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Osteoporosis screening in postmenopausal women aged 50–64 years: BMI alone compared with current screening tools
Accepted Manuscript Title: Osteoporosis screening in postmenopausal women aged 50–64 years: BMI alone compared with current screening tools Author: Xuezhi Jiang Lauren E. Good Ryan Spinka Peter F. Schnatz PII: DOI: Reference:
S0378-5122(15)30058-X http://dx.doi.org/doi:10.1016/j.maturitas.2015.09.009 MAT 6488
To appear in:
Maturitas
Received date: Revised date: Accepted date:
20-7-2015 25-9-2015 28-9-2015
Please cite this article as: Jiang Xuezhi, Good Lauren E, Spinka Ryan, Schnatz Peter F.Osteoporosis screening in postmenopausal women aged 50ndash64 years: BMI alone compared with current screening tools.Maturitas http://dx.doi.org/10.1016/j.maturitas.2015.09.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Osteoporosis Screening in Postmenopausal Women Aged 50-64 Years: BMI alone compared with current screening tools Running Title: BMI and Osteoporosis Screening Xuezhi Jiang, M.D., Ph.D.1,3* [email protected], Lauren E. Good1, Ryan Spinka, B.S.1, Peter F. Schnatz, D.O.1,2,3,4 1
Reading Hospital, Departments of Obstetrics and Gynecology, Reading, PA
2
Reading Hospital, Departments of Internal Medicine, Reading, PA
3
Sidney Kimmel Medical College of Thomas Jefferson University, Departments of Obstetrics and Gynecology, Philadelphia, PA 4
Sidney Kimmel Medical College of Thomas Jefferson University, Departments of Internal Medicine, Philadelphia, PA
*
Corresponding author at: Assistant Professor of OBGYN, Sidney Kimmel Medical College of Thomas Jefferson University, Reading Hospital; Department of ObGyn – R1; P.O. Box 16052 Reading, PA 19612-6052. Tel.: 484-628-8827; fax: 484-628-9292.
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Highlights
We compared the predictive performance of BMI alone with 5 screening modalities in identifying early postmenopausal women with osteoporosis. BMI (< 28) had a comparable numerical screening performance overall to the current screening modalities. However, a better osteoporosis screening tool remains to be developed.
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Abstract Objectives: Consensus on when to initiate DXA screening for early postmenopausal women (<65 years of age) is lacking. Low Body Mass Index (BMI) has been proposed as one of the major risk factors for osteoporosis. This study sought to compare the predictive performance of BMI alone with 5 screening modalities (the U.S. Preventive Services Task Force [USPSTF] FRAX threshold of 9.3%, a risk factor based approach [≥ 1 risk factors], the Osteoporosis Self-Assessment Tool [OST <2], the Osteoporosis Risk Assessment Instrument [ORAI ≥9], and the Simple Calculated Osteoporosis Risk Estimation [SCORE ≥ 6]) in identifying early postmenopausal women with osteoporosis. Methods: Postmenopausal women aged 50-64 years presenting for a DXA test were recruited between January 1, 2007, and March 1, 2009. Demographic data and osteoporosis risk factors were obtained through a telephone survey. The performance of each screening tool in predicting osteoporosis was compared. Results: Of 445 study participants, 95% were White, 38 had osteoporosis (T-score ≤ -2.5). BMI (<28) was associated with the highest Sensitivity (95%), the lowest Negative Likelihood Ratio (LR-) of 0.14, an AUC of 0.73, and the number needed to scan (NNS) of 8. The USPSTF approach had the lowest sensitivity (24%), highest LR- (0.91), lowest AUC (0.62), and highest NNS (9). Among 5 established modalities, SCORE (≥ 6) appears to be the best (Sensitivity: 92%, LR-: 0.24, AUC: 0.75, NNS: 9). Conclusion: BMI (< 28) had a comparable numerical screening performance overall to the current screening modalities. BMI (< 28) could be considered a potential indicator when screening early postmenopausal White women for osteoporosis. However, a better osteoporosis screening tool remains to be developed. Keywords: Osteoporosis screening; Early postmenopausal women; DXA scan; Bone mineral density
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Introduction Osteoporosis is a major public health challenge worldwide. The goal of osteoporosis screening is to identify individuals at increased risk of sustaining a low trauma fracture, who would benefit from intervention to minimize that risk. Dual-energy X-ray Absorptiometry (DXA) is by far the preferred method for identifying patients with osteoporosis.1 Appropriate and timely DXA screening can facilitate early clinical intervention to reduce the risk of osteoporotic fracture along with fracture-related morbidity and mortality. In the United States, osteoporosis and low bone density remain underdiagnosed.2 Most medical societies recommend DXA screening for women age 65 and older regardless of risk factors. Routine, or baseline, DXA screening of early postmenopausal women (younger than age 65) is not recommended if no risk factors exist. There is no consensus, however, on which early menopausal women should receive DXA screening. More than 30 different risk factors have been listed by various screening guidelines as an indication for DXA screening in this population. A riskfactor (RF) based approach recommends a DXA for those with one or more osteoporosis risk factors. 1,3
The United States Preventive Services Task Force (USPSTF) recommends a DXA for those with
a 10-year major osteoporotic fracture risk of 9.3% or higher, which is calculated by the Fracture Risk Assessment Tool (FRAX™)4 without a bone mineral density (BMD).5 The Osteoporosis SelfAssessment Tool (OST) recommends a DXA for those who score <2.6 The Simple Calculated Osteoporosis Risk Estimation (SCORE) recommends a DXA for those who score 6 or higher.7 The Osteoporosis Risk Assessment Instrument (ORAI) recommends BMD testing for those who score 9 or higher.8 Low BMI is one of the well-recognized modifiable risk factors for both osteoporosis and fracture. Results from the Early Postmenopausal Intervention Cohort (EPIC) indicated that early postmenopausal women in the lowest tertiles of BMI had up to 12% lower BMD at baseline and more than 2-fold higher 2-year bone loss compared with women in the highest tertiles.9 The results of another study suggested that a low BMI (< 20 kg/m2) is a good indicator for DXA referral in women less than 60 years of age.10 Pharmacologic therapy to prevent fractures has been demonstrated to be effective in postmenopausal women with osteoporosis. While the absolute number of women younger than 65 who have osteoporosis is large, they only account for a small proportion of the young postmenopausal population.11,12 Thus, knowing the predictive ability of the current osteoporosis
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screening modalities in early postmenopausal women is of great clinical importance. The present study was conducted to assess the predictive accuracy of the USPSTF screening strategy, the RF based approach, the OST tool, SCORE, ORAI, and low BMI alone in identifying early postmenopausal women with osteoporosis.
