Development and Validation of the ORACLE Score to Predict Risk of Osteoporosis

Richy, Florent; Deceulaer, Frederic; Ethgen, Olivier; Bruyere, Olivier; Reginster, Jean-Yves
November 2004
Mayo Clinic Proceedings;Nov2004, Vol. 79 Issue 11, p1402
Academic Journal
OBJECTIVE: To develop and validate a composite Index, the Osteoporosis Risk Assessment by Composite Linear Estimate (ORACLE), that Includes risk factors and ultrasonometric outcomes to screen for osteoporosis. SUBJECTS AND METHODS: Two cohorts of postmenopausal women aged 45 years and older participated in the development (n=407) and the validation (n=202) of ORACLE Their bone mineral density was determined by dual energy x-ray absorptiometry and quantitative ultrasonometry (QUS), and their historical and clinical risk factors were assessed (January to June 2003). Logistic regression analysis was used to select significant predictors of bone mineral density, whereas receiver operating characteristic (ROC) analysis was used to assess the discriminatory performance of ORACLE. RESULTS: The final logistic regression model retained 4 biometric or historical variables and 1 ultrasonometric outcome. The ROC areas under the curves (AUCs) for ORACLE were 84% for the prediction of osteoporosis and 78% for low bone mass. A sensitivity of 90% corresponded to a specificity of 50% for identification of women at risk of developing osteoporosis. The corresponding positive and negative predictive values were 86% and 54%, respectively, in the development cohort. in the validation cohort, the AUCs for identification of osteoporosis and low bone mass were 81% and 76% for ORACLE. 69% and 64% for QUS T score, 71% and 68% for QUS ultrasonometric bone profile index, and 76% and 75% for Osteoporosis Self-assessment Tool, respectively. ORACLE had the best discriminatory performance in identifying osteoporosis compared with the other approaches (P<.05). CONCLUSION: ORACLE exhibited the highest discriminatory properties compared with ultrasonography alone or other previously validated risk indices, it may be helpful to enhance the predictive value of QUS.


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