ORIGINAL ARTICLE

Prediction of absolute risk of non-spinal fracturesusing clinical risk factors and heel quantitative ultrasound

A. Díez-Pérez & J. González-Macías & F. Marín &M. Abizanda & R. Alvarez & A. Gimeno & E. Pegenaute &J. Vila & for the ECOSAP study investigators

Received: 5 September 2006 /Accepted: 16 November 2006 / Published online: 18 January 2007# International Osteoporosis Foundation and National Osteoporosis Foundation 2007

AbstractSummary The relationship between osteoporosis risk fac-tors, bone quantitative ultrasound (QUS) and non-spinalfracture risk was estimated in a cohort of 5,201 postmen-opausal women from Spain who were prospectivelyevaluated during three years. Several clinical risk factorsand low heel QUS values were independently associatedwith non-spinal fracture risk.Introduction Low-trauma, non-spinal fractures are a grow-ing source of morbidity and mortality in the elderly. The aim

of the present study was to examine the association of heelquantitative ultrasound (QUS) and a series of osteoporosisand fracture risk factors, with incident low energy non-spinal fractures in a population of elderly women, and toincorporate them into fracture prediction models.Methods 5,201 women aged 65 or older were enrolled in athree-year cohort study. Participants completed an osteoporo-sis and fracture risk factors questionnaire. QUS was measuredat the heel with a gel-coupled device. Cox-proportional hazardanalyses were performed to evaluate the association with thefirst incident low-trauma non-spinal fracture.Results Three hundred and eleven women (6.0%) sustained atotal of 363 low-trauma fractures, including 133 forearm/wrist, 54 hip, 50 humerus, 37 leg and 17 pelvic fractures. Forevery standard deviation decrease in the quantitativeultrasound index, the adjusted hazard ratios (95% CI) for

Osteoporos Int (2007) 18:629–639DOI 10.1007/s00198-006-0297-5

A preliminary report of this study was presented at the plenary postersession of the 27th Annual Meeting of the American Society for Boneand Mineral Research, Nashville, TN, November, 22–27, 2005.

For a complete list of ECOSAP investigators, see Appendix. The firstand second authors equally contributed to the research and themanuscript.

A. Díez-PérezBone Research Unit,Department of Internal Medicine,Hospital Universitario del Mar,Universidad Autónoma,Barcelona, Spain

J. González-MacíasDepartment of Internal Medicine,Hospital Universitario Marqués de Valdecilla,Santander, Spain

F. MarínDepartment of Medical Research,Eli Lilly and Company,Madrid, Spain

M. AbizandaPrimary Care Centre Gran Vía,Barcelona, Spain

R. AlvarezArea X, Primary Care,Madrid, Spain

A. GimenoPrimary Care Centre La Alcudia,Valencia, Spain

E. PegenautePrimary Care Centre Coronel de Palma,Madrid, Spain

J. VilaStatistics Support Unit,Institut Municipal d’Investigació Mèdica (IMIM),Barcelona, Spain

A. Díez-Pérez (*)Unidad de Investigación en Fisiopatología Osea y Articular,Hospital del Mar-IMIM,Paseo Marítimo 25-29,08003 Barcelona, Spaine-mail: adiez@imas.imim.es

any non-vertebral, hip, forearm/wrist, and humerus frac-tures were 1.31 (1.15–1.49), 1.40 (1.01–1.95), 1.50 (1.19–1.89) and 1.35 (0.97–1.87), respectively. Similar resultswere observed with other QUS variables. The bestpredictive models indicated that age, a history of falls, aprevious low-trauma fracture, a family history of fracture, acalcium intake from dairy products of less than 250 mg/day,and lower values of QUS parameters were independentlyassociated with the risk of non-spinal fractures.Conclusions Both clinical risk factors and QUS areindependent predictors of risk of fragility non-spinalfractures. A prediction algorithm using these variableswas developed to estimate the absolute risk of non-spinalfractures in elderly women in Spain.

Keywords Bone densitometry . Bone ultrasound .

Fractures . Menopause . Osteoporosis . Risk factors

Introduction

Non-vertebral fractures are a serious and increasing sourceof morbidity and mortality in the elderly [1–3]. Most ofthese fractures occur at the hip, distal radius and thehumerus, and they are frequently associated with low bonemass [4–6]. Other risk factors and clinical conditions arealso associated with higher risk of non-spinal fractures,although most of the research focuses on hip fractures [7].The early identification of subjects at risk for these types offractures may be helpful to implement preventive strategiesand to decrease the burden in terms of cost and illness.

Bone mineral density (BMD) measurement by dual-energy X-ray absorptiometry (DXA) is the current goldstandard for the diagnosis of osteoporosis, and it is a well-established tool to predict fracture risk. In a meta-analysisincluding 11 prospective studies in women, the predictiveability of the technique indicates relative risk for fracturesof about 1.6 for a decrease of one standard deviation inBMD, except for lumbar spine measurements for spinefractures (relative risk: 2.3) and hip measurements for hipfractures (relative risk: 2.6) [8].

Quantitative bone ultrasound (QUS) is widely usedgiven its simplicity, convenience, low cost, and the factthat it does not use ionizing radiation. It has been suggestedthat QUS might assess some bone structural features inaddition to bone mass [9]. Cross-sectional studies havedemonstrated that QUS, mainly at the calcaneus, discrim-inates between patients with osteoporotic fractures andcontrols, independently of BMD measured by DXA [10–17]. Longitudinal studies have also shown that thecorrelation between reduced calcaneal QUS values and therisk of humeral, hip, and overall fractures is similar to thatobtained with DXA [18]. The validation of these results

with new QUS equipment and in populations from differentcountries where the pattern of risk factors for fracture maydiffer is of great interest. Moreover, DXA availability is stillsub-optimal in some geographic areas, and QUS may be analternative technique for fracture risk assessment, especiallyin a primary care setting [19].

We conducted a 3-year prospective study to analyze therelationship of calcaneal QUS parameters and a series ofosteoporosis and fracture risk factors with low-trauma non-spinal fractures in a population of elderly women attendingprimary care centers throughout Spain. We developed asimple prediction model based on these factors to provideindividualized estimates of the probability of sustainingthese types of fractures.

Materials and methods

Study design and participants

A total of 5,201 Caucasian women aged 65 or older wererecruited in 58 primary care centers throughout Spainbetween March 2000 and June 2001. A non-randomizedsampling of consecutive cases attending the clinic, regard-less of the reason, was performed and women givinginformed consent were included. Ninety-three women didnot agree to participate in the study. The protocol wasapproved by the appropriate institutional review committees.An average of 90 women was included in each center (range26–161). Women returned to the study center semiannually,and were followed up for the occurrence of non-spinal low-trauma fractures for approximately three years.

