Published online before print May 15, 2008, 10.1148/radiol.2481070986
(Radiology 2008;248:179-184.)
© RSNA, 2008
Osteoporotic Fracture Risk in Elderly Women: Estimation with Quantitative Heel US and Clinical Risk Factors1
Idris Guessous, MD,
Jacques Cornuz, MD, MPH,
Christiane Ruffieux, PhD,
Peter Burckhardt, MD, and
Marc-Antoine Krieg, MD
1 From the Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland (I.G., J.C., M.A.K.); University Institute of Social and Preventive Medicine (I.G., J.C., C.R.) and Department of Community Medicine and Public Health (J.C.), University of Lausanne, Rue du Bugnon 17, 1005 Lausanne, Switzerland; and Department of Medicine, Bois-Cerf Clinic, Lausanne, Switzerland (P.B.). Received June 8, 2007; revision requested August 13; revision received October 9; accepted December 18; final version accepted January 5, 2008. The Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk study was initiated by the Swiss Federal Office for Social Security and funded by the Concordat des Caisses-Maladies Suisses.
Address correspondence to I.G. (e-mail: idris.guessous{at}chuv.ch).
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ABSTRACT
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Purpose: To derive a prediction rule by using prospectively obtained clinical and bone ultrasonographic (US) data to identify elderly women at risk for osteoporotic fractures.
Materials and Methods: The study was approved by the Swiss Ethics Committee. A prediction rule was computed by using data from a 3-year prospective multicenter study to assess the predictive value of heel-bone quantitative US in 6174 Swiss women aged 70–85 years. A quantitative US device to calculate the stiffness index at the heel was used. Baseline characteristics, known risk factors for osteoporosis and fall, and the quantitative US stiffness index were used to elaborate a predictive rule for osteoporotic fracture. Predictive values were determined by using a univariate Cox model and were adjusted with multivariate analysis.
Results: There were five risk factors for the incidence of osteoporotic fracture: older age (>75 years) (P < .001), low heel quantitative US stiffness index (<78%) (P < .001), history of fracture (P = .001), recent fall (P = .001), and a failed chair test (P = .029). The score points assigned to these risk factors were as follows: age, 2 (3 if age > 80 years); low quantitative US stiffness index, 5 (7.5 if stiffness index < 60%); history of fracture, 1; recent fall, 1.5; and failed chair test, 1. The cutoff value to obtain a high sensitivity (90%) was 4.5. With this cutoff, 1464 women were at lower risk (score, <4.5) and 4710 were at higher risk (score, 
4.5) for fracture. Among the higher-risk women, 6.1% had an osteoporotic fracture, versus 1.8% of women at lower risk. Among the women who had a hip fracture, 90% were in the higher-risk group.
Conclusion: A prediction rule obtained by using quantitative US stiffness index and four clinical risk factors helped discriminate, with high sensitivity, women at higher versus those at lower risk for osteoporotic fracture.
© RSNA, 2008
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INTRODUCTION
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Osteoporosis is characterized by low bone mass and microarchitectural deterioration of bone tissue, which leads to increased bone fragility and increased risk of fracture (1). Currently, diagnosis of osteoporosis is supported by using dual x-ray absorptiometry (DXA) measured at the hip and/or lumbar spine and defined as a bone mineral density (BMD) that is 2.5 standard deviations or more below the average value for young healthy women (2). Osteoporosis is a major public health issue, and the incidence of osteoporosis is expected to increase in association with the worldwide aging of the population (3,4). For example, the incidence of hip fracture is predicted to increase fourfold by 2050 (5). Because the incidence of osteoporosis is expected to outpace economic resources (5), it is crucial to develop improved detection methods that can identify those women who need DXA measurement and will potentially benefit from treatment. One potential approach is to use quantitative ultrasonography (US) of the heel. Quantitative US of the heel is noninvasive, free of radiation, and relatively inexpensive; moreover, it helps predict fracture risk independently of DXA (6,7).
We hypothesized that a simple score incorporating clinical risk factors of osteoporotic fractures and bone status assessed by using quantitative US of the heel could be used to help predict the risk of osteoporotic fractures. Thus, the purpose of our study was to derive a prediction rule by using prospectively obtained clinical and bone US data to identify elderly women at risk for osteoporotic fractures.