Methods This study was originally approved by the Hartford Hospital institutional review board to compare previous pregnancies and/or breast-feeding and their association with postmenopausal osteoporosis.13 The current research is being conducted under the review and approval of the Reading Health System institutional review board. Postmenopausal (>12 months since last menstrual period) women aged 50-64 years presenting for a screening Dual-energy X-ray absorptiometry (DXA) test were recruited between January 1, 2007, and March 1, 2009. The recruitment stopped after the sample size calculated by the a priori power analysis for original study was met13. A telephone survey was conducted after informed consent was obtained (Fig. 1). The survey questions included age, weight, height, race, various osteoporosis risk factors (i.e. a history of fragility fractures of the spine or hip that occurred after age 50 years, parental hip fracture, ever or current long-term use of steroids [>3 months use], current smoking, small stature [<127 lb. or BMI <21 kg/m2], a medical history of rheumatoid arthritis (RA), other medical causes of bone loss [i.e. hyperthyroidism, hyperparathyroidism, kidney failure, anorexia], use of long-term therapy with medications known to adversely affect BMD [i.e. Leuprolide, Depo Provera, Heparin, Anticonvulsants], use of arms to stand up (as an indicator of physical activity), ever or current hormonal therapy (HT), concomitant medications, along with family and personal medical histories. Each woman had a DXA performed at one of four Jefferson radiology testing sites in the greater Hartford, Connecticut, area. All four testing sites belong to the same institution using bone densitometers of the same make and model. All densitometers were treated and maintained in the same way. The results from each DXA were obtained and incorporated into the database. The study investigators were blinded to the DXA results during the process of data entry. Three of the women surveyed failed to provide the researcher with their age and were consequently eliminated from the study. Osteoporosis was defined as a T-score of -2.5 or lower. In this study, the FRAX without BMD input was used to determine the 10-year probability that early postmenopausal women will
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sustain any one of four major osteoporotic fractures (hip, proximal humerus, wrist, and vertebral). According to the USPSTF guideline,5 and hence this study, women with a fracture risk of 9.3% or higher would be a candidate for a DXA. The OST score is calculated as 0.2 (weight in kg − age in years) and rounded up to the closest integer. 6 Screening is recommended for women with an OST score of < 2 in this study. The SCORE was developed by Lydick et al.7 to identify post-menopausal women who may have a T score of < -2 and therefore indicate the need to be screened. A total of six parameters were taken into account in the SCORE calculator including age, race (black vs. nonblack), RA, history of Non-traumatic Fractures of the spine, hip or wrist (FractureHx), prior estrogen therapy (ET) use, and weight [Race + RA + FractureHx + ET + (3 x Age / 10) - (Weight / 10)].7,14 Women with a score of 6 or higher are considered candidates for a DXA. The 3-item ORAI was developed and validated in a cohort of Canadian women aged 45 years or older and had a sensitivity of 94.4% and specificity of 41.4% in that population.8 Three risk factors including age, weight, and current ET use were utilized in the calculation, and a total score of 9 or above warrant BMD testing. After sensitivity analyses (data not shown), those risk factors included in a RF based approach were determined to be history of a fragility fracture of the spine or hip that occurred after age 50 years, parental hip fracture, ever or current long-term use of steroids, current smoking, small stature, and a medical history of RA. Women with one or more risk factors are considered candidates for a DXA. BMI alone, as the comparison tool, will be used as a predictor for osteoporosis and the optimal cutpoint (OCP) will be detected from the Receiver Operating Characteristic (ROC) curve. Women with a BMI lower than the OCP will be considered candidates for a DXA. The objective of this study is to compare the screening performance of the aforementioned screening modalities. The data and information from all women, who were originally recruited for a study analyzing the association of previous pregnancies, breastfeeding, or both with osteoporosis, were entered into the FRAX calculator without BMD input, the OST tool, ORAI, and the SCORE. DXA results are used as the primary outcome. The Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Negative Likelihood Ratio (LR-), and the area under the Curve (AUC) of each screening strategy to identify early postmenopausal women with osteoporosis will be determined. AUC analyses using logistic regression were performed to compare each model with “chance” (AUC=0.5) and to evaluate usefulness of each model in predicting osteoporosis. Paired AUC comparisons were conducted between all models to evaluate predictive superiority of each
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screening tool. Post hoc power analysis was performed based on the difference between AUCs of USPSTF and BMI. All data were analyzed using SAS 9.3, with P <0.05 being deemed statistically significant.
Results Baseline characteristics of Study Participants Of 445 participants, 95% were White, 2% were Black, 2% were Hispanic, and 1% was Asian/other. The average age (SD) was 57 (4.2) and the mean BMI (SD) was 27 (5.8). Of these women, 38(8.5%) had osteoporosis and 3(0.7%) had an osteoporotic fracture. The mean (SD) 10year predicted major osteoporotic fracture risk by FRAX without BMD for the entire population was 7.2% (3.5%) (Table 1). Of the 38 women with osteoporosis, 71% had a normal weight (BMI, 18.5-24.9), 3% were underweight (BMI <18.5), 21% were overweight (BMI, 25-29.9), and 5% were obese (BMI >30). Of the 445, only one (0.2%) was underweight and also had osteoporosis (100%). There were 195 women (43.8%) who were normal weight, of whom 27 (13.9%) had osteoporosis. There were 132 women (29.7%) who were overweight, of whom 8 (6.1%) were diagnosed with osteoporosis. There were 117 women (26.3%) who were obese, of whom 2 (1.7%) had osteoporosis. The Cochran-Armitage Trend Test suggests the risk of osteoporosis increases as BMI decreases, P<0.0001.
BMI alone as a predictor of osteoporosis BMI alone as a predictor of osteoporosis was assessed with the ROC curve (AUC=0.73). The optimal cut-point was identified at BMI of 27.7 with corresponding sensitivity and specificity of 95% and 38%, respectively (Fig. 2). Age is one of the most significant risk factors of osteoporosis and fracture for older postmenopausal women,15 but was not shown to have a significant predictive value in early postmenopausal women, with an AUC of 0.53, which was not significantly different from chance alone (AUC=0.50). Age was positively correlated with BMI in this study population (r=0.13, p=0.01).
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Comparison of the 6 osteoporosis screening strategies The USPSTF strategy would refer 17% (77/445) of all participants aged 50-64 for BMD testing, compared with 68% (304/445) using the SCORE (≥6), 47% (210/445) using OST (<2), 40% (178/445) using RF based strategy, 41% (182/445) using ORAI, and 64% (287/445) using the BMI alone (<28) strategy. Of the 38 women with osteoporosis, the USPSTF strategy identified 24% (9/38) as being at risk of osteoporosis and needing BMD testing, compared with 92% (35/38) using SCORE, 79% (30/38) using OST, 66% (25/38) using RF, 74% (28/38) using ORAI, and 95% (36/38) using the BMI alone strategy. Of the 6 screening strategies, the USPSTF had the lowest sensitivity (24%) with 95% CI of 11-40% for identifying osteoporosis, the highest false negative rate (FN) of 8%, and the highest LR- (0.92). BMI had the highest sensitivity (95% CI) of 95% (82-99), the lowest FN (1.3%), and the lowest LR(0.14). False positive rates are similar across all strategies (87%-88%). Pairwise comparisons of the AUCs for the 6 strategies in identifying early postmenopausal women with osteoporosis are listed in Table 2. Post hoc power analysis showed that the power of the current sample size of 445 to distinguish the difference of 11% between AUCs of USPSTF and BMI (0.62 vs. 0.73) is 39%, which is far less than adequate power of 80%. The PPV using each of the 6 strategies was comparable with a range of 12-15%. The number of DXAs needed to detect one case of osteoporosis, or the number needed to scan (NNS), was calculated for each strategy. The USPSTF and SCORE require the highest NNS (n=9), 8 for BMI alone, and 7 for the RF, ORAI, and OST (Table 3).
Discussion Osteoporosis screening is consistently recommended for women age 65 years and over. However, the data are limited regarding optimal osteoporosis screening strategies for early postmenopausal women. The majority of osteoporosis screening guidelines for young postmenopausal women are based upon risk factors. Among the published guidelines, the USPSTF has been reduced to the simplest, recommending a DXA for those with a 10-year fracture risk equivalent to a 65-year-old White woman without additional risk factors (9.3% by FRAX without a BMD result). However, the data are lacking on the accuracy of the USPSTF strategy as well as other established tools in identifying young postmenopausal women with osteoporosis.
