Validation of Three Tools for Identifying Postmenopausal Osteoporosis in a Han Population from six General Hospitals in Beijing: A Cross-sectional Study

Validation of Three Tools for Identifying Postmenopausal Osteoporosis in a Han Population from six General Hospitals in Beijing: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation of Three Tools for Identifying Postmenopausal Osteoporosis in a Han Population from six General Hospitals in Beijing: A Cross-sectional Study Ning An, Sijia Guo, Jisheng Lin, Haoxiang Zhuang, Jiayi Li, Hai Meng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3288926/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: To evaluate the validation of three screening tools for identifying Postmenopausal Osteoporosis (OP) including the Osteoporosis Self-Assessment Tool for Asians (OSTA), Fracture Risk Assessment Tool (FRAX), and Beijing Friendship Hospital Osteoporosis Self-assessment Tool (BFH-OST). Methods: A total of 745 community-dwelling Han Beijing postmenopausal females aged ≥45 years from six general hospitals were enrolled in this cross-sectional study. All participants completed a questionnaire and BMD was measured by dual-energy X-ray absorptiometry (DXA). Osteoporosis was defined by a T-score at least −2.5 SD less than that of average young adults in different diagnostic criteria [lumbar spine, femoral neck, total hip, worst hip, and World Health Organization (WHO)]. The abilities of the OSTA, FRAX, and BFH-OST to identify osteoporosis were analyzed by receiver operating characteristic (ROC) curves. Sensitivity, specificity, and area under the ROC curves (AUC) were calculated. Results: Osteoporosis prevalence ranged from 12.1% to 34.6% according to five different diagnostic criteria. The AUC range for the BFH-OST (0.726–0.813) was similar to the OSTA (0.723– 0.810), which revealed that both tools identified OP reliably. The AUC range for FRAX was 0.66–0.784, with corresponding sensitivities of 78.68% and specificities of 50.31%, suggesting limited predictive value. According to WHO criteria, the AUC values for the BFH-OST and for the OSTA were 0.752 and 0.748, with corresponding sensitivities of 86.82% and 86.05% and specificities of 50.51% and 51.13%, respectively. At defined thresholds, the BFH-OST and OSTA allowed avoidance of DXA in 63.1%–67.9% of participants, at a cost of missing 13.2%–26.0% of individuals with OP. Conclusions: OSTA and BFH-OST are both simple and effective tools for identifying postmenopausal osteoporosis in the Han Beijing population. osteoporosis Osteoporosis Self-Assessment Tool for Asians (OSTA) Beijing Friendship Hospital Osteoporosis Self-assessment Tool (BFH-OST) Fracture Risk Assessment Tool (FRAX) Figures Figure 1 Figure 2 Introduction Postmenopausal osteoporosis (OP) is a common skeletal metabolic disorder that affects postmenopausal women and involves microarchitectural deterioration and bone mineral density (BMD) reduction caused by menopause-related estrogen deprivation and increasing age [ 1 ]. Postmenopausal women with OP have a high susceptibility to fragility fractures, leading to an increasing burden on clinical resources and economic expenditure[ 2 ]. Due to the progressive aging trend of the population, the morbidity and mortality of OP and associated fractures grows year by year. Efforts to develop convenient and reliable screening tools for postmenopausal women are crucial to reduce the clinical and economic burdens in the aging society[ 3 – 5 ]. However, DXA is expensive, lacks portability, and exposes patients to radiation, making it unsuitable for community screening. BMD measured by DXA is currently considered to be the gold standard for the diagnosis of OP. According to the World Health Organization (WHO) criteria, OP is defined by a low BMD more than 2.5 standard deviations (SD) below the mean BMD for young healthy adults at any site of the lumbar spine (L1–L4), femoral neck, or total hip. However, OP identifying by DXA is not feasible due to its high cost, poor