FRAX®, a new tool for assessing fracture risk: clinical applications and intervention thresholds




John A. KANIS, MD
Anders ODÉN, PhD
Helena JOHANSSON
Fredrik BORGSTRÖM, PhD
Oskar STRÖM, PhD
Eugene V. MCCLOSKEY, MD
University of Sheffield Medical
School, Sheffield
UNITED KINGDOM

by J . A. Kani s , A. Odén,
H. Johans son, F. Borgs t röm,
O. St röm, and E. V. McCloskey,UK

FRAX® (http://www.shef.ac.uk/FRAX) is a web-based tool, developed by the World Health Organization (WHO) Collaborating Centre for Metabolic Bone Diseases, University of Sheffield (UK), that provides models for assessing fracture probability in men and women. These models, developed from studies in population-based cohorts in Europe, North America, Asia, and Australia, have been extensively validated in additional population-based cohorts with over a million patient years of observation. The algorithms in FRAX® integrate several well-validated clinical risk factors (CRFs)—age, body mass index, and dichotomized variables (eg, prior fracture, smoking, glucocorticoid use, rheumatoid arthritis), with or without bone mineral density (BMD). The models use Poisson regression to derive hazard functions of death and fracture that provide output as 10-year probabilities (hip fracture and major osteoporotic fracture of hip, spine, humerus, or forearm). Models are calibrated to specific countries where the epidemiology of fracture is known. This review addresses the translational practicalities of developing practice guidelines that apply the FRAX® tool at its intended primary-care level. The main applications are to identify patients requiring pharmacological intervention (CRFs alone suffice in some cases) and BMD testing. The practice guidelines that have incorporated FRAX® have set intervention thresholds that vary between countries, since considerations are not only clinical, but also economic. As for the FRAX® tool itself, it remains a work in progress that can only grow in strength, accuracy, and relevance as new databases on multiple other CRFs become available to enrich its algorithms.

Medicographia. 2010;32:33-40 (see French abstract on page 40)

Fracture is the main clinical outcome for osteoporosis patients. The ability to accurately predict the risk of fracture in a patient is highly useful for clinicians in order to select the most appropriate treatment and management interventions. Bone mineral density (BMD) is considered as a major determinant of bone strength, and assessment of BMD at the femoral neck using dual-energy x-ray absorptiometry (DXA) is often performed to diagnose osteoporosis. The usual expression of BMD is as a T-score, which represents the number of standard deviations (SDs) by which the BMD of a patient differs from the mean BMD in young, healthy individuals. A patient has a clinical diagnosis of osteoporosis when their T-score is 2.5 SD or more below that of the young adult mean (T score ≤–2.5 SD).1 Although a T-score ≤–2.5 SD has been shown to accurately predict fracture risk in up to half of women aged over 50 years,2 the risk of fractures in osteoporosis is also dependent on many other factors in addition to BMD. Indeed, many patients reported to be at low fracture risk according to their BMD assessment will still go on to experience fractures. Conversely, not all patients with a T-score ≤–2.5 SD will inevitably develop fractures. Treatment intervention thresholds based on only BMD therefore lack sensitivity and estimation of future fracture risk can be improved when other risk factors are taken into consideration.

The use of additional factors in fracture risk assessment is also advantageous in cases where BMD cannot be determined and in helping to decide the necessity of BMD assessments when health-care resources are limited.TheWorldHealth Organization (WHO) has developed statistical models that integrate information from BMD assessments and clinical risk factors for fracture to predict future fracture risk.1,3 These models can now be used clinically as the FRAX® tool (http: //www.shef.ac.uk/FRAX), which is a computer-based program that calculates the 10-year probability of major osteoporotic fracture (hip, spine, humerus, or wrist) and the 10-year probability of hip fracture for a patient.