The following exclusion criteria were used: Paget’sdisease of bone, multiple myeloma, known bone metastases,serum creatinine >265 μmol/dl, serum calcium >11.0 mg/dl,immobilization for >3 months in the preceding year,anomalies of the right foot interfering with calcanealultrasound, therapeutic doses of fluoride (>20 mg/day) formore than 3 months in the past two years, an estimated lifeexpectancy <3 years, or participation in any investigationalstudy involving drugs. At the baseline visit, the siteinvestigators made a comprehensive assessment of the riskfactors of osteoporosis and fractures using a structuredquestionnaire which included age, age at menarche, age atmenopause, years of fertile life, type of menopause, numberof live born offspring, weight, height, body mass index(BMI), personal antecedents of osteoporotic fractures inadulthood (≥35 years), history of osteoporotic fracturesamong first-degree relatives (specifying whether the motheror some other relative was involved), tobacco use, consump-tion of dairy products, alcohol consumption, physicalexercise (walking hours each week - intentional or otherwise- or intense physical exercise), the existence of sensory

630 Osteoporos Int (2007) 18:629–639

problems, the number of falls in the past year, a personalhistory of conditions constituting risk factors for osteoporosisor falls (chronic liver disease, Cushing’s syndrome, hyper-parathyroidism, cerebrovascular events, malabsorption, sec-ondary amenorrhea lasting more than one year, chronicbronchopathy (pulmonary obstructive disease/ asthma),rheumatoid arthritis, urolithiasis, Parkinson’s disease); andpresent or past consumption of drugs capable of affectingbone metabolism or relating to falls, i.e., anticonvulsantdrugs, thyroid hormones, glucocorticoids, anticoagulants(dicoumarin or heparin), thiazides, benzodiazepines, antiar-rhythmic drugs, oral antidiabetics or insulin, and a detailedlist of all available osteoporosis drugs.

Bone ultrasound and fracture assessment

The details of the QUS measurements in this study havebeen reported previously [10, 20]. In brief, QUS wasperformed at baseline in the right calcaneus using aSahara™ device (Hologic, Waltham, MA, USA). Thisequipment measures the broadband ultrasound attenuation(BUA)(dB/MHz) and the speed of sound (SOS)(m/sec) ina fixed region of interest in the central calcaneal zone. Theresults of these two variables are combined to calculate theestimated bone mineral density (eBMD) of the calcaneus ing/cm2, based on the following equation:

eBMD ¼ 0:002592� BUA þ SOSð Þ�3:687 g�cm2

� �:

Moreover, eBMD is also reported based on its T-score.The European reference population we used has beendescribed elsewhere [21], and yields results similar to theapplication of normative values from Spanish women forthe same QUS device [22].

This device also combines the values of BUA and SOSto yield a parameter known as the “quantitative ultrasoundindex” (QUI), based on the following equation:

QUI ¼ 0:41� BUA þ SOSð Þ�571:

The instruments were subjected to daily quality controlusing a phantom provided by the manufacturer during therecruitment period in each study site. Assessments weremade by site-specific trained examiners.

During scheduled six-month visits, patients were askedto report fractures that had occurred at non-spinal sitessince the last visit. Only low-energy trauma fractures,defined as secondary to minor trauma or a fall from thestanding position to floor level, were analyzed. Pathologicalfractures were excluded, as were those caused by severetrauma (traffic accidents, impact of moving objects, fallingfrom greater than standing height) and fractures of theskull, face, metacarpals and phalanges. A subgroup of mainnon-spinal fractures (hip, forearm/wrist, humerus, pelvis,

clavicle, leg) was predefined for the analysis. These are thesix most common types of non-spinal fractures associatedwith low bone mass [4]. All fractures were confirmed bythe site investigator, who reviewed the original X-ray filmor the radiological or surgical reports.

Statistical analyses

The characteristics of the patients with or without incidentfractures were compared by univariate analysis using a Coxproportional hazard model including only one variable asexplanatory. The time-to-first-event survival method wasused in the analyses. A multivariate Cox proportionalhazards model was used to estimate the hazard ratio (HR)of fracture and the 95% confidence intervals (CI) for eachstandard deviation (SD) decrease in all the QUS measure-ments. Additional analyses considered the risks for thedifferent fracture sites and for the group of main non-spinalfractures. These variables were initially adjusted for age in alinear regression model. Variables that achieved at leastmarginal significance (i.e., p<0.15) in the univariateanalysis were included as possible explanatory variables ina saturated model. Variables were removed via a backward-stepwise process if their exclusion did not significantlymodify the likelihood of the model, and the beta coefficientsof the remaining variables did not change more than 15%.All first-order interactions of the variables from the finalmodel were also tested. A non-parametric penalised splinefunction was applied to fit continuous variables in the model[23]. Calibration and discrimination were calculated asmeasures of performance. The D’Agostino-Na version ofthe Hosmer and Lemeshow goodness-of-fit test was used tocalculate a Chi square value [24]. The area under the ROCcurve (AUC) summarized the diagnostic discrimination.We used the index of rank correlation, Somers’ D, whichequals 2×(c−1/2) where c is the concordance (discrimi-nation) probability. The daily calcium intake from dairyproducts was categorized in < or ≥250 mg (≈ 1 cup of200 ml of milk or equivalent). Statistical analyses weredone with R 2.2.1 (The R Foundation for StatisticalComputing, Free Software Foundation, Inc., Boston, MA,USA) and SAS 9.1 (SAS Institute, Cary, NC, USA).

Results

At least one post-baseline visit was available for 5,146women (98.9%) of the cohort. Women without follow-upevaluations (n=55) did not differ from the main cohort inthe main demographic characteristics or QUS results (datanot shown). The main characteristics of the 5,146 womenwith at least one follow-up visit and the univariate analysiswith the differences between the women with or without

Osteoporos Int (2007) 18:629–639 631

incident fractures are summarized in Table 1. A total of4453 women (87.3%) completed the scheduled three-yearvisits. During a median of 3.01 years of follow-up (mean

(SD): 2.83 (0.72) years), representing a total follow-up of14,999 women-years, 311 women suffered at least oneincident low-trauma non-spinal fracture, a cumulative

Table 1 Baseline characteristics of the whole study population and by fracture status during follow-up visits

Non-vertebral fractures

All patients (n=5146) Yes (n=311) No (n=4835) p-value(b)

Age at baseline, ys 72.3 (5.4) 73.6 (5.6) 72.2 (5.3) <0.001Age at menarche, ys 13.3 (1.8) 13.2 (1.7) 13.3 (1.8) 0.521Age at menopause(c), ys 49.0 (4.7) 48.7 (4.9) 49.0 (4.7) 0.274Fertile years 35.1 (5.6) 34.7 (5.6) 35.1 (5.5) 0.209Surgical menopause 11.9% 13.4% 11.2% 0.409Weight, kg 68.4 (11.2) 68.4 (13.2) 68.4 (11.0) 0.949Weight ≤57 kg 14.6% 17.4% 14.4% 0.106Height, cm 153.0 (6.6) 152.2 (6.6) 153.0 (6.6) 0.030Body mass index (kg/m2) 29.2 (4.7) 29.5 (5.1) 29.2 (4.6) 0.351N°. live-born offspring:None 13.2% 15.2% 13.1% referent1 or 2 35.0% 37.7% 34.9% 0.685≥3 51.7% 47.1% 52.0% 0.137History of falls (last year) 26.7% 41.8% 25.7% <0.001Any fracture since age 35 20.2% 35.4% 19.2% <0.001Family history of fracture 16.7% 25.7% 16.2% <0.001Maternal history 11.2% 16.4% 10.9% 0.001First degree, non-maternal 5.5% 9.3% 5.3% <0.001Sensory organs disorders 5.7% 8.0% 5.6% 0.028Physical activity, hrs/wk 16.1 (13.3) 15.7 (13.3) 16.1 (13.3) 0.444Current smoking 2.2% 1.3% 2.3% 0.296Dairy calcium consumption, mg/day 880.5 (427.9) 818.5 (438.4) 884.5 (426.9) 0.018Dairy calcium consumption < 250 mg/day 4.8% 8.8% 4.5% 0.001Alcohol consumption (g/day) 2.4 (5.9) 3.2 (7.7) 2.4 (5.8) 0.013No alcohol 71.3% 65.6% 71.7% Referent1–2 drinks/day 27.1% 31.8% 26.8% 0.045>2 drinks/day 1.6% 2.6% 1.5% 0.100Current use of:Anticonvulsants 0.7% 0.6% 0.7% 0.848Thyroid hormone 2.5% 1.3% 2.6% 0.150Glucocorticoids(d) 2.1% 2.6% 2.1% 0.530Anticoagulants 3.0% 3.5% 3.0% 0.571Thiazide diuretics 16.6% 15.1% 16.6% 0.541Benzodiazepines 26.3% 28.3% 26.2% 0.457Antiarrhythmics 3.8% 5.1% 3.7% 0.217Antidiabetic drugs 9.7% 10.3% 9.7% 0.777Bisphosphonates 4.4% 5.8% 4.3% 0.240History ofChronic bronchopathy 3.0% 4.8% 2.9% 0.029Chronic liver disease 1.2% 1.0% 1.2% 0.764Stroke 1.3% 1.9% 1.2% 0.273Rheumatoid arthritis 0.8% 1.3% 0.8% 0.291Secondary amenorrhea 1.3% 0.6% 1.3% 0.262Urolithiasis 2.1% 1.6% 2.2% 0.555Parkinson’s disease 0.6% 0.6% 0.6% 0.770

