Authors: T. J. Vokes, D. L. Gillen
Categories: Original Article, Bone densitometry, FRAX, Osteoporosis, VFA, Vertebral fractures
Source: Osteoporosis International
Authors: T. J. Vokes, D. L. Gillen
Vertebral fracture assessment (VFA) is a new method for imaging thoracolumbar spine on bone densitometer. Among patients referred for bone densitometry, the selection of patients for VFA testing can be optimized using an index derived from clinical risk factors and bone density measurement.
VFA, a method for imaging thoracolumbar spine on bone densitometer, was developed because vertebral fractures, although common and predictive of future fractures, are often not clinically diagnosed. The study objective was to develop a strategy for selecting patients for VFA.
A convenience sample from a university hospital bone densitometry center included 892 subjects (795 women) referred for bone mineral density (BMD) testing. We used questionnaires to capture clinical risk factors and dual-energy X-ray absorptiometry to obtain BMD and VFA.
Prevalence of vertebral fractures was 18% in women and 31% in men (p = 0.003 for gender difference). In women, age, height loss, glucocorticoid use, history of vertebral and other fractures, and BMD T-score were significantly and independently associated with vertebral fractures. A multivariate model which included above predictors had an area under the receiver operating curve of 0.85 with 95% confidence interval (CI) of 0.81 to 0.89. A risk factor index was derived from the above multivariate model. Using a level of 2 as a cut-off yielded 93% sensitivity (95% CI 87, 96) and 48% specificity (95% CI 69, 83). Assuming a 15% prevalence of vertebral fractures, this cut-off value had a 24% positive and 97% negative predictive value and required VFA scanning of three women at a cost of 20 cost/VFA scan) to detect one with vertebral fracture(s).
Selecting patients for VFA can be optimized using an index derived from BMD measurement and easily obtained clinical risk factors.
Vertebral fractures are the most common osteoporotic fractures. They are important to detect because they are associated with significant morbidity, mortality, and reduced quality of life [1–3], and because they strongly predict future fractures [4–7]. Furthermore, the increase in fracture risk associated with vertebral fractures is independent of, and additive to, bone mineral density (BMD) measurement [7–9]. Therefore, having information about vertebral fractures in conjunction with BMD allows clinicians to better assess fracture risk and select appropriate therapies. Because only one third of vertebral fractures found on radiographs are clinically diagnosed [10–12], imaging is necessary for their detection. This has required radiographs which are usually not obtained in the course of clinical evaluation of osteoporosis. Further, even when vertebral fractures are present on radiographs, they are often not recognized by the reporting radiologist and do not lead to the diagnosis and appropriate treatment of osteoporosis [12, 13]. Recognition of the importance of vertebral fractures for osteoporosis care, coupled with the realization that they are often not clinically apparent, has led to the development of vertebral fracture assessment (VFA). VFA is a method for imaging the thoracolumbar spine on bone densitometers, usually obtained at the time of BMD measurement. This rapid and simple procedure is associated with low cost and radiation exposure, and has a reasonably good ability to detect vertebral fractures (reviewed in [14]).
However, it is not clear how to best select patients for VFA imaging, maximizing the detection of vertebral fractures yet minimizing scanning of subjects in whom finding a fracture is unlikely. The International Society for Clinical Densitometry (ISCD) has formulated recommendations for selecting patients for VFA [14], though such recommendations have not been tested in practice. Therefore, we set out to determine which patients among those who present for BMD measurement should have VFA imaging. We postulated that the information needed for decision making should be easily obtained through a short interview or intake questionnaire to permit its eventual use in a busy densitometry practice. We included risk factors such as age, history of fractures, and height loss, which were found in population studies to best identify subjects with vertebral fractures on radiographs [15, 16]. We also added the results of BMD measurement, since it is readily available at the time of VFA testing, and the history of glucocorticoid use, which is associated with increased risk of vertebral fractures [17–19] and is a common indication for BMD testing.
The study was approved by the University of Chicago’s Institutional Review Board and all participants signed a written informed consent. A convenience sample included 974 subjects (869 women) recruited when they presented for BMD measurement as part of their clinical care between 2001 and 2007. The densitometry facility performs all BMD testing at the University of Chicago, and patients are referred mostly by University of Chicago faculty. The patients come from the geographic area around the campus to receive their primary care at the University of Chicago or from the Metropolitan Chicago Area and Northwest Indiana for tertiary care. It is not known which of the study subjects, or densitometry patients in general, belong to which of these groups, as they cannot be strictly defined by geography. There were no specific criteria for including patients in the study—it required that the study personnel be present and that the subjects consent to participate.
The subjects completed a questionnaire which included information on personal and family history of fractures and their circumstances, young adult height and weight, medical history, medication use, and personal habits such as smoking, alcohol consumption, calcium intake, and activity level. Height and weight were measured using standard clinic equipment. Using this information, we also calculated the 10-year probability of major osteoporotic fractures using the version 3 of FRAX^®^ web-based tool [20].