The selection of the clinical risk factors used in FRAX® is based on a succession of meta-analyses that aimed to identify factors that are independently associated with osteoporotic fracture risk.1 These meta-analyses used the primary data obtained from 12 prospective cohort studies and comprised individual participant data from almost 60 000 men and women.4-18 The use of primary data in the analyses allows the prognostic importance of each risk factor to be determined in a multivariable context, thereby also allowing interactions between risk factors to be analyzed. Ultimately, this improves the accuracy of the statistical models aimed at predicting fracture risk. It should also be noted that the risk of publication bias is absent with the use of primary data. The dichotomous risk factors identified from the meta-analyses included prior fragility fracture, parental history of hip fracture, current smoker, oral glucocorticoids, rheumatoid arthritis, and alcohol consumption greater than 3 units per day. In addition, body mass index (BMI) was identified as a continuous variable associated with fracture risk. All of these variables showed low to moderate heterogeneity between the different population cohorts and all fulfilled the criteria of being risk factors that were “reversible”, with the appropriate interventions. Each variable was investigated for interactions with sex, age, and BMD, as well as for interactions with the variable itself. With the exception of BMI, all variables were associated with fracture risk independently of BMD.

Figure 1
Figure 1. UK FRAX® tool chart.

Chart for the input of data and formatting of results in the UK version of the FRAX® tool.
Abbreviations: BMD, bone mineral density; WHO, World Health Organization.
Copyright © 2008, World Health Organization Collaborating Centre for Metabolic Bone Diseases, University
of Sheffield, UK.

On the basis of the risk factors identified in these metaanalyses, four statistical models were constructed with the aim of predicting the probability of future fractures.1 These four models comprised the probability of hip fractures and the probability of other osteoporotic fractures, both with and withoutmeasurements for BMD. In eachmodel, fracture was computed as a continuous hazard function using Poisson regression. All significant interactions of risk factors that were observed in the initial meta-analyses were entered into the model. In turn, any of these interactions that were found to be no longer significant for hip fracture and other osteoporotic fractures within the framework of the statistical models were omitted. In addition to the risk factors identified in the metaanalyses, provision was also made in the models for secondary causes of osteoporosis that have been consistently reported to be associated with a significant increase in fracture risk. These included untreated hypogonadism in men and women, inflammatory bowel disease, prolonged immobility, organ transplantation, type 1 diabetes, and thyroid disorders.1

There is some uncertainty regarding the independence of these factors from BMD, but a conservative judgment was made that fracture risk was linked to a low BMD. However, in the absence of any measurements for BMD, the risk ratio for these other secondary causes was assumed to be similar to that of rheumatoid arthritis. The development of these statistical models forms the basis of the FRAX® tool. In the clinical setting, patient risk factors are easily obtained and can be input into the FRAX® Web site to give the probability of hip and other major osteoporotic fractures (Figure 1). Femoral neck BMD may be entered in addition as a T-score or as an absolute value.

It is important to note that besides clinical risk factors, the risk of fracture also varies with geographical location throughout the world.19 In order to calibrate the FRAX® models according to global region, algorithms have been developed based on average 10-year hip fracture probability according to epidemiological data for index countries. Global regions have been categorized according to hip fracture risk as follows:
(a) Very high risk (eg, Denmark, Iceland, Norway, Sweden, USA).
(b) High risk (eg, Australia, Austria, Canada, Finland, Germany, Greece, Hungary, Italy, Kuwait, Netherlands, Portugal, Singapore, Switzerland, Taiwan, UK).
(c) Moderate risk (eg, Argentina, China, France, Hungary, Hong Kong, Japan, Spain).
(d) Low risk (eg, Cameroon, Chile, Korea, Turkey, Venezuela). Currently, FRAX® algorithms have been developed for Austria, China, Germany, France, Italy, Japan, Spain, Sweden, Switzerland, Turkey, the United Kingdom, and the USA. Therefore, in situations where there is no FRAX® algorithm specific to a particular country, a representative country should be chosen that is similar in terms of fracture risk.

Since its launch, the FRAX® tool has been extensively used and its Web site receives an average of 55 000 hits each day. One of the clear uses of FRAX® is the evaluation of the need for treatment intervention among osteoporosis patients in order to minimalize the future risk of fractures. Despite its advantages, FRAX® does have some limitations, which must be borne in mind along when using it in the clinic. Furthermore, the development of algorithms that predict the future risk of osteoporotic fractures needs to be accommodated by the construction of new clinical guidelines. The remainder of this review focuses on the applications and constraints of FRAX® and also on the new challenges that this tool has brought to clinical guidelines for the management osteoporosis patients.