Results are expressed as mean (SD) or as %(a)

(a) data are shown if prevalence in any group was ≥0.5%.(b) p-value from a Cox regression model including one variable as explanatory.(c) women with non-surgical menopause only.(d) cumulative dose ≥7.5 mg/day of prednisone (or equivalent) for more than 6 months in course of lifetime.

632 Osteoporos Int (2007) 18:629–639

fracture rate of 6.0%, and an incident rate of 2420 per100,000 women-years. The overall adjudicated non-verte-bral fractures was 363, including 54 hip, 133 forearm/wrist,50 humerus, and 37 leg fractures (Table 2). Fifty-twopatients (1.0%) sustained 2 or more incident non-spinalfractures, and ninety-nine women (1.9%) died fromunrelated causes during follow-up.

After adjustment for age, lower values of calcaneal QUSmeasurements were observed in women with incident non-spinal fragility fractures (Table 3). A backward stepwiseCox regression analysis was performed to evaluate the HRsof fracture per 1-SD change in QUS values (Table 4). Afteradjusting for confounding variables, the HRs (95% CI) ofany type of low-trauma non-spinal fracture for each 1-SDdecrease was 1.32 (1.16–1.50), 1.29 (1.14–1.45), 1.33(1.17–1.51), 1.20 (1.08–1.34), and 1.31 (1.15–1.49) for

eBMD, eBMD T-score, BUA, SOS, and QUI, respectively(Table 4). The association between lower QUS values wasalso significant for main six low-trauma non-spinal frac-tures and for hip, forearm/wrist, and humerus fracturesseparately. The rest of the non-spinal fractures analyzed didnot show association with QUS, although a trend wasobserved for leg and pelvis fractures (Table 4). For all sites,SOS was less associated with fractures than BUA and othercalculated QUS parameters (Table 4).

Table 5 shows the Cox regression model to predict non-spinal fractures risk for women during a median of 3.01 yearsof follow-up. For the main analysis including all fractures,the model indicated that the risks increased approximately3% per each additional year of age, 70% for a prior historyof falls in the previous year, 73% for a previous history oflow energy fracture after the age of 35, 53% for a fracturehistory in a first-degree relative, 92% if the daily calciumintake from dairy products was less than 250 mg, and 31%per each SD decrease in the QUI. A sensitivity analysis, notconsidering the 226 women who had received at least onedose of bisphosphonates before the baseline visit, yieldedalmost identical results (data not shown).

The efficacy of prediction was tested with the AUCs,which were very similar for overall non-spinal, main non-spinal, hip, forearm/wrist and humerus fractures, rangingbetween 0.67 and 0.69 (Table 5).

Algorithms were developed to predict low-trauma non-spinal fractures from the β-coefficients of Cox proportionalhazard models using QUI and BUA (Tables 6 and 7).Examples of the application of these algorithms are shownin the Appendix.

Discussion

Clinical decisions in osteoporosis usually rely on theestimation of relative risk for fractures based on the

Table 3 Baseline QUS measures

All patients (n=5146) Fracture group (n=311) Non-fracture group (n=4835) p-valueMean (SD) Mean (95% CI) Mean (95% CI)

eBMD (g/cm2) 0.437 (0.118) 0.403 (0.390; 0.416) 0.439 (0.435; 0.442) <0.001eBMD (T-score) −1.27 (1.07) −1.58 (−1.70; −1.46) −1.26 (−1.29; −1.23) <0.001BUA (db/MHz)a 65.1 (17.6) 59.7 (57.7; 61.7) 65.4 (64.9; 65.9) <0.001SOS (m/s)a 1525.5 (36.0) 1518 (1514; 1522) 1526 (1525; 1527) <0.001QUI (%) 81.0 (18.6) 75.7 (73.7; 77.8) 81.3 (80.8; 81.9) <0.001

Age-adjusted comparison of QUS parameters between the groups of patients with and without incident low-trauma non-spinal fractures are alsoshown.

a BUA and SOS reports were not available in 459 women.eBMD: estimated bone mineral densityBUA: broadband ultrasound attenuationSOS: speed of soundQUI: quantitative ultrasound index (stiffness index)

Table 2 Number and distribution of the low-trauma non-spinalfractures sustained during the 3 years follow-up

Number of women with firstfracture at this site

Total number offractures (%)

Wrist/forearm

104 133 (36.7%)

Hip 49 54 (14.9%)Humerus 48 50 (13.8%)Leg 32 37 (10.2%)Foot(a) 30 31 (8.6%)Sternum/ribs

25 27 (7.4%)

Pelvis 13 17 (4.7%)Clavicle/scapula

10 10 (2.7%)

Sacrum/coccix

4 4 (1.1%)

Total 311(b) 363 (100%)

(a) excluding toes.(b) four women experienced two simultaneous non-spinal fractures(wrist + foot, pelvis + leg, wrist + pelvis, wrist + humerus).

Osteoporos Int (2007) 18:629–639 633

measurement of BMD by densitometry. However, thisapproach does not include the background risk of theindividual patient, beyond age- and gender-adjustments.Therefore, it is an imperfect estimate of the absolute risk foran individual patient over a given period of time. Absolute riskestimates provide a clinically meaningful dimension of theactual risk of fracture and a more solid basis for intervention.For this reason, the International Osteoporosis Foundation andthe National Osteoporosis Foundation recommend that risk offracture be expressed as a fixed-term absolute risk [25].