VFA images and BMD measurements of the lumbar spine and proximal femur were obtained by two ISCD-certified technologists using a Prodigy densitometer (GE Medical Systems, Madison, WI, USA). All VFA images were evaluated by one ISCD-trained clinician (TJV) using Genant semi-quantitative approach [21] as recommended by the ISCD [14, 22] where vertebra with a fracture on visual inspections is assigned the following grade 1 (mild) fracture represents a reduction in vertebral height of 20–25%; grade 2 (moderate) a reduction of 26–40%; and grade 3 (severe) a reduction of over 40%. A subject in the vertebral fracture group had at least one grade 2 fracture or two grade 1 fractures. The main analysis was performed after excluding subjects with a single grade 1 fracture (N = 31) because it is often not clear whether these represent true fractures or non-fracture deformities, because grade 1 fractures are not as clearly predictive of future fractures as are higher grades [23], and because they are often difficult to conclusively diagnose on VFA [14, 22, 24].
Height loss was calculated by subtracting the measured height from the self-reported young adult height. Self-reported vertebral fractures were present if the subject reported spine or vertebral fractures (excluding neck or cervical fractures) in response to the question “have you had any broken bones”. Non-vertebral (peripheral) fracture was defined as any fracture occurring after age 25, in the course of usual physical activity, excluding fractures of the face, fingers, and toes, or those resulting from a motor vehicle accident. Glucocorticoid use (systemic but not inhaled) was defined as at least 5 mg/day of prednisone or equivalent for at least 3 months (cumulative exposure equivalent to at least 0.450 g of prednisone), as recommended by the American College of Rheumatology [25]. For BMD measurement, the lower of the lumbar spine or proximal femur T-score (femoral neck or total hip) was used for analysis as recommended by the ISCD [26].
All analyses were performed using STATA statistical software package [27]. The differences in the clinical characteristics and risk factors between men and women and between subjects with and without vertebral fractures were compared using t tests for continuous variables and chi-square tests for categorical variables. The association between vertebral fracture and risk factors was modeled using logistic regression. Given the known gender differences in prevalence of and risk factors for vertebral fractures, all analyses were a priori stratified by gender. For 173 subjects who did not provide information on young adult height, height loss was imputed via multiple imputation [28] using linear regression estimates based on measured current height, age, race, and gender. Standard errors for model estimates accounted for multiple imputation of height loss [28]. While an increase in precision was observed using the imputed data (more narrow confidence intervals), no substantial differences in the estimates associated with modeled covariates were observed (i.e., the odds ratios, OR, for each predictor were not different with or without imputed values). Prediction models for fracture risk were constructed utilizing data on a random sample consisting of two thirds of the original study cohort. Goodness-of-fit tests for predictive models were carried out using the Hosmer–Lemeshow goodness-of-fit statistic for binary regression [29]. Out-of-sample performance of the resulting predictive models was assessed using the remaining one third of the originally study cohort as a validation sample.
Among the 974 subjects who consented to participate in the study, 51 were excluded from analysis because they had un-interpretable VFAs, and 31 because they had a single grade 1 fracture, leaving 892 (795 women) subjects for analysis. (Including patients with grade 1 fractures in the fracture group resulted in qualitatively similar conclusions but lower strength of association between vertebral fractures and risk factors.) The clinical characteristics of the participants are shown in Table 1. Women with and without fractures were significantly different in all of the risk factors of interest (Table 1). A higher percentage of women with fractures were receiving pharmacologic therapy for osteoporosis, although this difference was not significant when controlling for presence of osteoporosis by BMD criteria. Table 1Clinical characteristics of women and men with and without vertebral fracturesWomen (n = 795)Men (n = 97)Vertebral fracturesVertebral fracturesCharacteristicNoYes p value^a^ NoYes p value^a^ (n = 649)(n = 146) (n = 67)(n = 30) Age, years61.2 (19–92)70.5 (20–95)<0.000158.1 (20–90)63.1 (34–87)0.15Race African210 (81%)49 (19%)0.2116 (73%)6 (27%)0.42 Caucasian398 (82%)88 (18%)48 (69%)22 (31%) Hispanic12 (67%)6 (33%)1 (33%)2 (67%) Asian29 (91%)3 (9%)2 (100%)0 (0%)BMD T-score^b^ −2.2 (−6 to 2.1)−3.0 (−5.2 to 0)<0.0001−2.1 (−3.9 to 0.9)−3.0 (−5.2 to −0.5)0.0001Lumbar spine−1.5 (−5.3 to 3.2)−2.1 (−5.2 to 2.4)<0.0001−1.2 (−3.9 to 2.6)−2.5 (−5.2 to 2.1)0.0002Femoral neck−2.0 (−6.0 to 2.3)−2.7 (−4.9 to 0.3)<0.0001−1.8 (−3.5 to 2.2)−2.5 (−4.2 to −0.3)0.002Total hip−1.4 (−5.3 to 3.1)−2.2 (4.6 to 0.7)<0.0001−2.3 (−4.3 to −0.3)−2.3 (−4.3 to −0.3)0.001Heel−0.8 (−4 to 4.5)−1.5 (−4.1 to 1.7)<0.0001−1.1 (−4.2 to 2.8)−1.9 (−4.8 to 2.1)0.018Height loss, inches0.9 (0–7)2.0 (0–7)<0.00011.3 (0–6)1.9 (0–7)0.04Non-vertebral fractures143 (22%)63 (45%)<0.00114 (22%)4 (13%)0.34Self-reported vertebral fractures5 (0.8%)35 (24%)<0.0010 (0.0%)7 (23%)<0.001Glucocorticoid use99 (15%)40 (27%)<0.00127 (40%)10 (33%)0.51Height, inches63.3 (51–73)61.6 (53–69)<0.00168.5 (62–74)67.4 (61–74)0.15Weight, pounds152 (74–300)145 (80–255)0.025181 (119–284)171 (112–283)0.22Osteoporosis therapy235 (36%)70 (48%)0.00821 (31%)10 (33%)0.85Results are given as mean (range) for continuous variables and number (%) for categorical variables ^a^ p values were derived from t test for continuous variables and chi-square test for categorical variables ^b^Lowest of lumbar spine, femoral neck, or total hip T-score
Age was a significant predictor of vertebral fractures alone and when controlled for BMD T-score (Table 2). The prevalence of vertebral fractures did not increase until age 60 (Fig. 1a) but then approximately doubled with each decade, with a progressive increase in probability of fracture with increasing age (Table 3). Based on this observation, the variable we used was “age over 50”. BMD T-score was a significant predictor of fractures with approximate doubling of the probability of having vertebral fractures for each 1 unit decrease in the T-score, particularly below −2 (Fig. 1b, Tables 2 and 3). The association of vertebral fractures with BMD was diminished but not eliminated when age was added to the model (Table 2). Compared to those with normal BMD, the risk of having vertebral fractures was significantly higher in women with osteoporosis but not in those with osteopenia (Table 3), with the probability of fracture approximately doubling for 1 unit decrease in T-score below −2 (Fig. 1b and Table 3). Height loss was also associated with vertebral fractures (Table 2) even when controlling for age and BMD, with prevalence of vertebral fractures doubling for each inch of height loss above 1 in. (Fig. 1c and Table 3). Use of glucocorticoids was a significant predictor of vertebral fractures with the strength of association increasing when age was added in the model (Table 2). Table 2Association of risk factors and prevalent vertebral fractures in women, expressed as odds ratio of having a fracture, derived from logistic regression with presence of vertebral fractures as a binary outcome and each risk factor alone or when controlled for other risk factors, all risk factors combined, or FRAXOR (95% CI) p valueROC (95% CI) Individual risk factors Age/decade1.9 (1.6, 2.2)<0.001Age/decade over 502.1 (1.8, 2.6)<0.0010.719 (0.67, 0.76) Age over 50 controlled for BMD1.9 (1.5, 2.3)<0.001BMD T-score/1 unit decrease1.9 (1.6, 2.3)<0.0010.679 (0.63, 0.73) Controlled for age over 501.6 (1.3, 1.9)<0.001Height loss/1 in.1.7 (1.5, 1.9)<0.0010.689 (0.64, 0.74) Controlled for age over 501.4 (1.2, 1.6)<0.001 Controlled for BMD1.6 (1.4, 1.8)<0.001 Controlled for age over 50 and BMD1.4 (1.2, 1.6)<0.001Glucocorticoid use2.1 (1.3, 2.7)0.0010.561 (0.52, 0.60) Controlled for age over 503.2 (2.0, 5.1)<0.001 Controlled for BMD2.1 (1.3, 3.2)0.001 Controlled for age over 50 and BMD3.0 (1.9, 4.8)<0.001Non-vertebral fracture2.8 (1.9, 4.1)<0.0010.612 (0.57, 0.66) Controlled for age over 502.5 (1.6, 3.7)<0.001 Controlled for BMD2.2 (1.5, 3.3)<0.001 Controlled for age over 50 and BMD2.2 (1.4, 3.3)<0.001Self-reported vertebral fracture41 (16, 106)<0.0010.616 (0.58, 0.65) Controlled for age over 5065 (23, 183)<0.001 Controlled for BMD37 (14, 99)<0.001 Controlled for age over 50 and BMD59 (21, 168)<0.001 Combined risk factors Age/decade over 502.1 (1.7, 2.7)<0.001 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \left. {\begin{array}{*{20}{c}} {} \hfill \{} \hfill \{} \hfill \{} \hfill \{} \hfill \{} \hfill \\end{array} } \right}\quad {\hbox{0}}{\hbox{.850}},\left( {{\hbox{0}}{\hbox{.81,}},0.89} \right)
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