An enhanced Fracture Risk Evaluation Model (FREM) using national health data on morbidity and medications
Authors' conclusion
This performance, together with the available predictors, makes FREMver2 a feasible decision support system as a step toward an opportunistic screening program in health care settings with access to administrative data.
Study context
- Population
- Entire population of Denmark aged ≥45 yr at baseline (2022), not previously diagnosed with osteoporosis or receiving osteoporosis treatment; stratified by sex and age (<65 yr, ≥65 yr)4 sex/age subgroups
- Participants
- 2,493,180 participants
- Outcome
- Prediction of imminent major osteoporotic fracture (MOF) and HF
- Follow-up
- 1-yr
Methodological detailsnationwide population-based, 60% development, 20% model validation, 20% cut-off validation split, logistic regression with LASSO regularization, Predictors: all Danish hospital diagnoses and filled pharmacy prescriptions 2007–2021
- Nationwide population-based
- 60% development, 20% model validation, 20% cut-off validation split
- Logistic regression with LASSO regularization
- Predictors: all Danish hospital diagnoses and filled pharmacy prescriptions 2007–2021
Performance
As reported in the abstract
Model discrimination (FREMVer2)
AUC for major osteoporotic fracture (MOF) across subgroups (poor to acceptable)
0.656-0.714
AUC for hip fracture (HF) across subgroups (acceptable)
0.728-0.764
Sensitivity in almost all subgroups
around 80% or higher
Original publication
An enhanced Fracture Risk Evaluation Model (FREM) using national health data on morbidity and medications. J Bone Miner Res. Published 31 October 2025. doi:10.
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