An enhanced Fracture Risk Evaluation Model (FREM) using national health data on morbidity and medications

Area of Science:

• Gerontology
• Epidemiology
• Health Informatics

Background:

• Osteoporosis poses a significant health risk to older adults, often leading to fractures and disability.
• Current diagnosis methods frequently identify osteoporosis late, typically after a fracture has occurred.
• Effective case-finding strategies are crucial for early intervention and preventing fractures.

Purpose of the Study:

• To extend the Fracture Risk Evaluation Model (FREM) for improved prediction of major osteoporotic fractures (MOF) and hip fractures (HF).
• To incorporate morbidity and medication data into the FREM for enhanced risk assessment.
• To evaluate age-specific risk cut-offs for MOF and HF prediction.

Main Methods:

• Utilized administrative health data from the entire Danish population aged ≥45 years (N=2,493,180).
• Developed FREMVer2 using logistic regression with LASSO regularization on diagnoses and prescriptions from 2007-2021.
• Validated the model across sex- and age-stratified subgroups.

Main Results:

• FREMVer2 achieved acceptable Area Under Curves (AUCs) for MOF (0.656-0.714) and HF (0.728-0.764).
• The model demonstrated high sensitivity (around 80% or higher) across most subgroups.
• Predictors used are readily available in general practitioner electronic health records in Denmark.

Conclusions:

• FREMVer2 is a feasible decision support system for opportunistic osteoporosis screening using administrative data.
• The enhanced model can aid in identifying individuals at high risk for osteoporotic fractures.
• This approach supports timely risk assessment and treatment initiation in healthcare settings.