portability, and patient exposure to ionizing radiation. Previous studies have shown that asymptomatic OP and osteopenia are prevalent in postmenopausal women in China, but they often go unnoticed. The OSTA and the FRAX are two such tools that have been developed[ 6 , 7 ]. OSTA is based on age and weight and has proven effective in predicting OP risk in postmenopausal women. The efficacy and cutoff value for the OSTA may vary among diverse populations and diagnostic criteria. Therefore, it is necessary to revalidate the tool according to the local demographic profile, before OSTA can be applied clinically for OP identifying. FRAX predicts the 10-year probability of hip fracture and severe osteoporotic fracture based on individual clinical risk factors. However, both tools have limitations in terms of efficacy of use[ 8 , 9 ]. The Beijing Friendship Hospital Osteoporosis Self-Assessment Tool (BFH-OST) was developed to overcome these limitations[ 10 – 14 ]. It is based on four clinical risk factors, including age, weight, height, and fragile fracture history. BFH-OST is a cost-effective and powerful tool that can identify OP in postmenopausal women, especially Han Chinese women. In this study, the validation of BFH-OST, OSTA, and FRAX was compared to determine suitable thresholds for omitting BMD testing reasonably in a community-dwelling postmenopausal women population from six General Hospitals in Beijing[ 3 , 15 , 16 ]. Materials and methods The study was approved by the ethics committee of Beijing Friendship Hospital, Capital Medical University. We confirm that this study was conducted in accordance with the Declaration of Helsinki. All participants provided signed informed consent for enrollment in this study. A flow diagram of the study is shown in Fig. 1 . Study population We conducted the cross-sectional study and recruited a population of postmenopausal women (aged 45 years or older) from six general hospitals (Peking Union Medical College Hospital, Peking University Shougang Hospital, Beijing Fengtai Hospital, Beijing Haidian Hospital, Beijing Liangxiang Hospital, Civil Aviation General Hospital) from January 2021 to June 2022. The main inclusion and exclusion criteria are listed in Table 1 . None of the participants recruited for this study had been previously diagnosed with OP. Table 1 Inclusion and Exclusion Criteria for This Study Inclusion Criteria Exclusion Criteria Postmenopausal women (Menopause for more than 12 months). Ability to read and provide informed consent. Han Chinese residency in Beijing for ≥ 20 years. History of glucocorticoid use. History of thyroid supplements, antidepressant treatment. History of metabolic bone disease (e.g., type I diabetes, hyperparathyroidism or hypoparathyroidism, Paget’s disease, osteomalacia, renal osteodystrophy, osteogenesis imperfecta). History of organ transplantation. History of taking anti-OP medications. History of malignant tumor. Operation of lumbar spine or bilateral hips. Significant renal or hepatic impairment. refuse informed consent BMD and T- BMD Measurements and Data Collection via Questionnaire The study enrolled postmenopausal women (age ≥ 45 years) residing in six general hospitals. The participants were consecutively recruited for BMD measurements at the hip and spine. The participants were required to complete a structured questionnaire with a trained interviewer to provide information on demographic variables and clinical risk factors for osteoporosis (OP), including previous fracture history, current smoking, alcohol consumption, and parental hip fracture history[ 4 , 17 , 18 ]. Height was measured using a stadiometer, and weight was measured with an electronic balance. All enrolled patients were wearing light-weight indoor clothing during the measurement process. To ensure data quality, the database was established and checked by three researchers and then rechecked by senior researchers. A well-trained technologist performed the DXA scan process to minimize subjective error. The BMD T-score was