From relative risk to absolute fracture risk calculation: The FRAX algorithm

McCloskey, Eugène; Johansson, Helena; Odén, Anders; Kanis, John А.

doi:10.1007/s11914-009-0013-4

Cited by 73 publications

(43 citation statements)

References 53 publications

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“…Finally, patients with pituitary disorders were excluded from the study because TSH is not a reliable indicator of thyroid disorders in the presence of pituitary dysfunction. Fracture outcomes in the form of major osteoporotic fractures (22) were collected from the NHDR. November 30, 2012 marked the end of the study.…”

Section: Study Populationmentioning

confidence: 99%

The Excess Risk of Major Osteoporotic Fractures in Hypothyroidism Is Driven by Cumulative Hyperthyroid as Opposed to Hypothyroid Time: An Observational Register-Based Time-Resolved Cohort Analysis

Abrahamsen

Jørgensen

Laulund

et al. 2014

Journal of Bone and Mineral Research

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The long-term relationship between hypothyroidism and fracture risk is challenging to dissect because of the modifying influence of subsequent thyroxine replacement with the potential for excessive replacement doses. We studied changes in serum thyrotropin concentration (TSH) over time and association with fracture risk in real-world patients presenting with elevated TSH. All TSH determinations were done in the same laboratory, which served all hospitals and general practices. The study population consisted of all adults with a first measurement of TSH >4.0 mIU/L (n ¼ 8414) or normal TSH (n ¼ 222,138; comparator). We used a Cox proportional hazards analysis incorporating additional time-dependent covariates to represent initiation of thyroxine replacement and cumulative number of periods with high versus low TSH after index date with a mean follow-up of 7.2 years. Elevated baseline TSH was not associated with an increased risk of hip fracture (HR 0.90; 95% CI, 0.80 to 1.02) or major osteoporotic fractures (HR 0.97; 95% CI, 0.90 to 1.05), nor was subsequent thyroxine prescription predictive of increased risk of fractures. The number of subsequent 6-month periods with low TSH-suggesting excessive thyroxine dosing-was significantly associated with increased risk of both hip fracture (HR 1.09; 95% CI, 1.04 to 1.15) and major osteoporotic fracture (HR 1.10; 95% CI, 1.06 to 1.14). When gender-and agestratified analyses for major osteoporotic fractures were undertaken, hyperthyroid time was identified as a predictor of fracture risk in postmenopausal women whereas hypothyroid time predicted increased fracture risk in men below age 75 years. In conclusion, among patients who present with an elevated TSH, the long-term risk of hip and other osteoporotic fractures is strongly related to the cumulative duration of periods with low TSH-likely from excessive replacement. An independent effect of elevated TSH could only be observed in young and middle-aged men, suggesting gender-discrepant consequences on risk.

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Section: Study Populationmentioning

confidence: 99%

The Excess Risk of Major Osteoporotic Fractures in Hypothyroidism Is Driven by Cumulative Hyperthyroid as Opposed to Hypothyroid Time: An Observational Register-Based Time-Resolved Cohort Analysis

Abrahamsen

Jørgensen

Laulund

et al. 2014

Journal of Bone and Mineral Research

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“…Additionally, risk factors are quantified in a binary fashion, rather than using multiple state options. A wide number of risk factors were considered for inclusion but only nine were felt to have sufficient evidence to justify their inclusion in the model [7].The developers consider FRAX to be a 'platform technology' into which new risk factors can be incorporated as they become available [3]. CRFs used in isolation do not predict fracture risk as strongly as a BMD measurement, however, CRFs in combination with BMD provide an enhanced predictive ability over BMD alone.…”

Section: Fraxmentioning

confidence: 99%

“…In recent years there has been a move away from T-scores as the operating definition of osteoporosis, to the use of absolute risk of fracture and risk calculators based on algorithms to estimate those risks [3]. This movement has led to an evolved definition of the disease which incorporates more clinical risk factors (CRF) and which bases treatment decisions on absolute risk of fracture thresholds over a 10-year period rather than T-scores [3]. This paradigm shift brings osteoporosis into line with other conditions such as heart disease where patient risks are assessed on an absolute risk of event basis over a 10-year period [4].…”

Section: Introductionmentioning

confidence: 99%

Developing Novel Prognostic Biomarkers for Multivariate Fracture Risk Prediction Algorithms

et al. 2012

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Multivariate prediction algorithms such as FRAX ® and QFractureScores provide an opportunity for new prognostic biomarkers to be developed and incorporated, potentially leading to better fracture prediction. As more research is conducted into these novel biomarkers, a number of factors need to be considered for their successful development for inclusion in these algorithms. This review paper describes two well-known multivariate prediction algorithms for osteoporosis fracture risk applicable to the UK population, FRAXand QFractureScores, and comments on the current prognostic tools available for fracture risk, dual x-ray assessment (DXA), quantitative ultrasound (QUS), genomic and biochemical markers and highlights the factors that need to be considered in the development of new biomarkers. These factors include the requirement for prospective data, collected in new cohort studies or using archived samples, the need for adequate stability data to be provided and appropriate storage methods to be used when retrospective data is required. AUC measures have been found to have limited utility in assessing the impact of the addition of new risk factors on the predictive performance multivariate algorithms. New performance evaluation measures, such as net reclassification index (NRI) and integrated discrimination improvement (IDI) are increasingly important in the evaluation of the impact of the addition of new markers to multivariate algorithms and these are also discussed.

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“…o n l y bility of hip fracture or a large osteoporotic fracture. 34,35 In clinical practice this algorithm application could result in initiating a therapy precociously or in assessing BMD. 36,37 DEFRA DEFRA is the Italian algorithm for risk fracture prediction (http://www.defra-osteoporosi.it).…”

Section: Diagnostic Algorithm and Diagnosismentioning

confidence: 99%

The management of the patient with osteoporosis: from evidence to clinical practice

Ambrosino¹,

Riccardo²,

Gnerre³

et al. 2017

Ital J Med

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About 200 million people worldwide are affected by osteoporosis 11 with a higher prevalence in women than men; the prevalence of osteoporosis increases progressively with age in post-menopausal period (5% in the fifth decade versus 50% in the eighth decade). 12According to the WHO, 13 osteoporosis affects more ABSTRACTOsteoporosis is the most common bone disease and is an important problem of public health. In fact, it represents the main cause of age-related fractures and disabilities with a consequent increasing sanitary, social and economic impact. Unfortunately, often osteoporosis is not as thoroughly investigated as it would be desirable and it is underestimated in diagnosis and therapy. The aim of this monograph is to sensitize medical internists to a careful evaluation and an efficacious treatment of osteoporosis in order to reduce the risks of this disease, in particular the fractures, with a view to improving the quality of patients' life.

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From relative risk to absolute fracture risk calculation: The FRAX algorithm

Cited by 73 publications

References 53 publications

The Excess Risk of Major Osteoporotic Fractures in Hypothyroidism Is Driven by Cumulative Hyperthyroid as Opposed to Hypothyroid Time: An Observational Register-Based Time-Resolved Cohort Analysis

The Excess Risk of Major Osteoporotic Fractures in Hypothyroidism Is Driven by Cumulative Hyperthyroid as Opposed to Hypothyroid Time: An Observational Register-Based Time-Resolved Cohort Analysis

Developing Novel Prognostic Biomarkers for Multivariate Fracture Risk Prediction Algorithms

The management of the patient with osteoporosis: from evidence to clinical practice

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