AB1123 Poor assessment of risk of osteoporosis after a forearm fracture in women: a health insurance database study in the loire valley region (france)

E Cattelain-Lopez, D Chu Miow Lin, P Goupille, A Happe, E Lespessailles, F Jacquot, P Tauveron, E Rusch, E Oger, D Mulleman
2017 Abstracts Accepted for Publication   unpublished
calculated the incident rate of all fractures. After dividing the patients according to the use of GCs, we compared baseline characteristics and fracture-free survival between two groups. We compared accuracies of TBS, BMD, clinical risk factors for fracture and their combinations for predicting new fractures using areas under the receiver operator characteristic (ROC) curve (AUC). Results: A total of 14 fractures in 12 patients were occurred among 100 patients during follow-up (428.8
more » ... rs): 9 among the 44 in GC users and 5 in 56 GC non-users. Incidence of fracture was not different between two groups (log-rank test, p=0.27). AUC for incident fracture prediction of TBS alone [AUC 0.54, 95% confidence interval (CI) 0.35-0.72] was comparable with TBS combined with L-spine BMD (AUC 0.54, 95% CI 0.36-0.71) or with hip BMD (AUC 0.55, 95% CI 0.37-0.73). Accuracy for prediction of new fracture is increased when TBS was combined with age and history of previous fracture (AUC 0.74, 95% CI 0.62-0.85). In GC users, history of previous fracture alone (AUC 0.79, 95% CI 0.62-0.97) showed the best accuracy for predicting new fracture among TBS, BMD, clinical risk factors for fracture and their combinations. Conclusions: TBS combined with age and previous history of fracture showed the highest accuracy for predicting new fracture compared to TBS or BMD alone or their combinations in RA patients. In GC users, history of previous fracture alone showed the highest accuracy for predicting new fracture.
doi:10.1136/annrheumdis-2017-eular.3982 fatcat:rzhhisa77ndjrftxhifmtjyesq