calculated automatically by the DXA software[ 19 ]. The mean values of young Chinese women which had been set as the reference population were used to calculate the T-scores: femoral neck 0.803 ± 0.101 g/cm2, and total hip BMD 0.864 ± 0.113 g/cm2, L1–L4 BMD 0.967 ± 0.109 g/cm. OP was defined as a T-score of − 2.5 or lower at different skeletal sites, according to various criteria including the lumbar spine (L1–L4), femoral neck, total hip, worst hip (femoral neck or total hip), and the World Health Organization (WHO) criteria (any site)[ 1 ]. OSTA score The OSTA was originally developed by Koh et al in 2001, and it uses age and body weight as influencing factors in its final formula. The formula is as follows: (Body weight [kg] - Age [year]) *0.2. The result of the calculation is then rounded to the nearest integer. For instance, a 70-year-old man with a body weight of 75 kg would have an index of: (75 − 70) × 0.2 = 1. FRAX score FRAX is a widely used computer algorithm that calculates fracture risk in women based on clinical risk factors[ 20 , 21 ]. It takes into account both the risk of fracture and the risk of death and includes four models to calculate the probability of fracture. By combining clinical risk factors with bone mineral density (BMD) of the femoral neck, the 10-year fracture probability can be more accurately predicted. The version of FRAX used in this study was the one used in the Chinese mainland, which includes the Major Osteoporotic Fracture (FRAX-MOF) and Hip Fracture (FRAX-HF) models. BFH-OST score In our previous study, we developed the BFH-OST, which uses a multivariate regression model based on weight and history of previous fracture. The BFH-OST was shown to effectively identify postmenopausal women at increased risk for osteoporosis, leading to more prudent use of DXA BMD measurements. The model is calculated using the formula: [Body weight (kg) – Age (years)] × 0.5 + 0.1 × Height (cm) – [Previous fracture (0/1)]. The four key factors included in the model are body weight, age, height, and previous fracture. For example, a 60-year-old woman weighing 45 kg and measuring 160 cm with a previous fracture would have a BFH-OST index of 7.5, calculated as: (45–60) × 0.5 + 0.1 × 160–1 = 7.5. Statistical analysis The performance of the different tools (OSTA, FRAX without BMD, and BFH-OST) in predicting OP at the lumbar spine, total hip, femoral neck, worst hip (femoral neck or total hip), and any site (WHO criteria) was evaluated and compared. Data input and initial calculations were performed using Microsoft Excel software version 2021, and basic descriptive statistical analysis was conducted. To perform single-factor analysis, independent-samples Student’s t-test, non-parametric test, and one-way analysis of variance were used in SPSS software version 25.0 (IBM Corporation, Armonk, NY, USA). BMD T-scores were summarized at each site, and the performance of the three tools for predicting OP at each site was compared. To estimate the 95% confidence interval (CI), a ROC curve was constructed using MedCalc software version 11.5.0.0 (MedCalc Software, Ostend, Belgium). The predictive value of the three tools was determined based on the AUC as follows: perfectly predictive (AUC = 1), highly predictive (0.9 < AUC < 1), moderately predictive (0.7 < AUC < 0.9), less predictive (0.5 < AUC < 0.7), and non-predictive (AUC < 0.5)[ 22 , 23 ]. In all statistical analyses, a p-value less than 0.01 was considered statistically significant. Additionally, we calculated ideal thresholds based on the AUC results to maximize the diagnostic benefit and minimize missed diagnoses. Results In our study, a total of 925 participants were recruited for participation. According to the inclusion and exclusion criteria, 745 individuals were eligible for analysis. The characteristics of the participants are shown in Table 2 . We observed several differences between the osteoporosis and healthy individual groups, including age, weight, height, and previous fracture. However, there