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A low BMI has been considered one of most important risk factors for osteoporosis9,10. The most widely accepted cut-point is <21 kg/m2 (or a weight <127 lbs)3. However, by using this strict cut-point, we were only able to target 10% (44) of our study population and identify 26% (10) of the women with osteoporosis. With an optimal BMI cut-point of < 28, 64% (278) of our study population met screening criteria and 95% (36) of our patients with osteoporosis were detected. Although the NNS is not the lowest, it is lower than that with SCORE, which is one of the two modalities having a sensitivity >90%. A good screening test requires a higher sensitivity and uses a negative result to rule out the disease. The negative likelihood ratio (1-sensitivity/specificity), which indicates how much to decrease the probability of disease if the test is negative, is considered a good indicator for ruling out disease. The lower the LR- the more confidence can be placed in the screening test for using a negative result to rule out the disease. LR- < 0.1indicates that the negative result has a large and often conclusive effect on decreasing the probability of disease presence; in essence, a negative test result is able to rule out the disease. A good screening test, therefore, should have a LR- < 0.1. While recognizing that BMI alone is still far from ideal, it is the closest to a LR- < 0.1 and the overall predictive performance is superior to all other strategies being compared, therefore it may be considered a potential screening strategy. Although BMI is not the most accurate measure of body fat, it is a more appropriate measure of obesity while compared to body weight alone. A weight < 70kg has also been suggested in the literature as an indicator for osteoporosis screening.16 The AUC of weight alone in predicting osteoporosis in this study population was the same as BMI (AUC=0.73). The optimal cut-point was identified at a body weight of 175lbs (79kg) with corresponding sensitivity and specificity of 95% and 29%, respectively. While compared with BMI, a weight <175 lbs (79 kg) was suboptimal and associated with a higher LR- (0.18) and NNS (9). Contrary to our expectation, age was not a significant predictor of osteoporosis in this young postmenopausal population (AUC=0.53). This finding may be explained by the significant positive correlation between Age and BMI in this study population. The protective effect from increased BMI may have offset the negative effect of aging on bone health. Although the difference between BMI alone (BMI < 28) and the USPSTF recommendation was not significant (p=0.07), due to an inadequate power evidenced by our post-hoc power analysis, the possibility of a type II error should not be excluded. In this study, the sensitivity and AUC of the USPSTF strategy to identify young postmenopausal women with osteoporosis were the lowest among all six screening modalities. In
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addition, the USPSTF strategy was associated with the highest number needed to scan to detect one case of osteoporosis. The highest LR- further classifies the USPSTF strategy as a poor screening test among all six strategies. The PPV and False positive of the 6 strategies was similar, which is consistent with the results of another recent study.17 It is concerning that the USPSTF strategy only detects about one-quarter of young postmenopausal women with osteoporosis, meaning 76% of treatment candidates in this age group would not be detected. However, this result was not surprising since FRAX initially was not designed to be a predictor of low BMD but a tool that combines BMD and clinical risk factors to identify treatment candidates in healthy postmenopausal women based on 10-year fracture risk. The Osteoporosis Self-assessment Tool was first designed to predict the risk of osteoporosis in Asian postmenopausal women and only takes into account body weight and age.6 It was later tested using a different cut-point (OST ≤ 1) in US postmenopausal White women aged 45-64 years with the AUC (95%CI) of 0.77 (0.73-0.81).18 Recently, the performance of the OST<2 in predicting osteoporosis was evaluated in a sample of 4343 South American women with an interquartile (2575%) age range of 54 to 67 years. The AUC was 0.71 with a sensitivity and specificity of 84% and 44%, respectively. Although the OST requires the lowest NNS, it still missed about 20% of the treatment candidates and incurred 86% of DXA tests being performed for non-osteoporotic women. Forty-four percent of non-osteoporotic women would be referred for BMD testing by the OST at a cut-point score of 2. The Osteoporosis Risk Assessment Instrument considered one more parameter in the calculator, current ET use, as compared to OST. Interestingly, adding one more factor reduced the sensitivity and AUC, and increased the LR-, indicating the belief that “the more, the better” does not hold true while developing an osteoporosis screening tool. The Simple Calculated Osteoporosis Risk Estimation (SCORE) was developed in 1998 based on the data collected from 1279 postmenopausal women in the U.S. with sensitivity and specificity originally reported as 89% and 50% respectively.7 Besides age and weight, 4 other risk factors were also considered in the SCORE calculator. It later was evaluated in 398 Canadian postmenopausal women at least 45 years of age.19 A similar performance was noted with a sensitivity of 90%, specificity of 32%, AUC of 0.71, and PPV of 64%. According to the results of these data, 68% of those with a normal BMD would be referred for a DXA. Another study in Belgium white women reported a sensitivity of 91.5%, specificity of 26.5%, and a PPV of 52.8%.20 In our study population,
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the SCORE missed 8% of the treatment candidates, and it resulted in 66% of non-osteoporotic women being referred for BMD testing. The risk factor based approach was recommended by most medical societies, however, it became more difficult for clinicians to follow due to the fact that more than 30 risk factors have been listed in these guidelines. While realizing some risk factors play a more significant role than others, 6 major risk factors were identified after sensitivity analysis and compared with other modalities in this study. However, the risk factor based approach offered suboptimal sensitivity (66%) and AUC (0.64), with 44% of the treatment candidates being missed and 38% of non-osteoporotic women being referred for testing. Of 178 DXAs, 86% were performed in non-osteoporotic women. It is not surprising that all screening modalities had a low PPV (12-15%) or a high number of unnecessary scans (86-88%) in our cohort due to the low prevalence of osteoporosis (8.6%) in young postmenopausal women (50-64 years of age). It has been reported that the prevalence of osteoporosis in US and European Union postmenopausal women, including those aged >65 years, is about 30%.21 Since many women are not properly screened or treated for osteoporosis,22 how to identify those women with osteoporosis and high risk of osteoporotic fracture remains to be a big challenge to clinicians. Although DXA testing is non-invasive, efforts to minimize the number of unnecessary tests, and potential negative implications such as inappropriate or unnecessary treatment, should be a priority. Menopause researchers should focus on identifying a screening tool with high sensitivity (>90%), AUC (>0.8), as well as low LR- (<0.1), and low NNS, since effective interventions can help prevent morbidity and mortality in the right patient population. This study is limited by a relatively small sample size and low statistical power of detecting the difference in AUCs. We also recognized that the study population may not necessarily represent the general population. It was performed in a single institution, with a less diversified population, where White constituted the majority of patients. The study findings should not, therefore, be applied to other ethnic groups. In addition, it is a select population who were already scheduled for DXA before being contacted about the study. Unlike the other 5 established tools, the cut-point for BMI was not predetermined, therefore, it would only apply to a similar population. It’s predictive value must be validated in a larger population based study prior to clinical use. The prevalence of osteoporosis in this study population may be different from the general population; however, comparison of performance was based on those parameters such as sensitivity and LR- that do not depend on the disease prevalence in the examined groups.
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Conclusion The results of this study suggest that none of the current and commonly used screening strategies are ideal. With a desire to have a simple, clinically applicable screening tool, a BMI (<28) may be considered a potential indicator for referring early postmenopausal White women for BMD testing. While a large multicenter, multi-ethnic, population-based study is needed to confirm the predictive value of BMI as an osteoporosis screening tool for younger postmenopausal women, unrelenting efforts from the medical community are needed to develop better strategies for osteoporosis screening in early postmenopausal women.
Conflict of interest Statement There are no conflicts of interest
Funding This work and manuscript preparation was unfunded
Ethical approval, consent or animal equivalent This study was originally approved by the Hartford Hospital institutional review board and the current research is being conducted under the review and approval of the Reading Health System institutional review board (IRB# 023‐10)
Contributors and Their Role Xuezhi Jiang, M.D., Ph.D. Role: Study design, data analysis, manuscript writing Lauren E. Good Role: literature search, creating database Ryan Spinka, B.S. Role: literature search, creating database Peter F. Schnatz, D.O. Role: Study design, manuscript writing
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These data have been presented in oral format at the 25th NAMS (North American Menopause Society) Annual Meeting on October 16, 2014, in Washington, DC. However, these data and results have not been previously published in manuscript form.
Précis: The performance of BMI<28 in predicting osteoporosis in young postmenopausal White women aged 50-64 may be as good as the current screening tools.