Evaluation of patients for fracture risk

The clinical guidelines for the management of osteoporosis in most countries are currently based on an opportunistic approach where certain clinical risk factors for fracture suggest the possible diagnosis of osteoporosis.20-26 The presence of these risk factors in a given patient is an indication for BMD assessment using DXA. Following this, treatment intervention is considered for patients with BMD values that are within the range of osteoporosis, as defined by the WHO (ie, T-score ≤–2.5 SD).1 Treatment is also recommended for women with a previous history of osteoporotic fracture, without necessarily the need for BMD assessment. With these clinical guidelines, the threshold for treatment intervention is largely dependent on the value of a patient’s BMD.1 However, several of the risk factors that indicate the need for BMD assessment do in themselves contribute independently to fracture risk.27 For example, at age 80 years, the 10-year probability of hip fracture is around 12% in women with a T-score of –2.5 SD, whereas, at age 50 years, the probability is only 2% for the women with the same T-score (Figure 2).28,29 Similarly, the 10-year probability for any major osteoporotic fracture (hip, forearm, shoulder, or clinical spine fracture) in women with a T-score of -2.5 SD ranges from 11% at the age of 50 years to 26% at the age of 80 years.29 These observations demonstrate that the age of a patient has a marked impact on the risk of osteoporotic fracture and that fracture risk can be more accurately assessed from age and BMD than by BMD alone. Similar observations were also noted for the other clinical risk factors identified for use in the FRAX® model that all have an impact independent from BMD on the future risk of fracture (Figure 3, page 36). The incorporation of these factors into the FRAX tool provides a means by which the future probability of fracture for patients can be predicted with more accuracy than with the use of BMD assessments alone.

Figure 2
Figure 2. Probability of hip fracture in Swedish women.

Ten-year probability of hip facture by age and femoral neck bone mineral density
in women from Sweden. Modified from reference 29: Kanis JA, Johnell O, Oden A, Dawson A, De Laet C,
Johnson B. Osteoporos Int. 2001;12:989-995. Copyright © 2001, Springer.

Limitations of FRAX®in clinical evaluations

FRAX® has been primarily designed for use in most countries by primary-care physicians, who have relatively little expert knowledge in the management of patients with osteoporosis.

Figure 3
Figure 3. Effect of clinical risk factors on osteoporotic fracture risk.

Ten-year probability of a major osteoporotic fracture in Caucasian men and women aged 65 from the US according to the presence of the clinical risk factors shown. The body mass index is set at 24 kg/m2.
Modified from reference 35: Kanis JA, Johnell O, Oden A, Johansson H, McCloskey EV. Osteoporos Int. 2008;19:385-397. Copyright © 2008, Springer.

However, the FRAX® tool is not a substitute for a detailed clinical evaluation and physicians must be aware of its limitations when they interpret results in the clinic. Many of the risk factors used in FRAX®, such as cigarette smoking, alcohol consumption, and use of glucocorticoids, are dose dependent.30-32 For these, FRAX® uses risk ratios based on an average dose. Similarly, the risk of fracture increases with the number of prior fractures,33,34 and a previous vertebral fracture is a particularly strong risk factor. Due to a lack of substantial clinical data, the clinician should also be aware that several risk factors for fracture have not been included in the FRAX® algorithm. These include factors such as biochemical markers of bone turnover, risk of falls, and previous pharmacological treatment. In the clinic, this information may also need to be taken into account if necessary.

The FRAX® tool allows the entry of several secondary causes of osteoporosis as risk factors for fracture. With respect to these secondary risk factors, the current evidence is unclear regarding the proportion of risk that they carry compared with BMD. Because of this, it is conservatively assumed that they all mediate fracture risk as a result of low BMD and that they are all left unweighted when entered into FRAX®.35 Another secondary cause of osteoporosis is rheumatoid arthritis. However, it has been established that rheumatoid arthritis carries a fracture risk independent to that provided by BMD,36 and this factor is therefore weighted accordingly in FRAX®. As mentioned above, when no BMD data is entered, the other secondary risk factors for osteoporosis are presumed to increase fracture risk in a manner similar to that of patients with rheumatoid arthritis.

