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

ER Christensen, Kasper Westphal Leth, Frederik Lykke Petersen, Tanja Gram Petersen, Sören Möller, Bo Abrahamsen, Katrine Hass Rubin*

*Corresponding author for this work

Research output: Contribution to conference without publisher/journalConference abstract for conferenceResearch

24 Downloads (Pure)

Abstract

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

Design and Methods: Clinical risk factor information was obtained through questionnaires sent to women aged 65-80 years living in the Region of Southern Denmark on February 1st, 2010, who were invited to participate in the Risk-stratified Osteoporosis Strategy Evaluation (ROSE) 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.

Primary variables: Wrist, humerus, vertebral and hip fractures, and risk factors for osteoporosis as defined by the Danish National Treatment Guideline.

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

Conclusion: Adding self-reported clinical data to the FREM algorithm did not increase precision in predicting one-year MOF risk. The discrimination of FREMorig was similar to that achieved using CRFonly, suggesting it may be possible to achieve the same precision in risk in fracture-risk prediction by using register data instead of relying on self-reported risk information.
Original languageEnglish
Publication date2024
Publication statusPublished - 2024
EventÅben Forskerdag 2024 - Hotel Comwell, Middelfart, Denmark
Duration: 8. Nov 20248. Nov 2024

Conference

ConferenceÅben Forskerdag 2024
LocationHotel Comwell
Country/TerritoryDenmark
CityMiddelfart
Period08/11/202408/11/2024

Cite this