were no significant differences in terms of drinking, smoking, or family history. Specifically, the mean height and weight were lower in the osteoporosis group. Table 2 Summary of descriptive characteristics of Osteoporosis Group and Non- Osteoporosis Group Characteristics Osteoporosis Group Non-Osteoporosis Group P (t/χ2) Subjects, n 258 487 Weight, kg 57.79 ± 9.33 63.58 ± 8.84 < 0.001(8.356) Height, cm 157.4 ± 5.71 159.8 ± 4.76 < 0.001(5.754) Previous fracture 45/258(17.4%) 51/487(10.5%) 0.007(7.298) BMD, g/cm2 Femoral neck 0.605 ± 0.101 0.778 ± 0.126 < 0.001(20.405) Total hip 0.682 ± 0.129 0.868 ± 0.125 < 0.001(19.162) L1-L4 0.780 ± 0.145 0.993 ± 0.156 < 0.001(18.483) Family history 25(9.7%) 43(8.8%) 0.698(0.151) Current smoker 8(3.1%) 14(2.9%) 0.862(0.03) Alcohol 30 g/d 2(0.8%) 4(0.8%) 0.947(0.004) Notes : Data are presented as n (%) or mean ± standard deviation. BMD T-scores classified according to WHO criteria: osteoporosis (≤-2.5), non-Osteoporosis (>-2.5). Abbreviations: BMD, bone mineral density ROC curves outcomes In our study, we evaluated the ROC curves and AUCs for each tool according to different diagnostic criteria. A summary of the cutoff values and AUCs is presented in Table 3 . The AUC values of the tools for predicting OP ranged from 0.717 to 0.810 (OSTA), 0.652 to 0.751 (FRAX-MOF), 0.749 to 0.785 (FRAX-HF), and 0.719 to 0.813 (BFH-OST), depending on the five diagnostic criteria. Table 3 Test Performance in Identifying OP at Defined Low-Risk Thresholds Test Performance OSTA, % BFH-OST, % FRAX without BMD MOF, % HF, % Lumbar spine BMD≤-2.5 AUC (95% CI) 0.723 0.726 0.66 0.67 Z statistic 11.142 11.305 7.347 7.811 P-value < 0.001 < 0.001 < 0.001 < 0.001 Cut-off value 0.2 16.7 3.1 0.7 Sensitivity 86.53 88.08 84.97 77.2 Specificity 46.88 45.99 35.89 46.25 Positive predictive value 1.61 1.63 1.33 1.44 Negative predictive value 0.47 0.26 0.42 0.49 Femoral neck BMD≤-2.5 AUC (95% CI) 0.742 0.746 0.712 0.743 Z statistic 10.131 10.299 9.255 11.023 P-value < 0.001 < 0.001 < 0.001 < 0.001 Cut-off value 1.8 10.6 4.6 1.2 Sensitivity 63.64 60.14 64.34 71.33 Specificity 75.45 79.05 67.05 66.07 Positive predictive value 2.59 2.87 1.95 2.1 Negative predictive value 0.48 0.5 0.53 0.43 Total hip BMD≤-2.5 AUC (95% CI) 0.81 0.813 0.751 0.784 Z statistic 12.851 12.921 8.886 10.697 P-value < 0.001 < 0.001 < 0.001 < 0.001 Cut-off value 1.6 11.8 5.9 3.2 Sensitivity 78.02 78.02 63.74 53.85 Specificity 72.1 72.4 79.76 90.79 Positive predictive value 2.8 2.83 3.15 5.84 Negative predictive value 0.3 0.3 0.45 0.51 Worst hip BMD≤-2.5 AUC (95% CI) 0.749 0.753 0.715 0.749 Z statistic 10.763 10.913 9.414 11.342 P-value < 0.001 < 0.001 < 0.001 < 0.001 Cut-off value -1.8 11.5 4.7 1.2 Sensitivity 62.75 63.4 62.75 71.24 Specificity 76.35 76.52 69.26 67.06 Positive predictive value 2.65 2.7 2.04 2.16 Negative predictive value 0.49 0.48 0.54 0.43 Worst any set BMD≤-2.5(WHO) AUC (95% CI) 0.748 0.752 0.686 0.709 Z statistic 13.436 13.654 9.346 10.777 P-value < 0.001 < 0.001 < 0.001 < 0.001 Cut-off value 0.2 16.6 3.7 0.7 Sensitivity 86.05 86.82 72.87 78.68 Specificity 51.13 50.51 51.95 50.31 Positive predictive value 1.76 1.75 1.52 1.58 Negative predictive value 0.27 0.26 0.52 0.42 Abbreviations: BMD, body mineral density; OSTA, Osteoporosis self-Assessment Tool for Asians; BFH-OST, Beijing Friendship Hospital Osteoporosis Self-Assessment Tool; FRAX, fracture risk assessment tool; MOF, major osteoporotic fractures; HF, hip fractures Based on our results, the BFH-OST and OSTA yielded the best predictive value among these tools. With the WHO criteria, BFH-OST and OSTA had the highest AUC values (0.752 and 0.748), and there was no significant difference between them (p = 0.07). We further compared the ROC curve results under different diagnostic criteria and selected a more appropriate diagnostic criterion. Specifically, we found that according to the WHO criteria, Our BFH-OST is similar to OSTA in identifying OP (with a cut-off value of 9.1) in Fig. 2 , with a sensitivity of 73.6%, a specificity