Due to the large amount of clinical data currently available for BMD at the femoral neck, FRAX® is only compatible with BMD measurements from this site. The risk of fracture associated with BMD measurements from the femoral neck is the same in men and women at any given age.37 One convenience of this is that, in accordance with current recommendations, the T-score can be obtained from a single reference standard, the National Health and Nutrition Examination Survey (NHANES) database for female Caucasians aged 20-29 years.1,38 However, it is important to consider that a range of other bone assessments also provide pertinent information concerning fracture risk.1 These include biochemical indices of bone turnover,39 quantitative ultrasound or computed tomography assessments,40,41 and BMD measurements from other parts of the skeleton.42 Although the data from these assessments is too sparse for a meta-analysis of fracture risk that could be used in FRAX®, they should be incorporated into future risk-assessment tools when more clinical information becomes available.

Figure 4
Figure 4. Algorithm for the assessment and management of individuals at risk of fracture.

Abbreviations: BMD, bone mineral density; CRF, clinical risk factor.
Modified from reference 1: Kanis JA; on behalf of the World Health Organization Scientific Group. Assessment of osteoporosis at the primary health-care level. Sheffield, UK: WHO Collaborating Centre, University of Sheffield; 2008.
Technical report. Copyright © 2008, World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield, UK.

In summary, the present model of FRAX® is able to enhance the assessment of osteoporosis patients through the integration of clinical risk factors with or without BMD measurements. Nevertheless, clinicians should not consider the FRAX® tool as the ultimate means of assessing patients, but rather as a basis of assessment which will improve as more clinical data regarding osteoporotic fracture risk becomes available.

Modifications to clinical guidelines to accommodate FRAX®

The advent of fracture risk prediction algorithms such as FRAX® requires some adjustments to current clinical practice guidelines in terms of thresholds for BMD assessment and treatment intervention. In the United Kingdom, some changes have been introduced by the National Osteoporosis Guideline Group (NOGG), and previous opportunistic strategies to identify cases of osteoporosis are now incorporating a probability- based assessment of patients.43 The clinical risk factors for fracture that are now included in the NOGG guidelines are the same as those used in FRAX® with the addition of a BMI less than 19 kg/m2.

The general procedure for managing a patient presenting to the clinic is illustrated in Figure 4.1 Patient management starts with an initial assessment of fracture probability based on age, sex, BMI, and clinical risk factors. The NOGG management strategy classifies patients as being at high, medium, or low risk of future fractures. With the use of the FRAX® tool, this categorization of patients is based on 10-year probabilities of osteoporotic fracture for women aged 50 to 80 years (Table I). In patients considered to be at high risk of future fracture, treatment is recommended, irrespective of BMD. For example, as with previous guidelines,20-26 the NOGG considers that women aged over 50 years with previous fractures should have treatment interventions without having to have BMD assessment.43 Based on the FRAX® model, the treatment intervention threshold in the UK has been set to the equivalent to that of the 10-year probability of future fracture in women over the age of 50 years with prior osteoporotic fracture, but whose BMD is unknown. As can be seen in Figure 5, this treatment intervention threshold increases progressively with age. This is because age is an important independent determinant of fracture risk, and this was not accounted for in the source guidelines. Compared with women of equivalent fracture risk, treatment interventions in men are largely similar in their efficacy,44 and therefore the same intervention threshold applies to men. It is also important to note that, if the resources are available, many clinicians would also perform a BMD test to gain additional information, such as a baseline measure to evaluate response to treatment. In patients considered to be at low risk (Figure 4), the probability of future fracture risk will be so low that a decision not to treat can be made without BMD assessments. An example of such a patient may be a woman at menopause with average BMI (24 kg/m2) with weak or no clinical risk factors, according to the Royal College of Physicians and European guidelines.20-25 The FRAX® 10-year probabilities of amajor fracture and hip fracture that exclude such women are shown in Table I for women with an average BMI.