In a cohort of more than 5,000 women aged 65 or over,we have found that QUS parameters obtained at thecalcaneus are independent predictors of non-spinal fracturerisk during three-year follow up. After adjustment forpossible confounders, a 1-SD decrease in QUS values wasassociated with an increase of approximately 30% in risk,with SOS showing a lower value. Moreover, five clinicalfactors also showed independent predictive power. Takentogether, these variables allowed us to develop a predictivemodel to assess the absolute risk for non-spinal fracture

Table 5 Cox regression models to predict non-spinal fracture risk

Risk factors Overall non-spinalfractures

Main non-spinalfractures(a)

Hip fractures Wrist/forearmfractures

Humerusfractures

Age (per 1 year) 1.03(1.01 – 1.05)(b)

1.03(1.01 – 1.06)(b)

1.07(1.02 – 1.12)(d)

1.01(0.97 – 1.04)

1.06(1.01 – 1.11)(e)

Falls in previousyear (vs. none)

1.70(1.35 – 2.15)(c)

1.66(1.28 – 2.15)(c)

1.23(0.68 – 2.22)

2.05(1.39 – 3.01)(c)

1.53(0.86 – 2.72)

Family history (1st degree)of fracture (vs. none)

1.53(1.17 – 1.99)(b)

1.45(1.08 – 1.95)(e)

1.88(1.00 – 3.53)(e)

1.02(0.63 – 1.67)

1.93(1.04 – 3.58)(e)

Personal history oflow-trauma fractures(vs. none)

1.73(1.36 – 2.21)(c)

1.82(1.40 – 2.39)(c)

1.25(0.67 – 2.35)

1.79(1.19 – 2.69)(d)

1.99(1.11 – 3.60)(e)

Calcium intake (dairy products)<250 mg/d

1.92(1.30 – 2.86)(b)

1.70(1.07 – 2.68)(e)

2.52(1.07 – 5.92)(e)

1.52(0.74 – 3.12)

1.61(0.58 – 4.47)

QUI (for each 1SD decrease)

1.31(1.15 – 1.49)(c)

1.40(1.21 – 1.62)(c)

1.48(1.06 – 2.05)(e)

1.49(1.19 – 1.86)(c)

1.32(0.96 – 1.81)

AUC (standard error) 0.672 (0.016) 0.680 (0.017) 0.686 (0.041) 0.676 (0.026) 0.689 (0.038)Calibration: χ2 9.5 5.0 10.6 2.0 9.8(p-value) (0.306) (0.756) (0.225) (0.981) (0.281)

Hazard ratios with associated 95% confidence intervals are shown. Only models with quantitative ultrasound index (QUI) are shown.(a) hip, wrist/forearm, humerus, pelvis, clavicle, leg.(b) p<0.005, (c) p<0.001, (d) p<0.01, (e) p<0.05AUC (SE): Area under Ccurve (standard error)

Table 4 Hazard ratios with 95% confidence intervals for first low-trauma non-spinal fractures at each site, for each decrease in 1-SD(a) of thequantitative calcaneus ultrasound values(b)

All fractures(n=311)

Main fractures(c)

(n=253)Hip (n=49) Wrist/forearm

(n=104)Humerus(n=48)

Leg (n=32) Pelvis(n=13)

eBMD(g/cm2)

1.32(d)

(1.16 – 1.50)1.41(d)

(1.22 – 1.63)1.45(e)

(1.04 – 2.01)1.50(d)

(1.19 – 1.89)1.37(0.99 – 1.91)

1.25(0.83 – 1.88)

1.56(0.77 – 3.00)

eBMD(T-score)

1.29(d)

(1.14 – 1.45)1.39 (d)

(1.22 – 1.60)1.46(f)

(1.07 – 2.01)1.46(f)

(1.17 – 1.80)1.37(e)

(1.01 – 1.87)1.24(0.85 – 1.82)

1.56(0.81 – 2.98)

BUA(db/MHz)

1.33 (d)

(1.17 – 1.51)1.38 (d)

(1.20 – 1.59)1.56(f)

(1.13 – 2.16)1.35(f)

(1.08 – 1.69)1.38(e)

(1.00 – 1.89)1.34(0.90 – 2.00)

1.62(0.82 – 3.20)

SOS (m/s) 1.20 (d)

(1.08 – 1.34)1.24(d)

(1.12 – 1.39)1.21(0.95 – 1.56)

1.30(d)

(1.12 – 1.51)1.18(0.90 – 1.55)

1.14(0.76 – 1.70)

1.41(0.97 – 2.05)

QUI (%) 1.31(d)

(1.15 – 1.49)1.40(d)

(1.21 – 1.62)1.40(e)

(1.01 – 1.95)1.50(d)

(1.19 – 1.89)1.35(0.97 – 1.87)

1.24(0.83 – 1.87)

1.58(0.79 – 3.15)

(a) 1 SD: eBMD=0.12 g/cm2 , eBMD T-Score=1.0 SD, BUA=17.6 db/MHz, SOS=36.0 m/s, QUI=18.6%.(b) adjusted by age, history of falls, prevalent fractures, family history of fractures and calcium intake from dairy products.(c) hip, wrist/forearm, humerus, pelvis, clavicle, leg.(d) p<0.001.(e) p≤0.05.(f) p≤0.01.

634 Osteoporos Int (2007) 18:629–639

based on clinical factors and bone measurements that areeasily retrievable in a primary health care setting.

Fracture prediction capacity for DXA is well established[8]. The evidence for QUS is scarcer, but most studiessupport its use for fracture risk assessment [10–18]. Even inwomen with high BMD on DXA, bone ultrasound param-eters are associated with hip fractures [26]. The use of QUSis increasing, and it has been approved for screening of lowbone mass. However, the diversity of QUS devices, theirperformance in different populations, and the lack ofstandardization have precluded a wider acceptance of thetechnique for fracture risk assessment. Thus, the newlyintroduced bone ultrasound devices must be prospectivelyvalidated for this objective in the population in which theyare intended to be used [19]. Our results confirm preliminaryfindings from our group that the calcaneal system we testeddiscriminates between fracture and non-fracture groups in across-sectional analysis [10]. Lower QUS values wereassociated with higher risk for overall non-spinal fractures,with even higher HRs for fractures at the hip, forearm/wrist,and the humerus, which are associated with low bone mass

[4] (Table 4). These results are similar to those reported inprospective studies based on heel QUS devices [18]. For hipfractures, relative risks of 1.9 and 2.3 have been reported inthe 3-year EPIDOS [27] and SEMOF studies [28], while inthe NORA Study, the same device we used, yielded a risk of1.31 after only 1 year of follow-up [29].

However, although bone intrinsic properties are impor-tant components of fracture risk, its single assessment doesnot capture the many different factors that influence thisrisk. Our current understanding of osteoporotic fracturescontemplates several factors such as falls, nutrition, andlife-style [30], which should be considered in the individualrisk assessment. These clinical risk factors must beprevalent, strongly associated with fractures, and, ideally,economical and simple to evaluate in a routine clinical visit.The independent risk factors in our multivariate model wereage, personal and familial history of fracture, history offalls, and a daily calcium intake from dairy products lowerthan 250 mg (≈ 1 cup of 200 ml of milk or equivalent). Noassociation was found with other risk factors that have beenrelated with osteoporosis and fracture risk such as lowerweight, reproductive history, physical activity, smoking,long-term exposure to glucocorticoids, and several chronicdiseases. However, it should be noted that we did notevaluate vertebral fractures, and that our model was notintended to predict low bone mass, while most of these riskfactors have been associated with this type of osteoporoticfracture, or with the prediction of low bone mass [31–35].The predictors in our model are biologically plausible, andmost of them are in accordance with those described inprospective studies [36–39] and in meta-analysis [40–42].Aging is strongly associated with a derangement in theconstituents of bone strength other than BMD, also knownas bone quality elements [43], including increased boneturn-over [44], microarchitectural properties [45], mineral[46] and non-mineral bone tissue composition [47], as wellas with postural reflexes and muscle strength deterioration,producing a propensity to fall [48], a widely recognized riskfactor for fractures, particularly non-spinal, and our resultsconcur with this association. Family history can reflect thestrong genetic influence in osteoporosis, whereas a pasthistory of fragility fracture is a strong marker of increasedrisk [41]. Calcium intake is relevant for bone health, but itsaccurate calculation is difficult and time consuming. A cupof milk is generally considered the typical dairy productunit, and it often represents the only daily consumption.Therefore, we chose this low cut-off threshold of calciumintake, easy to assess on a primary care setting, thatrepresents approximately one portion of dairy productsper day. This same approach has been used in a recentmeta-analysis [42]. After applying this cut-off, we found asignificant predictive value for those individuals with verylow calcium intake, consistent with previous reports in

