Prediction of imminent osteoporotic fracture risk in Danish postmenopausal women-can the addition of self-reported clinical risk factors improve the prediction of the register-based FREM algorithm?

UNLABELLED: Obtaining accurate self-reports on clinical risk factors, such as parental hip fracture or alcohol and tobacco use, limits the utility of conventional risk scores for fracture risk. We demonstrate that fracture-risk prediction based on administrative health data alone performs equally to prediction based on self-reported clinical risk factors.

BACKGROUND: Accurate assessment of fracture risk is crucial. Unlike established risk prediction tools that rely on patient recall, the Fracture Risk Evaluation Model (FREM) utilises register data to estimate the risk of major osteoporotic fracture (MOF). We investigated whether adding self-reported clinical risk factors for osteoporosis to the FREM algorithm improved the prediction of 1-year fracture risk by comparing three approaches: the FREM algorithm (FREMorig), clinical risk factors (CRFonly), and FREM combined with clinical risk factors (FREM-CRF).

METHOD: Clinical risk factor information was obtained through questionnaires sent to women aged 65-80 years living in the Region of Southern Denmark in 2010, who participated in the Risk-stratified Osteoporosis Strategy Evaluation study. Register data was obtained through national health registers and linked to the survey data. Positive and negative predictive values and concordance statistics were calculated for the performance of each approach using logistic regression and Cox proportional hazards models.

RESULTS: Of the 18,605 women included, 280 sustained a MOF within 1 year. All three approaches performed similarly in 1-year fracture risk prediction for low- and high-risk individuals. However, the FREMorig and FREM-CRF approach slightly overestimated fracture risk for medium-risk individuals.

CONCLUSION: Adding self-reported clinical data to FREM did not increase precision in predicting 1-year MOF risk. The discrimination of FREMorig was similar to that of CRFonly, suggesting it may be possible to estimate fracture risk with the same precision by using register data instead of self-reported risk information. Register-based prediction models may be applicable in individualised risk monitoring or large-scale osteoporosis screening programmes.