Table I
Table I. Range of probabilities for BMD testing.

The lower and upper limits for bone mineral density (BMD) assessment according
to 10-year probabilities of major fracture and hip fracture for women with average body mass index are shown.
Modified from reference 43: Kanis JA, McCloskey EV, Johansson H, Ström O, Borgström F, Oden A; National Osteoporosis Guideline Group. Osteoporos Int. 2008;19:1395-1408. [Erratum. Osteoporos Int. 2009;20:499-502.] Copyright © 2009, Springer.

Figure 5
Figure 5. Management chart for osteoporosis.

The darker shaded area in the left hand panel shows the limits of fracture probabilities for the assessment of BMD. The right hand panel gives the intervention threshold.
Abbreviation: BMD, bone mineral density. Modified from reference 43: Kanis JA, McCloskey EV, Johansson H, Ström O, Borgström F, Oden A; National Osteoporosis Guideline Group. Osteoporos Int. 2008;19:1395-1408. [Erratum. Osteoporos Int. 2009;20: 499-502.] Copyright © 2009, Springer.

The proportion of patients considered to be at intermediate risk (Figure 4) will vary between different countries and depends partly on available resources. It is in this group of patients that a BMD evaluation could be potentially useful in order to further assess future risk of fracture. The NOGG has included 10-year probabilities of future fractures that represent upper and lower thresholds for BMD assessment across a range of different ages over 50 years (Figure 5). Patients above the upper probability threshold are recommended for treatment intervention regardless of their BMD. This threshold prevents a patient classified as being at high risk on the basis of clinical risk factors being reclassified as low risk due to information gleaned from BMD assessments.10 For patients below the lower assessment threshold, neither treatment nor BMD evaluation is considered necessary. This threshold has been seen set to exclude a requirement for BMD testing in patients who have minimal risk of future fractures. In the United Kingdom, the upper assessment threshold has been arbitrarily set at 1.2 times the intervention threshold and determines the number of patients who would be eligible for BMD testing.45 Excluding patients with a previous history of osteoporotic fracture, these assessment thresholds imply that, depending on their age, 15% to 30% of patients should undergo BMD assessment.43 Assessing this proportion of the population presenting at the clinic makes the most of the predictive power of BMD measurements, especially with respect to hip fracture.10

In the light of these recommendations for treatment intervention and BMD assessment, the NOGG has summarized the following proposals for patient management:43

1. Postmenopausal women with a previous history of osteoporotic fracture should be considered for treatment. BMD measurement may sometimes be appropriate for these patients, particularly in younger postmenopausal women. Men with a history of osteoporotic fracture should be referred for BMD assessment.

2. Men aged 50 years or more and all postmenopausal women with aWHO risk factoror a BMI <19 kg/m2 should have their future probability of fracture evaluated using the FRAX® tool without measurement of BMD.

3. Individuals with probabilities of a major osteoporotic fracture below the lower assessment threshold shown in Figure 5 can be reassured. A further evaluation using FRAX® is recommended in 5 years or less, depending on the clinical context.

4. Individuals with probabilities of a major osteoporotic fracture above the upper assessment threshold given in Figure 5 or with probabilities of a hip fracture above the upper limit in Table I can be treated without BMD testing.

5. Individuals with probabilities of a major osteoporotic fracture within the limits of the assessment thresholds given in Figure 5 and with probabilities of a hip fracture below the upper limit in Table I should have a BMD test, and probabilities for future fracture risk should be recalculated with FRAX®. If the recalculated probabilities exceed the treatment threshold, treatment intervention should be considered.Where probabilities fall below the treatment threshold, a further assessment is recommended in 5 years or less, depending on the clinical context.