Table 7 β-Coefficients underlying low-trauma non-spinal predictionfrom best Cox regression model using BUA

β-Coeff SE

Age (per 1 year) 0.037 0.010Falls in previous year (vs. none) 0.485 0.119Family history (1st-degree) of fracture(vs. none)

0.447 0.134

Personal history of low-trauma fractures(vs. none)

0.576 0.125

Calcium intake (dairy products) <250 mg/d 0.618 0.202eBMD T-Score (for each 1 SD decrease) 0.284 0.061

β-Coeff: β-CoefficientSE: Standard ErrorSD: Standard Deviation

Table 6 β-Coefficients underlying low-trauma non-spinal predictionfrom best Cox regression model using QUI

β-Coeff SE

Age (per 1 year) 0.033 0.010Falls in previous year (vs. none) 0.533 0.119Family history (1st degree) offracture (vs. none)

0.424 0.135

Personal history of low-traumafractures (vs. none)

0.550 0.125

Calcium intake (dairy products)<250 mg/d

0.655 0.202

QUI (for each 1 SD decrease) 0.270 0.065

β-Coeff: β-CoefficientSE: Standard ErrorSD: Standard Deviation

Osteoporos Int (2007) 18:629–639 635

North American [49], European [50], and Mediterraneanpopulations similar to ours [51]. However, other studies[52] and a recent meta-analysis [42] have not found thisassociation. We speculate that the homogeneity of ourpopulation or some factors not controlled for in moststudies, such as fractional calcium absorption or theinteraction between dietary protein and calcium, mightexplain these discrepancies [53].

Two recently published non-spinal fracture predictionmodels based on the OFELY cohort [36] and the SOF study[37] included a similar series of risk factors to ours,although they used BMD measured by DXA instead ofQUS. Furthermore, we did not evaluate any index ofmuscular performance, and smoking was not a predictivefactor in our cohort, in contrast to the SOF model [37] andto a recent meta-analysis [54], probably because of the verylow prevalence of smokers in this age group in Spanishwomen (2.2%). Similarly, the low use of corticoids in ourseries (2.1%) may also explain the discrepancy with themodel developed by Johansson et al. [55], where the use ofcorticoids was higher (9.3%).

The relatively short list of risk factors in our predictivemodel makes its assessment feasible in clinical routine of aprimary care centre. Although any of the QUS parametersmay be used, we selected the model including QUI(Appendix) based on a larger group of patients for whomfull covariate information was available, and because itperformed well in the prediction. Moreover, QUI has abetter precision error, and a stronger correlation with DXAheel BMD, and it is an absolute value not influenced by thereference norms needed to calculate T-scores [56]. QUI, acombination of BUA and SOS, is analogous to the stiffnessparameter provided by other heel devices [57]. Thepredictive ability of our model exam\ined by ROC analysisfor main non-spinal and hip fractures (0.680 and 0.686respectively) is very similar to those reported by heel DXA(0.635) [38], and comparable to those obtained using othersites and technologies (range: 0.66–0.88) [37, 57, 58].

In addition to the scales based on numerical additive scoresto quantify risk for fractures [37, 58, 59], other fracture riskscales are provided as computer-based calculators, such asthe Medsurf Remaining Lifetime Fracture Probability(RLFP) [60], based on the Hawaii Osteoporosis Study, andthe St. Boniface General Hospital scale [61]. A thirdcomputer model based on theoretical considerations hasrecently been proposed, but it overestimates fracture riskwhen applied to populations by 2–3 fold [62]. Thedevelopment of this type of tool in the assessment of absoluterisk for fractures seems worthwhile, as there are valuableprecedents in other areas of medicine, mainly for cardiovas-cular [63, 64] and breast cancer risk assessment [65].

Our study has limitations. The population analyzed inour study does not necessarily make this model suitable for

younger postmenopausal women or for men. However,women aged 65 or older is the target population forscreening strategies according with NOF guidelines [66].Moreover, our study population is clinically relevant sinceis at higher risk of osteoporosis and fracture than men orwomen below 65 and, accordingly, is where any interven-tion should be assumed as more cost-effective.

This is the first prospective study to assess non-spinalfracture risk in women from Spain, and our results need tobe validated in other populations, although the weight ofthe risk factors is comparable to other western Caucasiangroups. Our population was recruited during a visit to aprimary care center, and this subgroup may be notrepresentative of the general population. However, theseare real-life conditions because only those who are incontact with the health care system are a viable target forcase-finding screening strategies. We have not includedvertebral fractures for the feasibility of the study. However,non-spinal low-trauma fractures are a relevant problem bythemselves in this population. Indeed, decisions based onnon-spinal fracture risk are also expected to result in asignificant benefit for spinal fractures. Finally, although theformula is not user-friendly, the models will allow thedevelopment of a web-based calculator.

In summary, we have developed an absolute riskassessment tool for individualized prediction of fracturerisk, particularly suitable for primary care physicians, thatcombines easily assessable clinical risk factors and QUS.Fracture risk assessment strategies at the primary healthcare level may significantly impact the management of thedisease and decrease its burden in the future.

Acknowledgements We thank M. Nieva, Ma.A. Valero, E. Arriaza,A.C. Franch, and Ma.S. Galatte, Department of Medical Research, EliLilly and Company. Madrid (Spain), for the study monitoring, andSergi Mojal, Institut Municipal d’Investigació Mèdica (IMIM), forstatistics programming.

Disclaimers Dr. Fernando Marín is an employee of Eli Lilly, acompany that investigates and commercializes bone active drugs. Allother authors state that they have no conflicts of interest.

Funding This study was supported by an unrestricted grant from theDepartment of Medical Research. Eli Lilly and Company, Madrid(Spain).

Appendix

The probability that a woman with a specific characteristicdevelop an osteoporotic fracture in the next three years iscomputed by:

1� bSexp β!

� b!� �

� β!

�X!� �0

B@1CA

636 Osteoporos Int (2007) 18:629–639

Where:bS= Baseline survival function at 3 years (i.e., 0.948).b!� b

!= Linear function multiplying the estimated beta-

coefficients (Tables 6 or 7) by the vector of patient’scharacteristic.

b!�X

!= Linear function multiplying the estimated beta-

coefficients by the vector of all-patients means characteris-tic (from Table 2).

Baseline survival function: probability to not have afracture for a women evaluated with covariates (age, historyof falls, etc. ) set at the means of the sample.

For example, consider a 72-year-old woman who hashistory of fall in previous year, no family history offracture, has had a personal low-trauma fracture, takes 2cups of milk every day and has a QUI value of 76%.

β!� b

!¼ 0:033�72þ 0:533�1þ 0:424�0þ 0:550�1

þ0:655�0þ 0:270� �1ð Þ� 76� 81:0

18:6¼ 3:5316

β!�X

!¼ 0:033�72:3þ 0:533�0:267þ 0:424�0:202þ0:550�0:167þ 0:655�0:048

þ0:270� �1ð Þ� 81� 81:0

18:6¼ 2:7416

(Note that because QUI-value is included standardizedin the Cox model, 81�81:0

18:6 ¼ 0).Thus, on average a woman with the above characteristics

has a chance to develop an osteoporotic fracture in the nextthree years:

1� 0:948exp3:5316�2:7416ð Þ

� �¼ 0:11 ¼¼> 11%

Similar calculations can be done using BUA results(Table 7).