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Reference 1. Osteoporosis. ACOG Practice Bulletin No. 129, September 2012. Obstetrics & Gynecology 2012:120:718-34. 2. Chestunt CH. Osteoporosis, an underdiagnosed disease. JAMA 2001; 286(22):2865-6. 3. Bonnick S, Harris S, Kendler D, et al. Management of osteoporosis in postmenopausal women. 2010 Position Statement of The North Menopause Society: position statement. Menopause 2010;17:25-54. 4. WHO Fracture risk assessment tool (FRAX®). http://www.shef.ac.uk/FRAX/tool.aspx?country=9. Accessed September 24, 2015. 5. AHRQ. Screening for Osteoporosis: Clinical Summary of U.S. Preventive Services Task Force. The Guide to clinical Preventive Services. Rockville, MD: AHRQ; 2012. 6. Koh LK, Ben Sedrine W, Torralba TP, Kung A, Fujiwara S, Chan SP, et al. Osteoporosis Self-Assessment Tool for Asians (OSTA) Research Group. A simple tool to identify Asian women at increased risk of osteoporosis. Osteoporos Int 2001;12:699–705. 7. Lydick E, Cook K. Turpin J, Melton m, Stine R, Brynes C. Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Mang Care 1998;4:37-48. 8. Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA, Tu JV. Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. Canadian Medical Assoc J. 2000;162(9):1289-94. 9. Ravn P, Cizza G, Bjarnason NH, Thompson D, Daley M, Wasnich RD, et al. Low bone mass index is an important risk factor for low bone mass and increased bone loss in early postmenopausal women. J of Bone & Mineral Research 1999;14(9):1622-7. 10. Iqbal SI, Morch LS, Rosenzweig M, Dela F. The outcome of bone mineral density measurements on patients referred from general practice. J of Clinical Densitometry 2005; 8(2):178-2. 11. Winzenberg, T and Jones, G. Dual energy X-ray absorptiometry. Australian Family Physician 2011; 40(1/2):43-44. 12. National Osteoporosis Foundation. Clinician’s Guide to Prevention and Treatment of Osteoporosis. Washington, DC: National Osteoporosis Foundation, 2010. 13. Schnatz PF, Barker KG, Marakovits KA, O'Sullivan DM. Effects of age at first pregnancy and breast-feeding on the development of postmenopausal osteoporosis. Menopause 2010; 17(6): 1161-6. 14. Geusens P, Hochberg MC, van der Voort DJ, et. al. Performance of risk indices for identifying low bone density in postmenopausal women. Mayo Clin Proc. 2002 Jul;77(7):629-37. 15. Jiang X, Westermann LB, Galleo GV, Demko J, Marakovits KA, Schnatz PF. Age as a Predictor of Osteoporotic Fracture Compared With Current Risk Prediction Models. Obstetrics & Gynecology 2013;122(5):1040-6.
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16. Michaëlsson K, Bergström R, Mallmin H, Holmberg L, Wolk A, Ljunghall S. Osteoporosis International 1996; 6(2):120-6. 17. Crandall CJ, Larson J, Gourlay ML, Donaldson MG, LaCroix A, Cauley JA, et al. Osteoporosis screening in postmenopausal women 50-64 years-old: comparison of U.S. Preventive Services Task Force Strategy and two traditional strategies in the Women’s Health Initiative. J of Bone & Mineral Research 2014; 29(7):1611-6. 18. Gourlay ML, Miller WC, Richy F, Garrett JM, Hanson LC, Reginster JY. Performance of Osteoporosis risk assessment tools in postmenopausal women aged 45-64 years. Osteoporosis International 2005; 16:921-7. 19. Cadarette SM, Jaglal SB, Murray TM. Validation of the simple calculated osteoporosis risk estimation (SCORE) for patient selection for bone densitometry. Osteoporosis International 1999;10(1):85-90. 20. Ben SW, Devogelaer JP, Kaufman JM, Goemaere S, Depresseux G, Zegels B, et al. Evaluation of the simple calculated osteoporosis risk estimation (SCORE) in a sample of white women from Belgium. Bone 2001;29(4):374-80. 21. Melton III LJ, Chrischilles EA, Cooper C, Lane AW, Riggs BL. Perspective: how many women have osteoporosis? J Bone Miner Res1992;7:1005–10. 22. Schnatz PF, Marakovits KA, Dubois M, O'Sullivan DM. Osteoporosis screening and treatment guidelines: are they being followed? Menopause 2011;18(10):1072-8.
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Figure Captions
Fig 1. This chart depicts the study recruitment process
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Fig 2. This figure depicts the area under the Receiver Operating Characteristics (ROC) curve for BMI and the optimal cut-point of BMI at 27.7 kg/m2 in predicting osteoporosis
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Tables Table 1. Selected Baseline Characteristics of the 445 Study Participants Age, years
BMI
Race/Ethnicity
Corticosteroid use ever Rheumatoid arthritis Current smoking Hx of fragility fractures of the spine or hip after age 50 Parental hip fracture
All 50‐54 55‐59 60‐64 All <18.5 18.5‐24.9 25‐29.9 ≥30 White Black Hispanic Asian/other Yes No Yes No Yes No Yes No
N (%)
Mean (SD)
Median (Min‐Max)
445 (100) 155 (34.8) 150 (33.7) 140 (31.5) 445 (100) 1 (0.2) 195 (43.8) 132 (29.7) 117 (26.3) 423 (95.1) 9 (2.0) 8 (1.8) 5 (1.1) 27 (6.1) 418 (93.9) 20 (4.5) 425 (95.5) 36 (8.1) 409 (91.9) 3 (0.7) 442 (99.3)
57.2 (4.2)
57.1 (50.0‐64.9)
27.1 (5.8)
25.9 (17.8‐54.9)
Yes 59 (13.3) No 386 (86.7) Prior ET/HT (estrogen Yes 215 (48.3) use) No 230 (51.7) 10‐yr risk of major osteoporotic 445 (100) 7.2 (3.5) 6.4 (1.4‐29.0) fracture by FRAX without BMD (%) OST 445 (100) 2.8 (3.1) 2.2 (‐4.1‐14.8) ORAI 445 (100) 8.1 (4.2) 7.0 (0‐16) SCORE 445 (100) 7.1 (3.7) 7.7 (‐8.3‐15.4) BMI: Body mass index, kg/m2; Hx: History; Min: Minimum; Max: Maximum; ET = estrogen therapy; HT = hormonal therapy (estrogen and progestogen); OST: Osteoporosis Self‐assessment Tool; ORAI: Osteoporosis Risk Assessment Instrument; SCORE: Simple Calculated Osteoporosis Risk Estimation.
19 Table 2. P‐values for pairwise AUC comparisons Strategies
USPSTF (≥9.3%)
RF (≥1 risk OST (<2) factor) BMI (<28) 0.07 0.06 0.88 USPSTF (≥9.3%) ‐ 0.71 0.043¹ RF (≥1 risk factor) ‐ 0.058 OST (<2) ‐ SCORE (≥6) The shaded area indicates duplicate P values that were not shown. ¹AUC higher for OST, ² AUC higher for SCORE, ³ AUC higher for SCORE
SCORE (≥6)
ORAI (≥9)
0.45 0.002² 0.005³ 0.44 ‐
0.28 0.19 0.30 0.15 0.08
20 Table 3. Comparison of predictive accuracy of BMI and other screening modalities Sensitivity Specificity PPV (CI) AUC (CI) LR‐ NNS P value1 (CI) (%) (CI) (%) (%) BMI (<28) 95 (82‐99) 38 (34‐43) 13 (9‐17) 0.73 (0.65‐0.81) 0.13 8 <0.0001 USPSTF (≥9.3%) 24 (11‐40) 83 (79‐87) 12 (5‐21) 0.62 (0.52‐0.72) 0.92 9 0.017 RF (≥1 risk factor) 66 (49‐80) 62 (58‐67) 14 (9‐20) 0.64 (0.56‐0.72) 0.55 7 0.0006 OST (<2) 79 (63‐91) 56 (51‐61) 14 (10‐20) 0.73 (0.65‐0.81) 0.38 7 <0.0001 ORAI (≥9) 74 (57‐87) 62 (57‐67) 15 (11‐22) 0.69 (0.60‐0.78) 0.42 7 <0.0001 SCORE (≥6) 92 (79‐98) 34 (29‐39) 12 (8‐16) 0.75 (0.67‐0.83) 0.24 9 <0.0001 USPSTF (≥4.7%) 92 (79‐98) 21 (17‐25) 10 (7‐13) 0.62 (0.52‐0.72) 0.38 10 <0.0001 OST (<3) 87 (72‐96) 43 (38‐47) 12 (9‐17) 0.73 (0.65‐0.81) 0.30 8 <0.0001 1 P value : when AUC was compared to chance alone; RF: Risk Factor Based Approach; OST: Osteoporosis Self‐ assessment Tool; ORAI: Osteoporosis Risk Assessment Instrument; SCORE: Simple Calculated Osteoporosis Risk Estimation; NNS: The Number Needed to Scan (The number of DXA tests needed to detect one case of osteoporosis); AUC: Area under the ROC curve; PPV: Positive predictive value; FP: False positive; CI: 95% Confidence limit. LR‐: Negative likelihood ratio. Strategies
S0378-5122(15)30058-X http://dx.doi.org/doi:10.1016/j.maturitas.2015.09.009 MAT 6488
To appear in:
Maturitas
Received date: Revised date: Accepted date:
20-7-2015 25-9-2015 28-9-2015
Please cite this article as: Jiang Xuezhi, Good Lauren E, Spinka Ryan, Schnatz Peter F.Osteoporosis screening in postmenopausal women aged 50ndash64 years: BMI alone compared with current screening tools.Maturitas http://dx.doi.org/10.1016/j.maturitas.2015.09.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Osteoporosis Screening in Postmenopausal Women Aged 50-64 Years: BMI alone compared with current screening tools Running Title: BMI and Osteoporosis Screening Xuezhi Jiang, M.D., Ph.D.1,3* [email protected], Lauren E. Good1, Ryan Spinka, B.S.1, Peter F. Schnatz, D.O.1,2,3,4 1
Reading Hospital, Departments of Obstetrics and Gynecology, Reading, PA
2
Reading Hospital, Departments of Internal Medicine, Reading, PA
3
Sidney Kimmel Medical College of Thomas Jefferson University, Departments of Obstetrics and Gynecology, Philadelphia, PA 4
Sidney Kimmel Medical College of Thomas Jefferson University, Departments of Internal Medicine, Philadelphia, PA
*
Corresponding author at: Assistant Professor of OBGYN, Sidney Kimmel Medical College of Thomas Jefferson University, Reading Hospital; Department of ObGyn – R1; P.O. Box 16052 Reading, PA 19612-6052. Tel.: 484-628-8827; fax: 484-628-9292.
