Short summary

Osteoporosis (brittle bones) is a systemic skeletal disease characterized by low bone mass and bone structure deterioration leading to increased fracture risk. Prediction models are used by clinicians treating osteoporosis to identify and target patients at high risk of fractures for drug treatment. Although such models exist for the prediction of long-term (10-year) fracture risk, there is a pool of patients with a high short-term risk of fracture/s, who are probably missed by the existing tools: existing data suggest that 1 in 4 re-fractures occur in the first year after a first fracture.

Rationale

Existing fracture risk prediction models estimate long-term fracture risk, likely underestimating imminent risks in specific population subgroups such as those with a recent fracture (on or off treatment) or those stopping anti-osteoporosis therapy. We aim to characterize cohorts of patients potentially at high imminent fracture risk, to study their observed short-term fracture rates, and to assess the performance of existing prediction tools (QFracture) to identify those at high risk of fracturing in 1- to 2-year follow-up. Authors have criticized the definition of fracture risk based on BMD measurements, and a number of fracture risk prediction tools have been developed for the identification of patients at high risk of fracturing. Commonly used prediction tools estimate 5 or 10-year absolute fracture risk, and include the Fracture risk assessment tool (FRAX), QFracturw or the Garvan nomogram all developed between 2008 and 2012. Researchers have advocated the need for local validation of such tools before they can be used in clinical practice in this is lacking for the majority of algorithms as regards Denmark. In addition, all of these fracture risk assessment tools like FRAX or QFracture estimate long- (rather than short-) term risk over 10 year periods. While such estimates may help identify women for osteoporosis treatment in general, short-term risk prediction may be relevant to target patients who may benefit from primary care-based interventions to improve adherence or who may be best candidates for alternative therapies.

Description of the cohort

The study includes all subjects with a record in the DNHDR (Landspatientregistret) with a diagnosis of osteoporosis, with a record in DNHDR with a fracture that could be due to osteoporosis and/or has filled prescriptions in the NPR for medications against osteoporosis or been treated in hospital with infusions or injections for osteoporosis.

Data and biological material

Year of birth, gender, migration

Contact dates, diagnoses and treatment codes from the hospital discharge reg.

Prescriptions filled

Death dates and causes of death.

Collaborating researchers and departments

OPEN Odense Patient data Explorative Network, Odense University Hospital

• Professor and Consultant Endocrinologist Bo Abrahamsen, PhD
• Associate professor Katrine Hass Rubin

Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK

• Associate Professor Daniel Prieto-Alhambra