ECOSAP (Ecografía Ósea en Atención Primaria)M.S. Arenas (C.S. La Florida. Alicante); J. Gálvez, E.

Mira (C.S. Los Ángeles. Alicante); V. Borreguero, M.A.Cabrera, P. Aceña (C.S. El Pla Hospital. Alicante); R.Berenguer, A. Cebreiro, M. Puchades, J. Cantero (C.S.Algemesí-Alfafar. Valencia); R. González, M.L. Altarriba,A. García-Royo (C.S. Salvador Pau. Valencia); M.A. Fortea,M.A. López (C.S. Campanar. Valencia); A. Gimeno, Z. Pla(C.S. La Alcudia. Valencia); C. Alfonso, B. García-López (C.S. San Andrés. Murcia); J.E. Carrasco, J. Aliaga (C.S.Abarán. Murcia); S. Giménez (C.S. Ciudad Jardín. Málaga);J.A. Godinez (C.S. Antequera. Málaga); S. Alvarez (C.S. LasAlbarizas. Marbella); F. Ruiz (C.S. Las Lagunas. MijasCosta); R. Moya, M.A. Martín, M.M. Pérez, R. Vera (C.S.Cerro del Águila. Sevilla); T. Guerrero, H. Sánchez (C.S.Fuensanta. Sevilla); J. Calvo, J.A. Alameda (C.S. El Molinode la Vega. Huelva); B. Galán (C.S. Fuente Palmera.

Córdoba); D. Martín (C.S. Torredelcampo. Jaén); J.A.Castro (C.S. Cartuja-Almanjayar. Granada); J.J. Rascón, P.Arqueros (C.S. Ciudad Jardín. Almería); J. Brunet, J.Comerma (ABS Sant Llatzer. Terrassa); C. Rubio, S.Cañadas, M. Berenguer (ABS Florida Nord. Hospitalet); L.Gayola (ABS Florida Sur. Hospitalet); R.M. Alcolea, T.Rama (ABS Llefiá. Badalona); J.J. Montero (ABS RondaPrim. Mataró); E. Marco (ABS Sarriá de Ter. Girona); C.Carbonell, A. Cama, C. Olmos (ABS Vía Roma. Barcelona);R. Villafáfila, C. Bentue (ABS Viladecans II. Barcelona); G.Amorós, E. Barraquer (CAP Horta Lisboa. Barcelona); M.B.Brun (CAP Montcada i Reixac. Barcelona); M. Abizanda, A.Cervera (CAP Gran Vía. Barcelona); E. Santos, M. Turégano(C.S. Zona Centro. Cáceres); M. Espigares, J. Pozuelos (C.S.La Paz. Badajoz); G. Rodríguez, J.M. Comas (C.S. Puebla deMontalbán. Toledo); E. Magaña, J. Pérez (C.S. Estación“Paseo del Muelle”. Talavera de la Reina); F. Chavida, C.Cancelo (C.S. Brihuega. Guadalajara); F. Laporta (C.S. LaRoda. Albacete); D. Zapatero, M. Sanz, A.C. García-Alvarez(C.S. Avda. de Daroca. Madrid); R. Julián, M.V. Castell (C.S.Peñagrande. Madrid); A. Morón (C.S. El Abajón. Madrid); J.C. Muñoz, S. Tojeiro (C.S. San Fernando. Madrid); M.L.Pascual, I. Nieto (C.S. Paseo de la Chopera. Madrid); J.A.Granados, F. Gómez (C.S. Guayaba. Madrid); O. Ortega, I.Jimeno (C.S. Isla de Oza. Madrid); C. Cámara (C.S. Coronelde Palma. Madrid); C. Onrubia, R. Martín (C.S. JoséAguado. León); M. Borge, C. Gago (C.S. Arturo Eyres Sur.Valladolid); E. Blanco (C.S. Béjar. Salamanca); F. Suárez (C.S. Sur Paulino Prieto. Oviedo); P. Benavides (C.S. Pumarín.Oviedo); R. Villanueva, J.C. de la Fuente (C.S. García Alonso“Bombero Etxaniz”. Bilbao); E. Sampedro, V. Rubio (C.S.Hermanos Iturrino. Irún); M.C. Napal, J.A. Tabar (C.S.Barañain. Navarra); M.D. Martínez (C.S. Rochapea. Pam-plona); T. Sagredo, F.E. Teruel (C.S. Txantrea. Pamplona); M.Flores (C.S. Espartero. Logroño); L.V. Hernández, F. Aganzo(C.S. La Almunia. Zaragoza); R. Córdoba, G. Guillén, E.de la Figuera (C.S. Delicias Sur. Zaragoza).

References

1. Nguyen TV, Center JR, Sambrook PN, Eisman JA (2001) Riskfactor for proximal humerus, forearm, and wrist fractures inelderly men and women. The Dubbo osteoporosis epidemiologystudy. Am J Epidemiol 153:587–595

2. Ismail AA, Pye SR, Cockerill WC, Lunt M, Silman AJ, Reeve J,Banzer D, Benevolenskaya LI, Bhalla A, Bruges J, Cannata JB,Cooper C, Delmas PD, Dequeker J, Dilsen G, Falch JA, Felsch B,Felsenberg D, Finn JD, Gennari C, Hoszowski K, Jajic I, Janott J,Johnell O, Kanis JA, Kragl A, Lopez A, Lorenc R, Lyritis G,Marchand F, Masaryk P, Matthis C, Miazgowski T, Naves-DiazM, Pols HAP, Poor G, Rapado A, Raspe HH, Reid DM, ReisingerW, Scheidt-Nave C, Stepan J, Todd C, Weber K, Woolf AD,O’Neill TW (2002) Incidence of limb fracture across Europe:results from the European Prospective Osteoporosis Study(EPOS). Osteoporos Int 13:565–571

Osteoporos Int (2007) 18:629–639 637

3. Johnell O, Kanis JA, Oden A, Sernbo I, Redlund-Johnell I,Petterson C, De Laet C, Jönsson B (2004) Mortality afterosteoporotic fractures. Osteoporos Int 15:38–42

4. Seeley DJ, Browner WS, Genant HK, Nevitt MC, Scott JC,Cummings SR (1991) Which fractures are associated with lowappendicular bone mass in elderly women? Ann Inter Med115:837–842

5. Stone KL, Seeley DG, Lui LY, Cauley JA, Ensrud K, BrownerWS, Nevitt MC, Cummings SR for the Study of OsteoporosisFractures Research Group (2003) BMD at multiple sites and riskof fracture of multiple types: long-term results from the Study ofOsteoporosis Fractures. J Bone Miner Res 18:1947–1954

6. Kanis JA, Johnell O, Oden A, Dawson A, De Laet C, Jonsson B(2001) Ten year probabilities of osteoporosis fractures accordingto BMD and diagnostic threshold. Osteoporosis Int 12:989–995

7. Cummings SR, Nevitt MC, Browner WS, Stone K, Fox KM,Ensrud KE, Cauley J, Black D, Vogt TM; for The Study ofOsteoporotic Fractures Research Group (1995) Risk factors forhip fracture in white women. N Engl J Med 332:767–773

8. Marshall D, Johnell O, Wedel H (1996) Meta-analysis of howwell measures of bone mineral density predict occurrence ofosteoporotic fractures. BMJ 312:1254–1259

9. Njeh CF, Fuerst T, Diessel E, Genant HK (2001) Is quantitativeultrasound dependent on bone structure?. A reflection. OsteoporosInt 12:1–15