2
Highlights
We compared the predictive performance of BMI alone with 5 screening modalities in identifying early postmenopausal women with osteoporosis. BMI (< 28) had a comparable numerical screening performance overall to the current screening modalities. However, a better osteoporosis screening tool remains to be developed.
3
Abstract Objectives: Consensus on when to initiate DXA screening for early postmenopausal women (<65 years of age) is lacking. Low Body Mass Index (BMI) has been proposed as one of the major risk factors for osteoporosis. This study sought to compare the predictive performance of BMI alone with 5 screening modalities (the U.S. Preventive Services Task Force [USPSTF] FRAX threshold of 9.3%, a risk factor based approach [≥ 1 risk factors], the Osteoporosis Self-Assessment Tool [OST <2], the Osteoporosis Risk Assessment Instrument [ORAI ≥9], and the Simple Calculated Osteoporosis Risk Estimation [SCORE ≥ 6]) in identifying early postmenopausal women with osteoporosis. Methods: Postmenopausal women aged 50-64 years presenting for a DXA test were recruited between January 1, 2007, and March 1, 2009. Demographic data and osteoporosis risk factors were obtained through a telephone survey. The performance of each screening tool in predicting osteoporosis was compared. Results: Of 445 study participants, 95% were White, 38 had osteoporosis (T-score ≤ -2.5). BMI (<28) was associated with the highest Sensitivity (95%), the lowest Negative Likelihood Ratio (LR-) of 0.14, an AUC of 0.73, and the number needed to scan (NNS) of 8. The USPSTF approach had the lowest sensitivity (24%), highest LR- (0.91), lowest AUC (0.62), and highest NNS (9). Among 5 established modalities, SCORE (≥ 6) appears to be the best (Sensitivity: 92%, LR-: 0.24, AUC: 0.75, NNS: 9). Conclusion: BMI (< 28) had a comparable numerical screening performance overall to the current screening modalities. BMI (< 28) could be considered a potential indicator when screening early postmenopausal White women for osteoporosis. However, a better osteoporosis screening tool remains to be developed. Keywords: Osteoporosis screening; Early postmenopausal women; DXA scan; Bone mineral density
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Introduction Osteoporosis is a major public health challenge worldwide. The goal of osteoporosis screening is to identify individuals at increased risk of sustaining a low trauma fracture, who would benefit from intervention to minimize that risk. Dual-energy X-ray Absorptiometry (DXA) is by far the preferred method for identifying patients with osteoporosis.1 Appropriate and timely DXA screening can facilitate early clinical intervention to reduce the risk of osteoporotic fracture along with fracture-related morbidity and mortality. In the United States, osteoporosis and low bone density remain underdiagnosed.2 Most medical societies recommend DXA screening for women age 65 and older regardless of risk factors. Routine, or baseline, DXA screening of early postmenopausal women (younger than age 65) is not recommended if no risk factors exist. There is no consensus, however, on which early menopausal women should receive DXA screening. More than 30 different risk factors have been listed by various screening guidelines as an indication for DXA screening in this population. A riskfactor (RF) based approach recommends a DXA for those with one or more osteoporosis risk factors. 1,3
The United States Preventive Services Task Force (USPSTF) recommends a DXA for those with
a 10-year major osteoporotic fracture risk of 9.3% or higher, which is calculated by the Fracture Risk Assessment Tool (FRAX™)4 without a bone mineral density (BMD).5 The Osteoporosis SelfAssessment Tool (OST) recommends a DXA for those who score <2.6 The Simple Calculated Osteoporosis Risk Estimation (SCORE) recommends a DXA for those who score 6 or higher.7 The Osteoporosis Risk Assessment Instrument (ORAI) recommends BMD testing for those who score 9 or higher.8 Low BMI is one of the well-recognized modifiable risk factors for both osteoporosis and fracture. Results from the Early Postmenopausal Intervention Cohort (EPIC) indicated that early postmenopausal women in the lowest tertiles of BMI had up to 12% lower BMD at baseline and more than 2-fold higher 2-year bone loss compared with women in the highest tertiles.9 The results of another study suggested that a low BMI (< 20 kg/m2) is a good indicator for DXA referral in women less than 60 years of age.10 Pharmacologic therapy to prevent fractures has been demonstrated to be effective in postmenopausal women with osteoporosis. While the absolute number of women younger than 65 who have osteoporosis is large, they only account for a small proportion of the young postmenopausal population.11,12 Thus, knowing the predictive ability of the current osteoporosis
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screening modalities in early postmenopausal women is of great clinical importance. The present study was conducted to assess the predictive accuracy of the USPSTF screening strategy, the RF based approach, the OST tool, SCORE, ORAI, and low BMI alone in identifying early postmenopausal women with osteoporosis.