If the clinician has no access to computer facilities, the above guidelines can be broadly followed using simplified paper charts that summarize management decisions on the basis of clinical risk factors and age.46

The integration of a probability-based assessment of future fracture risk using FRAX® is currently being introduced into clinical guidelines for other countries.47-51 The European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) guidelines now apply the same treatment intervention and assessment thresholds (Figure 5).52 However, one difference of the ESCEO guidelines is that BMD measurements are recommended for all patients with future fracture probabilities above the lower assessment threshold. The Japanese Society for Bone and Mineral Research defines a diagnosis of osteoporosis requiring treatment as a BMD less than 70% of the young adult mean (YAM) and less than 80% of the YAM for patients with previous fracture.45 In order to integrate the FRAX® algorithm into Japanese guidelines, T-score equivalents to 70% and 80% of YAM BMD for Japanese people were used.53

Using the NHANES III reference for BMD at the femoral neck in Caucasian women aged 20-29 years, these T-scores were –2.7 SD and –1.8 SD, respectively.54 With these data, the treatment intervention thresholds based on 10-year probabilities of future fractures were highly concordant with the intervention thresholds developed for the UK and Europe (Figure 5).

Conclusions

The development of the FRAX® tool enables physicians working in primary health care to calculate the future risk of osteoporotic fractures in patients through the integration of a range of clinical risk factors with or without BMD measurements. This improves the sensitivity of future fracture risk assessments based on BMD measurements alone. The incorporation of the FRAX® tool into practice guidelines around the world provides an updated means of categorizing patients requiring treatment for osteoporosis and/or BMD assessments. Nevertheless, the FRAX® tool should not replace detailed clinical evaluation, and additional clinical factors that are not currently included in the FRAX® models may need to be considered by the physician, if necessary. In this regard, FRAX® is an evolving body of work that will be constantly updated to improve its outreach and relevance as new data on epidemiology and clinical risk factors are available.

_

Acknowledgement: The work on intervention thresholds has been supported by the National Osteoporosis Guideline Group (NOGG) and the International Osteoporosis Foundation.