10. Hernández JL, Marín F, González-Macías J, Diez-Perez A, Vila J,Gimenez S, Galán B, Arenas MS, Suárez F, Gayola L, Guillén G,Sagredo T, Belenguer R, Morón A, Arriaza E; for the ECOSAPstudy investigators (2004) Discriminative capacity of calcanealquantitative ultrasound and fracture risk factors in postmenopausalwomen with osteoporotic fractures. Calcif Tissue Inter 74:357–365

11. Glüer CC, Eastell R, Reid DM, Felsenberg D, Roux C, BarkmannR, Timm W, Blenk T, Armbrecht G, Stewart A, Clowes J,Thomasius FE, Kolta S (2004) Association of five quantitativeultrasound devices and bone densitometry with osteoporoticvertebral fractures in a population-based sample: The OPUSStudy. J Bone Miner Res 19:782–793

12. Krieg MA, Cornuz J, Ruffieux C, Sandini L, Buche D,Dambacher MA, Hartl F, Häuselmann HJ, Kraenzlin M, LippunerK, Neff M, Pancaldi P, Rizzioli R, Tanzi F, Theiler R, Tyndall A,Wimpfheimer K, Burckhardt P (2004) Comparison of three boneultrasounds for the discrimination of subjects with and withoutosteoporotic fractures among 7562 elderly women. J Bone MinerRes 18:1261–1266

13. Nguyen TV, Center JR, Eisman J (2004) Bone mineral density-independent association of quantitative ultrasound measurementsand fracture risk in women. Osteoporos Int 15:942–947

14. López-Rodríguez F, Mezquita-Raya P, de Dios Luna J, Escobar-Jiménez F, Muñoz-Torres M (2003) Performance of quantitativeultrasound in the discrimination of prevalent osteoporotic fracturesin a bone metabolic unit. Bone 32:571–578

15. Frost ML, Blake GM, Fogelman I (2002) A comparison offracture discrimination using calcaneal quantitative ultrasoundand dual X-ray absorptiometry in women with a history offracture at sites other than the spine and hip. Calcif Tissue Int71:207–211

16. Hartl F, Tyndall A, Kraenzlin M, Bachmeier C, Guckel C, Senn U,Hans D, Theiler R (2002) Discriminatory ability of quantitativeultrasound parameters and bone mineral density in a population-based sample of postmenopausal women with vertebral fractures:results of the Basel Osteoporosis Study. J Bone Miner Res17:321–330

17. Karlsson MK, Duan Y, Ahlborg H, Obrant KJ, Johnell O, SeemanE (2001) Age, gender, and fragility fractures are associated with

differences in quantitative ultrasound independent of bone mineraldensity. Bone 28:118–122

18. Marín F, González-Macías J, Díez-Pérez A, Palma S, Delgado-Rodríguez M (2006) Relationship between bone quantitativeultrasound and fractures: a meta-analysis. J Bone Miner Res21:1126–1135

19. Glüer CC; for the International Quantitative Ultrasound Consen-sus Group (1997) Quantitative ultrasound techniques for theassessment of osteoporosis: expert agreement on current status. JBone Miner Res 12:1280–1288

20. Díez-Pérez A, Marín F, Vila J, Abizanda M, Cervera A, CarbonellC, Alcolea RM, Cama A, Rama T, Galindo E, Olmos C (2003)Evaluation of calcaneal quantitative ultrasound in a primary caresetting as a screening tool for osteopororosis in postmenopausalwomen. J Clin Densitom 6:237–246

21. von Stetten E, Ouellet H, Wilson KE, Stein JA (1998) Europeancaucasian female reference data values for the Sahara clinicalbone sonometer. Bone 23(Suppl 5):S637. Abstract]

22. Marín F, Vila J, González-Macías J; on behalf the ECOSAPproject investigators (2003) Quantitative bone ultrasound: impactof the application of two different reference databases to apopulation of 5195 postmenopausal women. Med Clin (Barc)121:250–252

23. Eisen EA, Agalliu I, Thurston SW, Coull BA, Checkoway H(2004) Smoothing in occupational cohort studies: an illustrationbased on penalised splines. Occup Environ Med 61:854–860

24. D’Agostino RB Sr, Na BH (2004) Evaluation of the performanceof survival analysis models: discrimination and calibrationmeasures. In: Balakrishnan N, Rao C (eds) Advances in survivalanalysis: handbook of statistics, vol 23. Elsevier, Amsterdam

25. Kanis JA, Black D, Cooper C, Dargent-Molina P, Dawson-HughesB, De Laet C, Delmas P, Eisman J, Johnell O, Jonsson B, Melton L,Oden A, Papapoulos S, Pols H, Rizzoli R, Silman A, Tenenhouse A(2002) A new approach to the development of assessment guide-lines for osteoporosis. Osteoporos Int 13:527–536

26. Robbins JA, Schott AM, Garnero P, Delmas PD, Hans D, MeunierPJ (2005) Risk factors for hip fracture in women with high BMD:EPIDOS study. Osteoporos Int 16:149–154

27. Hans D, Schott AM, Duboeuf F, Durosier C, Meunier PJ; onbehalf of the EPIDOS Group (2004) Does follow-up durationinfluence the ultrasound and DXA prediction of hip fracture? TheEPIDOS prospective study. Bone 35:357–363

28. Krieg MA, Cornuz J, Ruffieux C, Burckhardt P (2004) Rôle desultrasons osseoux pour la prédiction du risque de fracture de lahanche chez la femme âgée de 70 ans ou plus : résultsts de l’étudeSEMOF et comparaison avec les données de la littérature. RevMed Suisse Rom 124:59–62

29. Miller PD, Siris ES, Barrett-Connor E, Faulkner KG, Wehren LE,Abbott TA, Chen YT, Berger ML, Santora AC, Sherwood LM(2002) Prediction of fracture risk in postmenopausal white womenwith peripheral bone densitometry: evidence from the NationalOsteoporosis Risk Assessment. J Bone Miner Res 17:2222–2230

30. Heaney RP (2003) Is the paradigm shifting? Bone 33:457–46531. Sedrine W, Devogelaer JP, Kaufman JM, Goemaere S, Depresseux

G, Zegels B, Deroisy R, Reginster JY (2001) Evaluation of thesimple calculated osteoporosis risk estimation (SCORE) in asample of white women from Belgium. Bone 29:374–380

32. Sedrine WB, Chevallier T, Zegels B, Kvasz A, Micheletti MC,Gelas B, Reginster JY (2002) Development and assessment of theOsteoporosis Index of Risk (OSIRIS) to facilitate selection ofwomen for bone densitometry. Gynecol Endocrinol 16:245–250

33. Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA,Tu JV (2000) Development and validation of the OsteoporosisRisk Assessment Instrument to facilitate selection of women forbone densitometry. CMAJ 162:1289–1294

638 Osteoporos Int (2007) 18:629–639

34. Weinstein L, Ullery B (2000) Identification of at-risk women forosteoporosis screening. Am J Obstet Gynecol 183:547–549

35. Koh LK, Sedrine WB, Torralba TP, Kung A, Fujiwara S, Chan SP,Huang QR, Rajatanavin R, Tsai KS, Park HM, Reginster JY(2001) Osteoporosis Self-Assessment Tool for Asians (OSTA)Research Group. A simple tool to identify Asian women atincreased risk of osteoporosis. Osteoporos Int 12:699–705

36. Albrand G, Muñoz F, Sornay-Rendu E, DuBoeuf F, Delmas PD(2003) Independent predictors of all osteoporosis-related fracturesin healthy postmenopausal women: the OFELY study. Bone32:78–85

