisoli
authors.forename: Ricardo C
authors.initials: RC
authors.lastname: Sáenz
authors.forename: Olga
authors.initials: O
authors.lastname: Geling
authors.forename: Philip D
authors.initials: PD
authors.lastname: Ross
mesh_terms: D000368:Aged; D019540:Area Under Curve; D015519:Bone Density; D005260:Female; D005272:Femur Neck / Q000503:physiopathology; D006801:Humans; D007843:Latin America; D016018:Least-Squares Analysis; D008875:Middle Aged; D015999:Multivariate Analysis; D010024:Osteoporosis / Q000175:diagnosis* / Q000503:physiopathology; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D044465:White People