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MATERIALS AND METHODS
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Women Included
We used data from the 3-year prospective multicenter Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk (SEMOF) study (8) (Table 1). The aim of the SEMOF study was to compare the predictive value of three quantitative US devices for hip fractures in a group of 7609 elderly Swiss women aged 70 years and older. Ten major Swiss osteoporosis centers, representing the various parts of the country, participated in the SEMOF study. The purpose of our current study was part of the original SEMOF study and was accepted by the Swiss Ethics Committee of Medical Sciences. All women gave written consent before being enrolled. Exclusion criteria were history of hip fracture, bilateral hip replacement, renal failure, and active cancer or dementia. Recruitment and sampling are described elsewhere (8). Four hundred ninety-five women did not answer the 6-month questionnaire and were considered lost to follow-up, which left 7114 women eligible for analysis. We excluded 930 women because of missing data and 10 women who were older than 85 years of age, which left 6174 women (age range, 70–85 years). There were no differences between the excluded women and those included in our study with regard to mean quantitative US stiffness index (two-sample t test, P = .08), proportion of current smokers (Pearson 
2, P = .20), and fracture history (Pearson 2, P = .50). There was a slight difference in age between the included and excluded groups (75.1 years ± 3.1 [standard deviation] vs 75.8 years ± 3.3; two-sample t test, P < .001), but the incidence rate ratio of osteoporotic fracture was not statistically different from 1, which indicates a similar incidence rate between the two groups (incidence rate ratio = 0.78; 95% confidence interval: 0.54, 1.11). Mean follow-up was 2.8 years, for a total of 17 546 person-years.
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Table 1. Baseline Characteristics and Prevalence of Risk Factors for Osteoporotic Fracture in 6174 Women in SEMOF Study
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Data Collected
Socioeconomic and clinical data had been obtained with a face-to-face interview performed by trained research assistants. Height, weight, body mass index, and results of chair testing (Figure) were collected. A quantitative US device (Achilles+; GE-Lunar, Madison, Wis) was used to assess two parameters of bone status at the heel: broadband ultrasound attenuation (in decibels per megahertz) and the speed of sound (in meters per second). These two parameters were combined to derive a third parameter, the stiffness index, expressed as a percentage of the mean value for a young adult reference population. The smaller the quantitative US stiffness index, the higher the fracture risk (9).
Briefly, the device (Achilles+) is a water-bath quantitative US system into which the subject places his or her heel. The device generates a band of frequencies from 200 to 600 kHz. The quantitative US stiffness index is automatically calculated by the software utilizing the following equation: quantitative US SI = (0.67 · BUA) + (0.28 · SOS) – 420, where SI is stiffness index, BUA is broadband ultrasound attenuation, and SOS is speed of sound. After a precise positioning of the foot into the device by the operator, the whole measurement procedure is automatic. Before the onset of the study, all operators (physicians and nurses) attended a 2-day training course. The stability of the devices was repeatedly and regularly controlled during the entire study by using the phantoms delivered by the manufacturer. Controls of stability were performed every week, as recommended by the manufacturers when quantitative US is used in clinical routine. During the first weeks of the inclusion phase of the study, the in vivo short-term precision (coefficient of variation) was assessed by obtaining 277 duplicate measurements in a subgroup of women included in the study. The coefficient of variation was calculated by using root-mean-square averages. The coefficient of variation was 2% for broadband ultrasound attenuation, 0.3% for speed of sound, and 2.2% for stiffness index. All the numeric data were regularly sent by the 10 centers to the coordinating center and were centralized in an Access database (MS Office; Microsoft, Redmond, Wash).
Fracture of the hip, wrist, or arm after low-grade trauma was considered osteoporotic. Low-grade trauma fractures were considered spontaneous or consequent to a fall from a standing height or less. Every 6 months during follow-up, each participant received by mail a questionnaire registering any changes in medical conditions during the intervals, particularly any illness, modification of medication, or fracture, with its precise localization and trauma level. A medical report from the physician in charge of the participant was obtained for each reported fracture.
During follow-up, 317 women reported a fracture; the incidence was 17 per 1000 women-years. The incidence of fracture among excluded women was similar (14 per 1000 women-years).
Predictive Rule and Statistical Analysis
To elaborate a predictive rule, two authors (I.G., M.A.K.) considered the following risk factors for osteoporotic fractures and falls: low quantitative US stiffness index (6–9), older age (10), low body mass index (11), history of fracture (12,13), maternal hip fracture (14), recent fall (within past 12 months) (15), failed chair test (unable to rise from a chair three times in succession without using arms) (16), current smoking habit (17), early menopause (before age 45 years), and surgical menopause (18). A univariate Cox model was used to determine which risk factors predict fracture. Their predictive value was then adjusted with a multivariate analysis. Results are expressed as hazard ratios and P values.