37. Black DM, Steinbuch M, Palermo L, Dargent-Molina P, LindsayR, Hoseyni MS, Johnell O (2001) An assessment tool forpredicting fracture risk in postmenopausal women. OsteoporosInt 12:519–528

38. Barr RJ, Adebajo A, Fraser WD, Halsey JP, Kelsey C, Stewart A,Reid DM (2005) Can peripheral DXA measurements be used topredict fractures in elderly women living in the community?.Osteoporos Int 16:1177–1183

39. Kaptoge S, Benevolenskaya LI, Bhalla AK, Cannata JB, BoonenS, Falch JA, Felsenberg D, Finn JD, Nuti R, Hoszowski K,Lorenc R, Miazgowski T, Jajic I, Lyritis G, Masaryk P, Naves-Diaz M, Poor G, Reid DM, Scheidt-Nave C, Stepan JJ, ToddCJ, Weber K, Woolf AD, Roy DK, Lunt M, Pye SR, O’NeillTW, Silman AJ, Reeve J (2005) Low BMD is less predictivethan reported falls for future limb fractures in women acrossEurope: results from the European Prospective OsteoporosisStudy. Bone 36:387–398

40. Kanis JA, Johnell O, De Laet C, Johansson H, Oden A, Delmas P,Eisman J, Fujiwara S, Garnero P, Kroger H, McCloskey EV,Mellstrom D, Melton LJ, Pols H, Reeve J, Silman A, TenenhouseA (2004) A meta-analysis of previous fracture and subsequentfracture risk. Bone 35:375–382

41. Kanis JA, Johansson H, Oden A, Johnell O, De Laet C, EismanJA, McCloskey EV, Mellstrom D, Melton LJ 3rd, Pols HA, ReeveJ, Silman AJ, Tenenhouse A (2004) A family history of fractureand fracture risk: a meta-analysis. Bone 35:1029–1037

42. Kanis JA, Johansson H, Oden A, De Laet C, Johnell O, EismanJA, Mc Closkey E, Mellstrom D, Pols H, Reeve J, Silman A,Tenenhouse A (2005) A meta-analysis of milk intake and fracturerisk: low utility for case finding. Osteoporos Int 16:799–804

43. Bouxsein ML (2003) Bone quality: an old concept revisited.Osteoporos Int 14(Suppl 5):S1–S2

44. Recker R, Lappe J, Davies KM, Heaney R (2004) Boneremodeling increases substantially in the years after menopauseand remains increased in older osteoporosis patients. J BoneMiner Res 19:1628–1633

45. Stauber M, Müller R (2006) Age-related changes in trabecularbone microstructures: global and local morphometry. OsteoporosInt 17:616–626

46. Turner CH (2002) Biomechanics of bone: determinants of skeletalfragility and bone quality. Osteoporosis Int 13:97–104

47. Wang X, Shen X, Li X, Mauli-Agrawal C (2002) Age-relatedchanges in the collagen network and toughness of bone. Bone31:1–7

48. Wickham C, Cooper C, Margetts BM, Baker DJ (1989) Musclestrength, housing and the risk of falls in elderly people. AgeAgeing 18:47–51

49. Ensrud KE, Duong T, Cauley JA, Heaney RP, Wolf RL, Harris E,Cummings SR (2000) Low fractional calcium absorptionincreases the risk for hip fracture in women with low calciumintake. Study of Osteoporotic Fractures Research Group. AnnIntern Med 132:345–353

50. Honkanen RJ, Honkanen K, Kroger H, Alhava E, Tuppurainen M,Saarikoski S (2000) Risk factors for perimenopausal distalforearm fracture. Osteoporos Int 11:265–270

51. Johnell O, Gullberg B, Kanis JA, Allander E, Elffors L, Dequeker J,Dilsen G, Gennari C, Lopes Vaz A, Lyritis G (1995) Risk factors forhip fracture in European women: the MEDOS Study. Mediterra-nean Osteoporosis Study. J Bone Miner Res 10:1802–1815

52. Feskanich D, Willett WC, Colditz GA (2003) Calcium, vitamin D,milk consumption, and hip fractures: a prospective study amongpostmenopausal women. Am J Clin Nutr 77:504–511

53. Dawson-Hughes B (2003) Interaction of dietary calcium andprotein in bone health in humans. J Nutr 133:852S–854S

54. Kanis JA, Johnell O, Oden A, Johansson H, De Laet C, EismanJA, Fujiwara S, Kroger H, McCloskey EV, Mellstrom D, MeltonLJ, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Smokingand fracture risk: a meta-analysis. Osteoporos Int 16:155–162

55. Johansson H, Oden A, Johnell O, Jonsson B, De Laet C, OglesbyA, McCloskey EV, Kayan K, Jalava T, Kanis JA (2004)Optimization of BMD measurements to identify high risk groupsfor treatment. A test analysis. J Bone Miner Res 19:906–913

56. Cheng S, Hans D, Genant HK (1999) Calcaneal quantitativeultrasound systems: gel-coupled. In: Njeh CF, Hans D, Fuerst T,Glüer CC, Genant HK (eds) Quantitative ultrasound: assessmentof osteoporosis and bone status. Martin Dunitz Ltd, London

57. McGrother CW, Donaldson MMK, Clayton D, Abrams KR,Clarke M (2002) Evaluation of a hip fracture risk score forassessing elderly women: the Melton Osteoporotic Fracture(MOF) Study. Osteoporos Int 13:89–96

58. Burger H, de Laet CE, Weel AE, Hofman A, Pols HA (1999)Added value of bone mineral density in hip fracture risk scores.Bone 25:369–374

59. Dargent-Molina P, Douchin MN, Cormier C, Meunier PJ, BreartG; EPIDOS Study Group (2002) Use of clinical risk factors inelderly women with low bone mineral density to identify womenat higher risk of hip fracture: the EPIDOS prospective study.Osteoporos Int 13:593–599

60. MedSurf’s Remaining Lifetime Fracture Probability. Available at:http://www.medsurf.com/RLFP_web_demo.php/. Accessed May16, 2006

61. Hip fracture risk calculator for healthy postmenopausal women.Available at: http://apps.sbgh.mb.ca/fracture/. Accessed May 16,2006

62. Ettinger B, Hillier TA, Pressman A, Che M, Hanley DA (2005)Simple computer model for calculating and reporting 5-yearosteoporotic fracture risk in postmenopausal women. J WomenHealth 14:159–171

63. Wilson PW, D’Agostino RB, Levy D, Belanger AM, SilbershatzH, Kannel WB (1998) Prediction of coronary heart disease usingrisk factor categories. Circulation 97:1837–1847

64. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, DeBacker G, De Bacquer D, Ducimetiere P, Jousilahti P, Keil U,Njolstad I, Oganov RG, Thomsen T, Tunstall-Pedoe H, TverdalA, Wedel H, Whincup P, Wilhelmsen L, Graham IM, SCOREproject group (2003) Estimation of ten-year risk of fatalcardiovascular disease in Europe: the SCORE project. EurHeart J 24:987–1003

65. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C,Mulvihill JJ (1989) Projecting individualized probabilities ofdeveloping breast cancer for white females who are beingexamined annually. J Natl Cancer Inst 81:1879–1886

66. National Osteoporosis Foundation. Physician’s Guide to Preven-tion and Treatment of Osteoporosis. Available at: http://www.nof.org/professionals/clinical.htm/. Accessed May 16, 2006

Osteoporos Int (2007) 18:629–639 639