Risk assessment tools to identify women with increased risk of osteoporotic fracture: Complexity or simplicity? A systematic review

Rubin, Katrine Hass; Holmberg, Teresa; Hermann, Anne Pernille; Abrahamsen, Bo; Brixen, Kim

doi:10.1002/jbmr.1956

Cited by 163 publications

(142 citation statements)

References 112 publications

(228 reference statements)

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“…Recently developed prediction tools, such as the FRAX algorithm, Qfracture and Garvan Fracture Risk Calculator, are aimed to assist clinicians in the management of their patients through the calculation of the patient's 5-or 10-year risk of fracture based on a combination of known risk factors. 1 Although relying on observational data rather than intervention studies, the tools may also be useful to some extent in patient consultations to assist discussions on the impact of changing modifiable risk factors. 87 The difficulty in adequately predicting fracture risk after intervention is reflected in the current debate surrounding the accuracy of risk estimation once intervention with anti-osteoporotic drugs has taken place.…”

Section: Risk Assessment Tools and Reversibility Of Riskmentioning

confidence: 99%

“…1 However, many risk factors for osteoporosis and osteoporotic fractures are in themselves modifiable and such risk factors are potential targets for intervention. 2 However, the strongest risk factors such as age, family history and prior fracture are essentially nonmodifiable; therefore, osteoporosis drugs of course remain essential in managing patients with high fracture risk due to osteoporosis.…”

Section: Introduction and Aimmentioning

confidence: 99%

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A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

Abrahamsen¹,

Brask-Lindemann

Rubin

et al. 2014

BoneKEy Reports

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Although many strong risk factors for osteoporosis-such as family history, fracture history and age-are not modifiable, a number of important risk factors are potential targets for intervention. Thus, simple, non-pharmacological intervention in patients at increased risk of osteoporotic fractures could include reduction of excessive alcohol intake, smoking cessation, adequate nutrition, patient education, daily physical activity and a careful review of medications that could increase the risk of falls and fractures. There remains, however, an unmet need for high-quality intervention studies in most of these areas.

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Section: Risk Assessment Tools and Reversibility Of Riskmentioning

confidence: 99%

Section: Introduction and Aimmentioning

confidence: 99%

A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

Abrahamsen¹,

Brask-Lindemann

Rubin

et al. 2014

BoneKEy Reports

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“…The OST scores were categorized with the cut-off value of 2; the BMD was normal if the OST score was ≥2, and low if the OST score was <2. (8,11,12) …”

Section: Auc Aucmentioning

confidence: 99%

“…In spite of this, both meta-analytic studies and systematic reviews have shown that the sensitivity of these tools is not higher than or equal to that of OST. (11,12) Furthermore, the performance of OST does not show consistent results, its sensitivity being higher than its specificity or vice versa. (13) The present study was carried out to evaluate the performance of OST in a more specific manner i.e.…”

Section: Introductionmentioning

confidence: 99%

Performance of osteoporosis self-assessment tool in detecting low bone mineral density in menopausal women

Haryono

Prastowo

2017

Universa Medicina

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“…The recent development of fracture prediction tools is a prime example of one clinical application of powerful statistical methods. (1) When prediction of an event is the sole objective, the existence of an association-whether causal or not-may be sufficient. However, to understand how that association arises, and to intervene appropriately, requires a deeper understanding of the cause and effect relationships.…”

mentioning

confidence: 99%

Untangling the Causality Knot: Another Tool for Clinical Researchers

Lix

Leslie

2014

Journal of Bone and Mineral Research

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Happy the man who has been able to learn the causes of things. -Virgil Clinical experience and a sound knowledge of pathophysiology are the traditional pillars of medicine. In recent years, new statistical methods have strengthened the ability of investigators to extract patterns from complex datasets that would otherwise remain hidden. Research to predict clinical events and the methods needed to support these analyses have evolved in tandem over the last few decades in a classical pas de deux. Indeed, the landscape of modern medicine would hardly be recognizable without such techniques. The recent development of fracture prediction tools is a prime example of one clinical application of powerful statistical methods.(1)When prediction of an event is the sole objective, the existence of an association-whether causal or not-may be sufficient. However, to understand how that association arises, and to intervene appropriately, requires a deeper understanding of the cause and effect relationships. Obesity, defined from elevated body mass index (BMI), has generated much controversy in the osteoporosis literature, variably reported to be protective, neutral, or a risk factor for osteoporosis and related fractures.(2-4) Weight and BMI are both strongly correlated with bone mineral density (BMD) as measured from dual-energy X-ray absorptiometry (DXA) in almost all studies, and it is no accident that weight or BMI are a component of virtually all fracture prediction tools.(1) However, when weight is partitioned into its components of lean mass and fat mass, the associations become more complicated. Most studies find that lean mass is positively associated with higher BMD. (5,6) For fat mass a variety of results have been found, (5,(7)(8)(9)(10)(11)(12) although a recent metaanalysis demonstrates a positive association between fat mass and bone density.(13) Meta-analytic results have also shown that among premenopausal women lean mass exerted a greater effect on femoral neck BMD than fat mass (r ¼ 0.45 versus r ¼ 0.30), whereas in postmenopausal women the effects of lean mass and fat mass were similar (r ¼ 0.33 versus r ¼ 0.31).(13) The presence of large correlations among weight, BMI, lean mass, and fat mass have fueled some of this controversy.(4) For example, if one assumes that lean mass is solely responsible for BMD and that fat mass has no effect, then a model that includes weight and fat mass would actually show the latter as having an adverse (negative) effect on BMD simply because fat is interpreted as "not lean" in the model. Body composition is a major factor impacting accuracy errors in DXA, effects that are amplified in longitudinal studies where weight, body composition, and BMD (not to mention a myriad of other factors) are changing simultaneously. (14)(15)(16) A recent meta-analysis examined the association between BMI and fracture risk in prospective cohorts from 25 countries (398,610 women with an average age of 63 years; 2.2 million person-years follow-up) with a reported 22% prevalence of obesity.(17) ...

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Risk assessment tools to identify women with increased risk of osteoporotic fracture: Complexity or simplicity? A systematic review

Cited by 163 publications

References 112 publications

A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

A review of lifestyle, smoking and other modifiable risk factors for osteoporotic fractures

Performance of osteoporosis self-assessment tool in detecting low bone mineral density in menopausal women

Untangling the Causality Knot: Another Tool for Clinical Researchers

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