Methods This study was originally approved by the Hartford Hospital institutional review board to compare previous pregnancies and/or breast-feeding and their association with postmenopausal osteoporosis.13 The current research is being conducted under the review and approval of the Reading Health System institutional review board. Postmenopausal (>12 months since last menstrual period) women aged 50-64 years presenting for a screening Dual-energy X-ray absorptiometry (DXA) test were recruited between January 1, 2007, and March 1, 2009. The recruitment stopped after the sample size calculated by the a priori power analysis for original study was met13. A telephone survey was conducted after informed consent was obtained (Fig. 1). The survey questions included age, weight, height, race, various osteoporosis risk factors (i.e. a history of fragility fractures of the spine or hip that occurred after age 50 years, parental hip fracture, ever or current long-term use of steroids [>3 months use], current smoking, small stature [<127 lb. or BMI <21 kg/m2], a medical history of rheumatoid arthritis (RA), other medical causes of bone loss [i.e. hyperthyroidism, hyperparathyroidism, kidney failure, anorexia], use of long-term therapy with medications known to adversely affect BMD [i.e. Leuprolide, Depo Provera, Heparin, Anticonvulsants], use of arms to stand up (as an indicator of physical activity), ever or current hormonal therapy (HT), concomitant medications, along with family and personal medical histories. Each woman had a DXA performed at one of four Jefferson radiology testing sites in the greater Hartford, Connecticut, area. All four testing sites belong to the same institution using bone densitometers of the same make and model. All densitometers were treated and maintained in the same way. The results from each DXA were obtained and incorporated into the database. The study investigators were blinded to the DXA results during the process of data entry. Three of the women surveyed failed to provide the researcher with their age and were consequently eliminated from the study. Osteoporosis was defined as a T-score of -2.5 or lower. In this study, the FRAX without BMD input was used to determine the 10-year probability that early postmenopausal women will
6
sustain any one of four major osteoporotic fractures (hip, proximal humerus, wrist, and vertebral). According to the USPSTF guideline,5 and hence this study, women with a fracture risk of 9.3% or higher would be a candidate for a DXA. The OST score is calculated as 0.2 (weight in kg − age in years) and rounded up to the closest integer. 6 Screening is recommended for women with an OST score of < 2 in this study. The SCORE was developed by Lydick et al.7 to identify post-menopausal women who may have a T score of < -2 and therefore indicate the need to be screened. A total of six parameters were taken into account in the SCORE calculator including age, race (black vs. nonblack), RA, history of Non-traumatic Fractures of the spine, hip or wrist (FractureHx), prior estrogen therapy (ET) use, and weight [Race + RA + FractureHx + ET + (3 x Age / 10) - (Weight / 10)].7,14 Women with a score of 6 or higher are considered candidates for a DXA. The 3-item ORAI was developed and validated in a cohort of Canadian women aged 45 years or older and had a sensitivity of 94.4% and specificity of 41.4% in that population.8 Three risk factors including age, weight, and current ET use were utilized in the calculation, and a total score of 9 or above warrant BMD testing. After sensitivity analyses (data not shown), those risk factors included in a RF based approach were determined to be history of a fragility fracture of the spine or hip that occurred after age 50 years, parental hip fracture, ever or current long-term use of steroids, current smoking, small stature, and a medical history of RA. Women with one or more risk factors are considered candidates for a DXA. BMI alone, as the comparison tool, will be used as a predictor for osteoporosis and the optimal cutpoint (OCP) will be detected from the Receiver Operating Characteristic (ROC) curve. Women with a BMI lower than the OCP will be considered candidates for a DXA. The objective of this study is to compare the screening performance of the aforementioned screening modalities. The data and information from all women, who were originally recruited for a study analyzing the association of previous pregnancies, breastfeeding, or both with osteoporosis, were entered into the FRAX calculator without BMD input, the OST tool, ORAI, and the SCORE. DXA results are used as the primary outcome. The Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Negative Likelihood Ratio (LR-), and the area under the Curve (AUC) of each screening strategy to identify early postmenopausal women with osteoporosis will be determined. AUC analyses using logistic regression were performed to compare each model with “chance” (AUC=0.5) and to evaluate usefulness of each model in predicting osteoporosis. Paired AUC comparisons were conducted between all models to evaluate predictive superiority of each
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screening tool. Post hoc power analysis was performed based on the difference between AUCs of USPSTF and BMI. All data were analyzed using SAS 9.3, with P <0.05 being deemed statistically significant.
Results Baseline characteristics of Study Participants Of 445 participants, 95% were White, 2% were Black, 2% were Hispanic, and 1% was Asian/other. The average age (SD) was 57 (4.2) and the mean BMI (SD) was 27 (5.8). Of these women, 38(8.5%) had osteoporosis and 3(0.7%) had an osteoporotic fracture. The mean (SD) 10year predicted major osteoporotic fracture risk by FRAX without BMD for the entire population was 7.2% (3.5%) (Table 1). Of the 38 women with osteoporosis, 71% had a normal weight (BMI, 18.5-24.9), 3% were underweight (BMI <18.5), 21% were overweight (BMI, 25-29.9), and 5% were obese (BMI >30). Of the 445, only one (0.2%) was underweight and also had osteoporosis (100%). There were 195 women (43.8%) who were normal weight, of whom 27 (13.9%) had osteoporosis. There were 132 women (29.7%) who were overweight, of whom 8 (6.1%) were diagnosed with osteoporosis. There were 117 women (26.3%) who were obese, of whom 2 (1.7%) had osteoporosis. The Cochran-Armitage Trend Test suggests the risk of osteoporosis increases as BMI decreases, P<0.0001.
BMI alone as a predictor of osteoporosis BMI alone as a predictor of osteoporosis was assessed with the ROC curve (AUC=0.73). The optimal cut-point was identified at BMI of 27.7 with corresponding sensitivity and specificity of 95% and 38%, respectively (Fig. 2). Age is one of the most significant risk factors of osteoporosis and fracture for older postmenopausal women,15 but was not shown to have a significant predictive value in early postmenopausal women, with an AUC of 0.53, which was not significantly different from chance alone (AUC=0.50). Age was positively correlated with BMI in this study population (r=0.13, p=0.01).
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Comparison of the 6 osteoporosis screening strategies The USPSTF strategy would refer 17% (77/445) of all participants aged 50-64 for BMD testing, compared with 68% (304/445) using the SCORE (≥6), 47% (210/445) using OST (<2), 40% (178/445) using RF based strategy, 41% (182/445) using ORAI, and 64% (287/445) using the BMI alone (<28) strategy. Of the 38 women with osteoporosis, the USPSTF strategy identified 24% (9/38) as being at risk of osteoporosis and needing BMD testing, compared with 92% (35/38) using SCORE, 79% (30/38) using OST, 66% (25/38) using RF, 74% (28/38) using ORAI, and 95% (36/38) using the BMI alone strategy. Of the 6 screening strategies, the USPSTF had the lowest sensitivity (24%) with 95% CI of 11-40% for identifying osteoporosis, the highest false negative rate (FN) of 8%, and the highest LR- (0.92). BMI had the highest sensitivity (95% CI) of 95% (82-99), the lowest FN (1.3%), and the lowest LR(0.14). False positive rates are similar across all strategies (87%-88%). Pairwise comparisons of the AUCs for the 6 strategies in identifying early postmenopausal women with osteoporosis are listed in Table 2. Post hoc power analysis showed that the power of the current sample size of 445 to distinguish the difference of 11% between AUCs of USPSTF and BMI (0.62 vs. 0.73) is 39%, which is far less than adequate power of 80%. The PPV using each of the 6 strategies was comparable with a range of 12-15%. The number of DXAs needed to detect one case of osteoporosis, or the number needed to scan (NNS), was calculated for each strategy. The USPSTF and SCORE require the highest NNS (n=9), 8 for BMI alone, and 7 for the RF, ORAI, and OST (Table 3).
Discussion Osteoporosis screening is consistently recommended for women age 65 years and over. However, the data are limited regarding optimal osteoporosis screening strategies for early postmenopausal women. The majority of osteoporosis screening guidelines for young postmenopausal women are based upon risk factors. Among the published guidelines, the USPSTF has been reduced to the simplest, recommending a DXA for those with a 10-year fracture risk equivalent to a 65-year-old White woman without additional risk factors (9.3% by FRAX without a BMD result). However, the data are lacking on the accuracy of the USPSTF strategy as well as other established tools in identifying young postmenopausal women with osteoporosis.