References

1. Kanis JA; on behalf of the World Health Organization Scientific Group. Assessment of osteoporosis at the primary health care level. Technical Report. Sheffield, UK: World Health Organization Collaborating Centre for Metabolic Bone Diseases, University of Sheffield; 2007.
2. Wainwright SA, Marshall LM, Ensrud KE, et al. Hip fracture in women without osteoporosis. J Clin Endocrinol Metab. 2005;90:2787-2793.
3. Kanis JA, Borgström F, De Laet C, et al. Assessment of fracture risk. Osteoporos Int. 2005;16:581-589.
4. Hofman A, Grobbee DE, de Jong PT, van den Ouweland FA. Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. Eur J Epidemiol. 1991;7:403-422.
5. De Laet CE, Van Hout BA, Burger H, Weel AE, Hofman A, Pols HA. Hip fracture prediction in elderly men and women: validation in the Rotterdam study. J Bone Miner Res. 1998;13:1587-1593.
6. O’Neill TW, Felsenberg D, Varlow J, Cooper C, Kanis JA, Silman AJ. The prevalence of vertebral deformity in European men and women: the European Vertebral Osteoporosis Study. J Bone Miner Res. 1996;11:1010-1018.
7. Ismail AA, Pye SR, Cockerill WC, et al. Incidence of limb fracture across Europe: results from the European Prospective Osteoporosis Study (EPOS). Osteoporos Int. 2002;13:565-571.
8. Melton LJ III, Crowson CS, O’Fallon WM,Wahner HW, Riggs BL. Relative contributions of bone density, bone turnover, and clinical risk factors to long-term fracture prediction. J Bone Miner Res. 2003;18:312-318.
9. Melton LJ III, Atkinson EJ, O’Connor MK, O’Fallon WM, Riggs BL. Bone density and fracture risk in men. J Bone Miner Res. 1998;13:1915-1923.
10. Johansson H, Oden A, Johnell O, et al. Optimization of BMD measurements to identify high risk groups for treatment—a test analysis. J Bone Miner Res. 2004;19:906-913.
11. Jones G, Nguyen T, Sambrook PN, Kelly PJ, Gilbert C, Eisman JA. Symptomatic fracture incidence in elderly men and women: the Dubbo Osteoporosis Epidemiology Study (DOES). Osteoporos Int. 1994;4:277-282.
12. Dargent-Molina P, Favier F, Grandjean H, et al. Fall-related factors and risk of hip fracture: the EPIDOS prospective study. Lancet. 1996;348:145-149.
13. Chapurlat RD, Garnero P, Breart G, Meunier PJ, Delmas PD. Serum estradiol and sex hormone-binding globulin and the risk of hip fracture in elderly women: the EPIDOS study. J Bone Miner Res. 2000;15:1835-1841.
14. Garnero P, Sornay-Rendu E, Claustrat B, Delmas PD. Biochemical markers of bone turnover, endogenous hormones and the risk of fractures in postmenopausal women: the OFELY study. J Bone Miner Res. 2000;15:1526-1536.
15. Honkanen R, Tuppurainen M, Kroger H, Alhava E, Saarikoski S. Relationships between risk factors and fractures differ by type of fracture: a populationbased study of 12,192 perimenopausal women. Osteoporos Int. 1998;8:25-31.
16. Svanborg A. Seventy-year-old people in Gothenburg a population study in an industrialized Swedish city. II. General presentation of social and medical conditions. Acta Med Scand Suppl. 1977;611:5-37.
17. Johansson C, Black D, Johnell O, Oden A, Mellstrom D. Bone mineral density is a predictor of survival. Calcif Tissue Int. 1998;63:190-196.
18. Fujiwara S, Kasagi F, Yamada M, Kodama K. Risk factors for hip fracture in a Japanese cohort. J Bone Miner Res. 1997;12:998-1004.
19. Kanis JA, Johnell O, De Laet C, Jonsson B, Oden A, Ogelsby AK. International variations in hip fracture probabilities: implications for risk assessment. J Bone Miner Res. 2002;17:1237-1244.
20. Kanis JA, Delmas P, Burckhardt P, Cooper C, Torgerson D. Guidelines for diagnosis and management of osteoporosis. The European Foundation for Osteoporosis and Bone Disease. Osteoporos Int. 1997;7:390-406.
21. Kanis JA, Burlet N, Cooper C, et al. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2008; 19:399-428.
22. Royal College of Physicians. Osteoporosis: clinical guidelines for the prevention and treatment. 1999.
23. Royal College of Physicians and Bone and Tooth Society of Great Britain. Update on pharmacological interventions and an algorithm for management. 2000.
24. Royal College of Physicians. Glucocorticoid-induced osteoporosis. Guidelines on prevention and treatment. Bone and Tooth Society of Great Britain, National Osteoporosis Society, and Royal College of Physicians; 2002.
25. European Community. Report on osteoporosis in the European Community. 1998.
26. National Osteoporosis Foundation. Physicians guide to prevention and treatment of osteoporosis. 2003.
27. Kanis JA. Diagnosis of osteoporosis and assessment of fracture risk. Lancet. 2002;359:1929-1936.
