Development and use of FRAX in osteoporosis. Osteoporos Int 21(Suppl 2):S407-S413

WHO Collaborating Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Beech Hill Road, Sheffield, S10 2RX, UK.
Osteoporosis International (Impact Factor: 4.17). 06/2010; 21 Suppl 2(S2):S407-13. DOI: 10.1007/s00198-010-1253-y
Source: PubMed


This paper reviews briefly the development and clinical use of FRAX in the development of assessment guidelines for osteoporosis.Fractures are the clinical consequence of osteoporosis and are a major cause of morbidity and mortality worldwide. Several treatments are available that have been shown to decrease the risk of fracture, but problems arise in identifying individuals at high fracture risk so that treatments can be effectively targeted. Case finding can be enhanced by the consideration of clinical risk factors that provide information on fracture risk over and above that provided by bone mineral density measurements. The FRAX tool integrates information on fracture risk from clinical risk factors with or without the use of BMD and can be used to improve the targeting of individuals at high fracture risk.

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    • "The FRAX algorithm aimed to provide an assessment for the prediction of 10-year risks of hip fracture and major osteoporotic fracture (hip, clinical spine, wrist, or proximal humerus fracture) in women and men, using clinical risk factors with or without femoral neck BMD [7] [8]. The clinical risk factors included age, gender, weight, height, a history of fragility fracture, parental hip fracture, current smoking, alcohol intake of ≥3 units daily, use of oral glucocorticoids, diagnosis of rheumatoid arthritis , and secondary osteoporosis [7] [28]. "
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    ABSTRACT: A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3,985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19 - 1.58) and 1.26 (95% CI: 1.12 - 1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18 months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that measures of frailty status may aid in fracture risk assessment and fracture prevention in the elderly. Further evidence from randomized controlled trials of osteoporosis medication interventions is needed to support the FI and FRAX as validated measures of fracture risk. Copyright © 2015. Published by Elsevier Inc.
    Bone 04/2015; 77. DOI:10.1016/j.bone.2015.04.028 · 3.97 Impact Factor
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    • "We have designed an expert system to assist in the diagnosis and treatment of osteoporosis. The software takes as input a set of clinically relevant parameters from which the 10-year fracture risk is computed based on published country specific data [8] [11] [18]. "
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    ABSTRACT: Expanding medical knowledge increases the potential risk of medical errors in clinical practice. We present, OPAD, a clinical decision support system in the field of the medical care of osteoporosis. We utilize clinical information from international guidelines and experts in the field of osteoporosis. Physicians are provided with user interface to insert standard patient data, from which OPAD provides instant diagnostic comments, 10-year risk of fragility fracture, treatment options for the given case, and when to offer a follow-up DXA-evaluation. Thus, the medical decision making is standardized according to the best expert knowledge at any given time. OPAD was evaluated in a set of 308 randomly selected individuals. OPAD’s ten-year fracture risk computation is nearly identical to FRAX (r = 0.988). In 58% of cases OPAD recommended DXA evaluation at the present time. Following a DXA measurement in all individuals, 71% of those that were recommended to have DXA at the present time received recommendation for further investigation or specific treatment by the OPAD. In only 5.9% of individuals in which DXA was not recommended, the result of the BMD measurement changed the recommendations given by OPAD.
    Computational and Mathematical Methods in Medicine 03/2015; 2015:1-7. DOI:10.1155/2015/189769 · 0.77 Impact Factor
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    • "Recently, a more effective strategy was implemented where continuous prediction was based on repeated assessments. This method has been extensively applied for evaluation of the risk of hip fracture, and for the time relationship between a hyperglycaemia indicator and subsequent diabetic retinopathy (Johansson et al., 2012; Kanis et al., 2010; Lind et al., 2010). The Fracture Risk Assessment Tool – a web-based algorithm that gives repeated estimates of the 10-year probability of major osteoporotic fractures – has achieved world-wide application (Watts, 2011). "
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    ABSTRACT: Prediction of the course of multiple sclerosis (MS) was traditionally based on features close to onset. To evaluate predictors of the individual risk of secondary progression (SP) identified at any time during relapsing-remitting MS. We analysed a database comprising an untreated MS incidence cohort (n=306) with five decades of follow-up. Data regarding predictors of all attacks (n=749) and demographics from patients (n=157) with at least one distinct second attack were included as covariates in a Poisson regression analysis with SP as outcome. The average hazard function of transition to SPMS was 0.046 events per patient year, showing a maximum at age 33. Three covariates were significant predictors: age, a descriptor of the most recent relapse, and the interaction between the descriptor and time since the relapse. A hazard function termed "prediction score" estimated the risk of SP as number of transition events per patient year (range <0.01 to >0.15). The insights gained from this study are that the risk of transition to SP varies over time in individual patients, that the risk of SP is linked to previous relapses, that predictors in the later stages of the course are more effective than the traditional onset predictors, and that the number of potential predictors can be reduced to a few (three in this study) essential items. This advanced simplification facilitates adaption of the "prediction score" to other (more recent, benign or treated) materials, and allows for compact web-based applications ( Copyright © 2014 Elsevier B.V. All rights reserved.
    Multiple Sclerosis and Related Disorders 09/2014; 3(5). DOI:10.1016/j.msard.2014.04.004 · 0.88 Impact Factor
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