Four authors (I.G., C.R., J.C., M.A.K.) computed a score from the Cox regression model and assigned points in proportion to the β coefficients. We selected a cutoff value with 90% sensitivity for the identification of women who had an osteoporotic fracture. We used bootstrap simulations to evaluate the stability of the score by repeating its construction in 500 bootstrap samples. By using samples identical in size to the original sample, we calculated the score distribution, as well as the standard errors for sensitivity and specificity (19).
Three age classes were considered: 70–74 years, 75–79 years, and 80–85 years. In accordance with Hans et al's work (20), quantitative US stiffness index values were divided into three classes, with a threshold value of 78 and 60, which correspond to DXA T scores at the femoral neck of –1 and –2.5, respectively. Statistical analyses were performed by using software (Stata, version 8.0; StataCorp, College Station, Tex). P values less than .05 were considered to indicate significant differences.
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RESULTS
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Risk Factors
At the univariate analysis, more advanced age (P < .001), low heel quantitative US stiffness index (P < .001), history of fracture (P = .001), a failed chair test (P = .029), and recent fall (P = .001) were significant predictors of osteoporotic fractures. The magnitude of the associations was generally smaller after multivariate analysis. However, according to multivariate analysis, a quantitative US stiffness index lower than 60 increases the rate of fractures by fourfold (P < .001). Although not statistically significant at multivariate analysis, a failed chair test and a history of fracture increase the rate of fracture by 27% (P = .188) and 23% (P = .070), respectively (Table 2). The points assigned to each risk factor are shown in Table 3, with the lowest score in our sample being 0 and the highest score being 14.
Predictive Rule Results
For each estimation of risk for osteoporotic fractures, the presence of risk factors was assessed and the predictive rule was obtained by computing the sum of the assigned points. The scores occupied a range between 0 (the lowest risk) and 14 (the highest risk), depending on the clinical case. The cutoff value yielding 90% sensitivity was 4.5. By using this cutoff, 76.3% (4710 of 6174) of women in the final study population were considered to be at higher risk (score, 4.5), and 23.7% (1464 of 6174) were considered to be at lower risk (score, <4.5). Osteoporotic fracture was found in 19.8% (290 of 1464) of women at higher risk but in only 1.8% (27 of 1464) of women at lower risk (Table 4). By using the bootstrap method, we found that the sensitivity of the score varied from 85% to 95% (mean, 90%), and specificity varied from 21% to 25% (mean, 23%).
The predictive value of the score remained stable irrespective of the distribution of the population of elderly women. Moreover, the 90% sensitivity of the score was preserved when predicting the incidence of a specific osteoporotic fracture (ie, hip fracture). Indeed, among women who had a hip fracture (n = 66), six were in the lower-risk group and 60 (90%) were in the higher-risk group (Table 5). The negative predictive values of the scores for osteoporotic fracture and hip fracture were very high (>98%), such that a score lower than 4.5 confidently ruled out the risk of fracture.
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DISCUSSION
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Results from our study involving 6174 participants during a 2.8-year follow-up showed that five risk factors are predictors of osteoporotic fracture: four clinical factors (older age, history of fracture, a failed chair test, a recent fall) and low heel quantitative US stiffness index. On the basis of these risk factors, we constructed a highly sensitive prediction rule for clinical screening in women at risk for osteoporotic fracture in the next 3 years.
Bone status is usually described by its density or its quality (21). According to the World Health Organization definition, BMD is a quantitative measurement used to characterize bone status and to diagnose osteoporosis (2). Moreover, BMD is also one of the major predictors of fracture. BMD is usually measured with central DXA; the femoral neck is considered an eligible site for BMD assessment (22). Since 2002, several organizations, including the National Osteoporosis Foundation and the U.S. Preventive Service Task Force, have recommended that women aged 65 years and older and women aged 60 years and older with risk factors for osteoporotic fracture undergo routine screening for osteoporosis (23,24). However, because of suboptimal provision of DXA in not only nonoccidental countries (25) but also most European countries, and certainly in the United States, this strategy cannot be implemented (26). Because of this, fracture prediction tools that use only clinical risk factors and not bone status (BMD measured by using DXA) have been investigated, but because bone status is a powerful predictor of fracture risk by itself (27), these tools might be less accurate to predict the risk of fracture. Scores and assessment tools that use clinical predictors of fracture or the likelihood of having a low BMD have been previously developed (28–31). The FRACTURE Index is based on a similar approach but includes hip BMD (32).