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A low BMI has been considered one of most important risk factors for osteoporosis9,10. The most widely accepted cut-point is <21 kg/m2 (or a weight <127 lbs)3. However, by using this strict cut-point, we were only able to target 10% (44) of our study population and identify 26% (10) of the women with osteoporosis. With an optimal BMI cut-point of < 28, 64% (278) of our study population met screening criteria and 95% (36) of our patients with osteoporosis were detected. Although the NNS is not the lowest, it is lower than that with SCORE, which is one of the two modalities having a sensitivity >90%. A good screening test requires a higher sensitivity and uses a negative result to rule out the disease. The negative likelihood ratio (1-sensitivity/specificity), which indicates how much to decrease the probability of disease if the test is negative, is considered a good indicator for ruling out disease. The lower the LR- the more confidence can be placed in the screening test for using a negative result to rule out the disease. LR- < 0.1indicates that the negative result has a large and often conclusive effect on decreasing the probability of disease presence; in essence, a negative test result is able to rule out the disease. A good screening test, therefore, should have a LR- < 0.1. While recognizing that BMI alone is still far from ideal, it is the closest to a LR- < 0.1 and the overall predictive performance is superior to all other strategies being compared, therefore it may be considered a potential screening strategy. Although BMI is not the most accurate measure of body fat, it is a more appropriate measure of obesity while compared to body weight alone. A weight < 70kg has also been suggested in the literature as an indicator for osteoporosis screening.16 The AUC of weight alone in predicting osteoporosis in this study population was the same as BMI (AUC=0.73). The optimal cut-point was identified at a body weight of 175lbs (79kg) with corresponding sensitivity and specificity of 95% and 29%, respectively. While compared with BMI, a weight <175 lbs (79 kg) was suboptimal and associated with a higher LR- (0.18) and NNS (9). Contrary to our expectation, age was not a significant predictor of osteoporosis in this young postmenopausal population (AUC=0.53). This finding may be explained by the significant positive correlation between Age and BMI in this study population. The protective effect from increased BMI may have offset the negative effect of aging on bone health. Although the difference between BMI alone (BMI < 28) and the USPSTF recommendation was not significant (p=0.07), due to an inadequate power evidenced by our post-hoc power analysis, the possibility of a type II error should not be excluded. In this study, the sensitivity and AUC of the USPSTF strategy to identify young postmenopausal women with osteoporosis were the lowest among all six screening modalities. In
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addition, the USPSTF strategy was associated with the highest number needed to scan to detect one case of osteoporosis. The highest LR- further classifies the USPSTF strategy as a poor screening test among all six strategies. The PPV and False positive of the 6 strategies was similar, which is consistent with the results of another recent study.17 It is concerning that the USPSTF strategy only detects about one-quarter of young postmenopausal women with osteoporosis, meaning 76% of treatment candidates in this age group would not be detected. However, this result was not surprising since FRAX initially was not designed to be a predictor of low BMD but a tool that combines BMD and clinical risk factors to identify treatment candidates in healthy postmenopausal women based on 10-year fracture risk. The Osteoporosis Self-assessment Tool was first designed to predict the risk of osteoporosis in Asian postmenopausal women and only takes into account body weight and age.6 It was later tested using a different cut-point (OST ≤ 1) in US postmenopausal White women aged 45-64 years with the AUC (95%CI) of 0.77 (0.73-0.81).18 Recently, the performance of the OST<2 in predicting osteoporosis was evaluated in a sample of 4343 South American women with an interquartile (2575%) age range of 54 to 67 years. The AUC was 0.71 with a sensitivity and specificity of 84% and 44%, respectively. Although the OST requires the lowest NNS, it still missed about 20% of the treatment candidates and incurred 86% of DXA tests being performed for non-osteoporotic women. Forty-four percent of non-osteoporotic women would be referred for BMD testing by the OST at a cut-point score of 2. The Osteoporosis Risk Assessment Instrument considered one more parameter in the calculator, current ET use, as compared to OST. Interestingly, adding one more factor reduced the sensitivity and AUC, and increased the LR-, indicating the belief that “the more, the better” does not hold true while developing an osteoporosis screening tool. The Simple Calculated Osteoporosis Risk Estimation (SCORE) was developed in 1998 based on the data collected from 1279 postmenopausal women in the U.S. with sensitivity and specificity originally reported as 89% and 50% respectively.7 Besides age and weight, 4 other risk factors were also considered in the SCORE calculator. It later was evaluated in 398 Canadian postmenopausal women at least 45 years of age.19 A similar performance was noted with a sensitivity of 90%, specificity of 32%, AUC of 0.71, and PPV of 64%. According to the results of these data, 68% of those with a normal BMD would be referred for a DXA. Another study in Belgium white women reported a sensitivity of 91.5%, specificity of 26.5%, and a PPV of 52.8%.20 In our study population,
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the SCORE missed 8% of the treatment candidates, and it resulted in 66% of non-osteoporotic women being referred for BMD testing. The risk factor based approach was recommended by most medical societies, however, it became more difficult for clinicians to follow due to the fact that more than 30 risk factors have been listed in these guidelines. While realizing some risk factors play a more significant role than others, 6 major risk factors were identified after sensitivity analysis and compared with other modalities in this study. However, the risk factor based approach offered suboptimal sensitivity (66%) and AUC (0.64), with 44% of the treatment candidates being missed and 38% of non-osteoporotic women being referred for testing. Of 178 DXAs, 86% were performed in non-osteoporotic women. It is not surprising that all screening modalities had a low PPV (12-15%) or a high number of unnecessary scans (86-88%) in our cohort due to the low prevalence of osteoporosis (8.6%) in young postmenopausal women (50-64 years of age). It has been reported that the prevalence of osteoporosis in US and European Union postmenopausal women, including those aged >65 years, is about 30%.21 Since many women are not properly screened or treated for osteoporosis,22 how to identify those women with osteoporosis and high risk of osteoporotic fracture remains to be a big challenge to clinicians. Although DXA testing is non-invasive, efforts to minimize the number of unnecessary tests, and potential negative implications such as inappropriate or unnecessary treatment, should be a priority. Menopause researchers should focus on identifying a screening tool with high sensitivity (>90%), AUC (>0.8), as well as low LR- (<0.1), and low NNS, since effective interventions can help prevent morbidity and mortality in the right patient population. This study is limited by a relatively small sample size and low statistical power of detecting the difference in AUCs. We also recognized that the study population may not necessarily represent the general population. It was performed in a single institution, with a less diversified population, where White constituted the majority of patients. The study findings should not, therefore, be applied to other ethnic groups. In addition, it is a select population who were already scheduled for DXA before being contacted about the study. Unlike the other 5 established tools, the cut-point for BMI was not predetermined, therefore, it would only apply to a similar population. It’s predictive value must be validated in a larger population based study prior to clinical use. The prevalence of osteoporosis in this study population may be different from the general population; however, comparison of performance was based on those parameters such as sensitivity and LR- that do not depend on the disease prevalence in the examined groups.
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Conclusion The results of this study suggest that none of the current and commonly used screening strategies are ideal. With a desire to have a simple, clinically applicable screening tool, a BMI (<28) may be considered a potential indicator for referring early postmenopausal White women for BMD testing. While a large multicenter, multi-ethnic, population-based study is needed to confirm the predictive value of BMI as an osteoporosis screening tool for younger postmenopausal women, unrelenting efforts from the medical community are needed to develop better strategies for osteoporosis screening in early postmenopausal women.
Conflict of interest Statement There are no conflicts of interest
Funding This work and manuscript preparation was unfunded
Ethical approval, consent or animal equivalent This study was originally approved by the Hartford Hospital institutional review board and the current research is being conducted under the review and approval of the Reading Health System institutional review board (IRB# 023‐10)
Contributors and Their Role Xuezhi Jiang, M.D., Ph.D. Role: Study design, data analysis, manuscript writing Lauren E. Good Role: literature search, creating database Ryan Spinka, B.S. Role: literature search, creating database Peter F. Schnatz, D.O. Role: Study design, manuscript writing
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These data have been presented in oral format at the 25th NAMS (North American Menopause Society) Annual Meeting on October 16, 2014, in Washington, DC. However, these data and results have not been previously published in manuscript form.
Précis: The performance of BMI<28 in predicting osteoporosis in young postmenopausal White women aged 50-64 may be as good as the current screening tools.
