Aalborg Universitet Does bone mineral density improve the predictive accuracy of fracture risk assessment ?

Objective To evaluate the added predictive accuracy of bone mineral density (BMD) to fracture risk assessment. Design Prospective cohort study using data between 01 January 2010 and 31 December 2012. setting North Denmark Osteoporosis Clinic of referred patients presenting with at least one fracture risk factor to the referring doctor. Participants Patients aged 40–90 years; had BMD T-score recorded at the hip and not taking osteoporotic preventing drugs for more than 1 year prior to baseline. Main outcome measures Incident diagnoses of osteoporotic fractures (hip, spine, forearm, humerus and pelvis) were identified using the National Patient Registry of Denmark during 01 January 2012–01 January 2014. Cox regression was used to develop a fracture model based on predictors in the Fracture Risk Assessment Tool (FRAX®), with and without, binary and continuous BMD. Change in Harrell’s C-Index and Reclassification tables were used to describe the added statistical value of BMD. results Adjusting for predictors included in FRAX®, patients with osteoporosis (T-score ≤−2.5) had 75% higher hazard of a fracture compared with patients with higher BMD (HR: 1.75 (95% CI 1.28 to 2.38)). Forty per cent lower hazard was found per unit increase in continuous BMD T-score (HR: 0.60 (95% CI 0.52 to 0.69)). Accuracy improved marginally, and Harrell’s C-Index increased by 1.2% when adding continuous BMD (0.76 to 0.77). Reclassification tables showed continuous BMD shifted 529 patients into different risk categories; 292 of these were reclassified correctly (57%; 95% CI 55% to 64%). Adding binary BMD however no improvement: Harrell’s C-Index decreased by 0.6%. Conclusions Continuous BMD marginally improves fracture risk assessment. Importantly, this was only found when using continuous BMD measurement for osteoporosis. It is suggested that future focus should be on evaluation of this risk factor using routinely collected data and on the development of more clinically relevant methodology to assess the added value of a new risk factor. IntrODuCtIOn Osteoporosis causes over 8.9 million fractures worldwide, of which over 4.5 million occur in the USA and Europe and account for 2.8 million disability adjusted life years. Further, 1.2 million disability adjusted life years are accounted for by hip fractures, which are projected to increase to 6 million by 2050. Given this burden, and treatment options for osteoporosis, identifying patients at risk of an osteoporotic fracture is high priority among health policy-makers to reduce the risk of future fracture. Risk prediction tools have been developed to aid in the identification of patients at risk. For example, the Fracture Risk Assessment Tool (FRAX) and QFracture are commonly used to assess fracture risk in patients based on predefined risk factors. Bone mineral density (BMD), a measurement used to aid diagnosis of osteoporosis, has also been identified as a fracture risk factor. Unlike some other fracture risk factors, treatment options (eg, bisphosphonate medication) are available that reduces the fracture risk markedly when treatment is initiated based on low BMD. strengths and limitations of this study ► Addresses a research question recommended by The National Institute for Health and Care Excellence to investigate the added value of bone mineral density (BMD) to fracture risk prediction. ► Investigates BMD in both the commonly used, binary and continuous format. ► Presents changes in calibration, discrimination and reclassification to describe the added value of BMD. ► Uses robustly collected data from Northern Denmark, with 3.2% missing data. ► As data are from a North Danish population, with at least one fracture risk factor, this limits generalisability of the results. on 10 S etem er 2018 by gest. P rocted by coright. ht://bm jon.bm jcom / B M J O pen: frst pulished as 10136/bm jopen17-018898 on 12 A ril 218. D ow nladed fom