Compared with DXA, quantitative US presents several advantages. First, it is well established that quantitative US can be used to assess not only bone density but also other parameters such as architecture or elasticity, which may contribute to bone quality (33). Second, a low quantitative US value is an independent risk factor for osteoporotic fracture in peri- and postmenopausal women (34). Third, US devices would certainly be easier to adapt to the growing demand, particularly in countries where mass screening has been recommended (eg, United States), because US is a portable and relatively inexpensive technique.
What are the implications of our prediction rule with the use of quantitative US? Results of a cohort study (35) of clinical risk factors determined that BMD testing is required in only a minority of women. Similarly, Kanis and Johnell (26) demonstrated by using a variety of scenarios, particularly a case-finding strategy involving clinical risk factors and then selective use of BMD, that the majority of women do not require DXA. Although there are conflicting data regarding their cost-effectiveness (32,36–38), strategies involving quantitative US have been shown to be useful in preselecting postmenopausal women with indications for DXA. It may also be an option in circumstances in which DXA equipment is lacking (21,22).
Our study had some limitations. First, the 23% specificity obtained with our score may be deemed suboptimal. However, the objective when elaborating our score was to improve the detection of women who need additional procedures, such as densitometry and preventive intervention. Therefore, as with all screening tests, this method must privilege sensitivity (the true-positive rate) rather than specificity. On the basis of the performance of other screening tests, including the sensitivity of first screening mammography for women older than 70 years of age (39), we chose a 90% sensitivity cutoff. Other scores specific to osteoporotic fracture (eg, Simple Calculated Osteoporosis Risk Estimation and Osteoporosis Risk Assessment Instrument) have similar specificities (29,31). Second, our analysis included neither women with secondary osteoporosis nor women older than 85 years of age, and our prediction rule may not be used with these populations. Further studies should be performed to develop prediction rules for these groups.
Another concern may be the score items themselves. We voluntarily integrated risk factors for fall, such as the chair test and recent fall, as it seemed to us essential to assess a global risk of osteoporotic fracture. Indeed, interventions such as muscle strengthening, balance retraining, withdrawal of psychotropic medication, and cardiac pacing reduce fracture incidence in elderly people (40). Other interventions, such as hip protectors and correction of visual deficiency, still need to be evaluated. Therefore, systematic use of prediction rule can help physicians to detect women at higher risk for osteoporotic fractures and, depending on which determinants predominate, to identify the mechanism underlying fracture. This will improve identification of the appropriate intervention (ie, DXA, biphosphonate treatment, and hip protectors).
Despite the evidence-based efficacy of treatments, patients with osteoporosis are not optimally treated. Experts have postulated two major reasons (26). The first is lack of general awareness, including that of physicians; the second is the heterogeneity with regard to access to DXA. Our prediction rule is a simple tool that can be applied systematically in the evaluation of elderly patients. Moreover, integration of heel quantitative US parameters may be an effective alternative to DXA in response to the expected growth in demand for osteoporosis management in the next decades.
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ADVANCES IN KNOWLEDGE
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- Five risk factors (older age, history of fracture, a failed chair test, a recent fall, and low heel quantitative US stiffness index) are predictors of osteoporotic fracture.
- A low quantitative bone US stiffness index increased the risk of fractures by fourfold.
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IMPLICATIONS FOR PATIENT CARE
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- Heel quantitative US, a noninvasive, ionizing radiation–free examination, may be an effective alternative to dual x-ray absorptiometry (DXA).
- A prediction rule may be used to better select women who need DXA examination.
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FOOTNOTES
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Abbreviations: BMD = bone mineral density • DXA = dual x-ray absorptiometry • SEMOF = Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk
Author contributions: Guarantors of integrity of entire study, I.G., M.A.K.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, I.G., M.A.K.; clinical studies, J.C., P.B., M.A.K.; statistical analysis, I.G., J.C., C.R., M.A.K.; and manuscript editing, all authors
Authors stated no financial relationship to disclose.
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ABSTRACT
INTRODUCTION