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Reference 1. Osteoporosis. ACOG Practice Bulletin No. 129, September 2012. Obstetrics & Gynecology 2012:120:718-34. 2. Chestunt CH. Osteoporosis, an underdiagnosed disease. JAMA 2001; 286(22):2865-6. 3. Bonnick S, Harris S, Kendler D, et al. Management of osteoporosis in postmenopausal women. 2010 Position Statement of The North Menopause Society: position statement. Menopause 2010;17:25-54. 4. WHO Fracture risk assessment tool (FRAX®). http://www.shef.ac.uk/FRAX/tool.aspx?country=9. Accessed September 24, 2015. 5. AHRQ. Screening for Osteoporosis: Clinical Summary of U.S. Preventive Services Task Force. The Guide to clinical Preventive Services. Rockville, MD: AHRQ; 2012. 6. Koh LK, Ben Sedrine W, Torralba TP, Kung A, Fujiwara S, Chan SP, et al. Osteoporosis Self-Assessment Tool for Asians (OSTA) Research Group. A simple tool to identify Asian women at increased risk of osteoporosis. Osteoporos Int 2001;12:699–705. 7. Lydick E, Cook K. Turpin J, Melton m, Stine R, Brynes C. Development and validation of a simple questionnaire to facilitate identification of women likely to have low bone density. Am J Mang Care 1998;4:37-48. 8. Cadarette SM, Jaglal SB, Kreiger N, McIsaac WJ, Darlington GA, Tu JV. Development and validation of the Osteoporosis Risk Assessment Instrument to facilitate selection of women for bone densitometry. Canadian Medical Assoc J. 2000;162(9):1289-94. 9. Ravn P, Cizza G, Bjarnason NH, Thompson D, Daley M, Wasnich RD, et al. Low bone mass index is an important risk factor for low bone mass and increased bone loss in early postmenopausal women. J of Bone & Mineral Research 1999;14(9):1622-7. 10. Iqbal SI, Morch LS, Rosenzweig M, Dela F. The outcome of bone mineral density measurements on patients referred from general practice. J of Clinical Densitometry 2005; 8(2):178-2. 11. Winzenberg, T and Jones, G. Dual energy X-ray absorptiometry. Australian Family Physician 2011; 40(1/2):43-44. 12. National Osteoporosis Foundation. Clinician’s Guide to Prevention and Treatment of Osteoporosis. Washington, DC: National Osteoporosis Foundation, 2010. 13. Schnatz PF, Barker KG, Marakovits KA, O'Sullivan DM. Effects of age at first pregnancy and breast-feeding on the development of postmenopausal osteoporosis. Menopause 2010; 17(6): 1161-6. 14. Geusens P, Hochberg MC, van der Voort DJ, et. al. Performance of risk indices for identifying low bone density in postmenopausal women. Mayo Clin Proc. 2002 Jul;77(7):629-37. 15. Jiang X, Westermann LB, Galleo GV, Demko J, Marakovits KA, Schnatz PF. Age as a Predictor of Osteoporotic Fracture Compared With Current Risk Prediction Models. Obstetrics & Gynecology 2013;122(5):1040-6.
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16. Michaëlsson K, Bergström R, Mallmin H, Holmberg L, Wolk A, Ljunghall S. Osteoporosis International 1996; 6(2):120-6. 17. Crandall CJ, Larson J, Gourlay ML, Donaldson MG, LaCroix A, Cauley JA, et al. Osteoporosis screening in postmenopausal women 50-64 years-old: comparison of U.S. Preventive Services Task Force Strategy and two traditional strategies in the Women’s Health Initiative. J of Bone & Mineral Research 2014; 29(7):1611-6. 18. Gourlay ML, Miller WC, Richy F, Garrett JM, Hanson LC, Reginster JY. Performance of Osteoporosis risk assessment tools in postmenopausal women aged 45-64 years. Osteoporosis International 2005; 16:921-7. 19. Cadarette SM, Jaglal SB, Murray TM. Validation of the simple calculated osteoporosis risk estimation (SCORE) for patient selection for bone densitometry. Osteoporosis International 1999;10(1):85-90. 20. Ben SW, Devogelaer JP, Kaufman JM, Goemaere S, Depresseux G, Zegels B, et al. Evaluation of the simple calculated osteoporosis risk estimation (SCORE) in a sample of white women from Belgium. Bone 2001;29(4):374-80. 21. Melton III LJ, Chrischilles EA, Cooper C, Lane AW, Riggs BL. Perspective: how many women have osteoporosis? J Bone Miner Res1992;7:1005–10. 22. Schnatz PF, Marakovits KA, Dubois M, O'Sullivan DM. Osteoporosis screening and treatment guidelines: are they being followed? Menopause 2011;18(10):1072-8.
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Figure Captions
Fig 1. This chart depicts the study recruitment process
17
Fig 2. This figure depicts the area under the Receiver Operating Characteristics (ROC) curve for BMI and the optimal cut-point of BMI at 27.7 kg/m2 in predicting osteoporosis
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Tables Table 1. Selected Baseline Characteristics of the 445 Study Participants Age, years
BMI
Race/Ethnicity
Corticosteroid use ever Rheumatoid arthritis Current smoking Hx of fragility fractures of the spine or hip after age 50 Parental hip fracture
All 50‐54 55‐59 60‐64 All <18.5 18.5‐24.9 25‐29.9 ≥30 White Black Hispanic Asian/other Yes No Yes No Yes No Yes No
N (%)
Mean (SD)
Median (Min‐Max)
445 (100) 155 (34.8) 150 (33.7) 140 (31.5) 445 (100) 1 (0.2) 195 (43.8) 132 (29.7) 117 (26.3) 423 (95.1) 9 (2.0) 8 (1.8) 5 (1.1) 27 (6.1) 418 (93.9) 20 (4.5) 425 (95.5) 36 (8.1) 409 (91.9) 3 (0.7) 442 (99.3)
57.2 (4.2)
57.1 (50.0‐64.9)
27.1 (5.8)
25.9 (17.8‐54.9)
Yes 59 (13.3) No 386 (86.7) Prior ET/HT (estrogen Yes 215 (48.3) use) No 230 (51.7) 10‐yr risk of major osteoporotic 445 (100) 7.2 (3.5) 6.4 (1.4‐29.0) fracture by FRAX without BMD (%) OST 445 (100) 2.8 (3.1) 2.2 (‐4.1‐14.8) ORAI 445 (100) 8.1 (4.2) 7.0 (0‐16) SCORE 445 (100) 7.1 (3.7) 7.7 (‐8.3‐15.4) BMI: Body mass index, kg/m2; Hx: History; Min: Minimum; Max: Maximum; ET = estrogen therapy; HT = hormonal therapy (estrogen and progestogen); OST: Osteoporosis Self‐assessment Tool; ORAI: Osteoporosis Risk Assessment Instrument; SCORE: Simple Calculated Osteoporosis Risk Estimation.
19 Table 2. P‐values for pairwise AUC comparisons Strategies
USPSTF (≥9.3%)
RF (≥1 risk OST (<2) factor) BMI (<28) 0.07 0.06 0.88 USPSTF (≥9.3%) ‐ 0.71 0.043¹ RF (≥1 risk factor) ‐ 0.058 OST (<2) ‐ SCORE (≥6) The shaded area indicates duplicate P values that were not shown. ¹AUC higher for OST, ² AUC higher for SCORE, ³ AUC higher for SCORE
SCORE (≥6)
ORAI (≥9)
0.45 0.002² 0.005³ 0.44 ‐
0.28 0.19 0.30 0.15 0.08
20 Table 3. Comparison of predictive accuracy of BMI and other screening modalities Sensitivity Specificity PPV (CI) AUC (CI) LR‐ NNS P value1 (CI) (%) (CI) (%) (%) BMI (<28) 95 (82‐99) 38 (34‐43) 13 (9‐17) 0.73 (0.65‐0.81) 0.13 8 <0.0001 USPSTF (≥9.3%) 24 (11‐40) 83 (79‐87) 12 (5‐21) 0.62 (0.52‐0.72) 0.92 9 0.017 RF (≥1 risk factor) 66 (49‐80) 62 (58‐67) 14 (9‐20) 0.64 (0.56‐0.72) 0.55 7 0.0006 OST (<2) 79 (63‐91) 56 (51‐61) 14 (10‐20) 0.73 (0.65‐0.81) 0.38 7 <0.0001 ORAI (≥9) 74 (57‐87) 62 (57‐67) 15 (11‐22) 0.69 (0.60‐0.78) 0.42 7 <0.0001 SCORE (≥6) 92 (79‐98) 34 (29‐39) 12 (8‐16) 0.75 (0.67‐0.83) 0.24 9 <0.0001 USPSTF (≥4.7%) 92 (79‐98) 21 (17‐25) 10 (7‐13) 0.62 (0.52‐0.72) 0.38 10 <0.0001 OST (<3) 87 (72‐96) 43 (38‐47) 12 (9‐17) 0.73 (0.65‐0.81) 0.30 8 <0.0001 1 P value : when AUC was compared to chance alone; RF: Risk Factor Based Approach; OST: Osteoporosis Self‐ assessment Tool; ORAI: Osteoporosis Risk Assessment Instrument; SCORE: Simple Calculated Osteoporosis Risk Estimation; NNS: The Number Needed to Scan (The number of DXA tests needed to detect one case of osteoporosis); AUC: Area under the ROC curve; PPV: Positive predictive value; FP: False positive; CI: 95% Confidence limit. LR‐: Negative likelihood ratio. Strategies