28. Hui SL, Slemenda CW, Johnston CC Jr. Age and bone mass as predictors of fracture in a prospective study. J Clin Invest. 1988;81:1804-1809.
29. Kanis JA, Johnell O, Oden A, Dawson A, De Laet C, Jonsson B. Ten year probabilities of osteoporotic fractures according to BMD and diagnostic thresholds. Osteoporos Int. 2001;12:989-995.
30. Kanis JA, Johnell O, Oden A, et al. Smoking and fracture risk: a meta-analysis. Osteoporos Int. 2005;16:155-162.
31. Kanis JA, Johansson H, Johnell O, et al. Alcohol intake as a risk factor for fracture. Osteoporos Int. 2005;16:737-742.
32. van Staa TP, Leufkens HG, Abenhaim L, Zhang B, Cooper C. Oral corticosteroids and fracture risk: relationship to daily and cumulative doses. Rheumatology (Oxford). 2000;39:1383-1389.
33. Delmas PD, Genant HK, Crans GG, et al. Severity of prevalent vertebral fractures and the risk of subsequent vertebral and nonvertebral fractures: results from the MORE trial. Bone. 2003;33:522-532.
34. Lunt M, O’Neill TW, Felsenberg D, et al. Characteristics of a prevalent vertebral deformity predict subsequent vertebral fracture: results from the European Prospective Osteoporosis Study (EPOS). Bone. 2003;33:505-513.
35. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey EV. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19:385-397.
36. Kanis JA, Johansson H, Oden A, et al. A meta-analysis of prior corticosteroid use and fracture risk. J Bone Miner Res. 2004;19:893-899.
37. Kanis JA, McCloskey EV, Johansson H, Oden A, Melton LJ III, Khaltaev N. A reference standard for the description of osteoporosis. Bone. 2008;42:467-475.
38. Kanis JA, Black D, Cooper C, et al. A new approach to the development of assessment guidelines for osteoporosis. Osteoporos Int. 2002;13:527-536.
39. Delmas PD, Eastell R, Garnero P, Seibel MJ, Stepan J. The use of biochemical markers of bone turnover in osteoporosis. Committee of Scientific Advisors of the International Osteoporosis Foundation. Osteoporos Int. 2000;11(suppl 6): S2-S17.
40. Gluer CC. Quantitative ultrasound techniques for the assessment of osteoporosis: expert agreement on current status. The International Quantitative Ultrasound Consensus Group. J Bone Miner Res. 1997;12:1280-1288.
41. Genant HK, Engelke K, Prevrhal S. Advanced CT bone imaging in osteoporosis. Rheumatology (Oxford). 2008;47(suppl 4):iv9-iv16.
42. Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ. 1996;312: 1254-1259.
43. Kanis JA, McCloskey EV, Johansson H, Ström O, Borgström F, Oden A. Case finding for the management of osteoporosis with FRAX–assessment and intervention thresholds for the UK. Osteoporos Int. 2008;19:1395-1408.
44. Kanis JA, Stevenson M, McCloskey EV, Davis S, Lloyd-Jones M. Glucocorticoid- induced osteoporosis: a systematic review and cost-utility analysis. Health Technol Assess. 2007;11:iii-iv,ix-xi,1-231.
45. Orimo H, Hayashi Y, Fukunaga M, et al. Diagnostic criteria for primary osteoporosis: year 2000 revision. J Bone Miner Metab. 2001;19:331-337.
46. National Osteoporosis Guideline Group; on behalf of the Bone Research Society. Osteoporosis: Clinical guideline for prevention and treatment. Sheffield, UK: University of Sheffield Press; 2008.
47. Siminoski K, Leslie WD, Frame H, et al. Recommendations for bone mineral density reporting in Canada: a shift to absolute fracture risk assessment. J Clin Densitom. 2007;10:120-123.
48. Czerwinski E, Badurski JE, Marcinowska-Suchowierska E, Osieleniec J. Current understanding of osteoporosis according to the position of the World Health Organization (WHO) and International Osteoporosis Foundation. Orthop Traumatol Rehabil. 2007;9:337-356.
49. Tsang SW, Kung AW, Kanis JA, Johansson H, Oden A. Ten-year fracture probability in Hong Kong Southern Chinese according to age and BMD femoral neck T-scores. Osteoporos Int. 2009;20:1939-1945.
50. Lippuner K, Johansson H, Kanis JA, Rizzoli R. Remaining lifetime and absolute 10-year probabilities of osteoporotic fracture in Swiss men and women. Osteoporos Int. 2009;20:1131-1140.
51. Kurth AA, Pfeilschifter J. [Diagnosis and treatment of postmenopausal osteoporosis and osteoporosis in men. German Guidelines Update 2006]. Orthopade. 2007;36:683-690.
52. Kanis JA, Burlet N, Cooper C, et al. European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporos Int. 2008;19:399-428.
53. Fujiwara S, Nakamura T, Orimo H, et al. Development and application of a Japanese model of the WHO fracture risk assessment tool (FRAX). Osteoporos Int. 2008;19:429-435.
54. Looker AC, Wahner HW, Dunn WL, et al. Updated data on proximal femur bone mineral levels of US adults. Osteoporos Int. 1998;8:468-489.