of 72.7%, and AUC values of 0.797. However, since this cut-off value is far from the previous cut-off, we carefully evaluated the differences in ROC curve results under different diagnostic criteria to select a more appropriate criterion. Lower Thresholds results In this study, the AUCs for the three tools revealed the best results in the total hip criterion. Based on the prevalence of OP and previous research reports, we selected the WHO criteria as the optimal threshold for our analysis. The low-risk thresholds were set at 16.6 for BFH-OST, 0.2 for OSTA, 3.7 for FRAX-MOF, and 0.7 for FRAX-HF. The performances of the different tools at these thresholds are summarized in Table 3 . When compared to the sensitivity and negative predictive value of FRAX-MOF, FRAX-HF, BFH-OST and OSTA performed better. With the WHO criteria, the AUC values for the BFH-OST and for the OSTA were 0.752 and 0.748, with corresponding sensitivities of 86.82% and 86.05% and specificities of 50.51% and 51.13%, respectively. At defined thresholds, the BFH-OST and OSTA allowed avoidance of DXA in 63.1–67.9% of participants, at a cost of missing 13.2–26.0% of individuals with OP. Discussion This study compared the performances of the OSTA, FRAX without BMD, and BFH-OST as prediction tools for postmenopausal OP in a Han Beijing women aged ≥ 45 years and attempted to define the optimal thresholds beyond which the unnecessary BMD testing for OP screening could be avoided. The prevalence of osteoporosis in postmenopausal women ranged from 12.1–34.6%, which is consistent with previous reports. The high prevalence of OP emphasizes the need for a reliable and convenient screening tool to identify Chinese postmenopausal women at risk, because most OP patients are asymptomatic, making the diagnosis easy to be missed. Five different diagnostic criteria (WHO, lumbar spine, worst hip, femoral neck, and total hip criteria) were included in the present analysis, and comparisons were made among these different screening tools. It has been reported that BMD of the femoral neck or total hip may be a better choice due to calcification of the abdominal aorta and interference of osteophytes in the lumbar spine. However, the low prevalence of osteoporosis in the femoral neck and total hip led to the adoption of the WHO criteria as the reference diagnostic standard in this study[ 24 , 25 ]. Table 2 shows that the average height, weight, and BMD index of the osteoporosis group were significantly lower than those of the non-osteoporosis group (P < 0.01). However, the OP group had more fragility fractures in the past compared to the non-OP group, which is consistent with previous studies. Our results also indicate that OP patients have a shorter height compared to the non-OP control group, which may be due to the physiological characteristics of the spine[ 26 ]. The vertebral bodies of osteoporosis patients are more prone to compression, and morphological changes in vertebral bodies and intervertebral spaces can lead to the shortening of the spine's length and even vertebral compression fractures[ 27 ]. However, smoking, drinking, and family history did not show any statistical difference, which may be related to the sample size of this experiment[ 28 , 29 ]. OSTA is a simple and effective screening tool for predicting OP. Due to its simple calculation, its prediction performance is better. Its AUC ranges from 0.717 to 0.810, and it has a higher OP recognition level in total hip (0.810) than in lumbar spine (0.717). According to WHO standards, the best cut-off value is -1. Compared to our data, the sensitivity is 86.05%, and the specificity is 51.13%. These results verify that OSTA is a reliable tool to predict OP among the Han population in Beijing. However, considering that racial and regional differences may affect these results, we suggest that the tool should be locally re-verified to adjust the critical value and improve prediction effectiveness. FRAX is well known as a prediction tool for the 10year probability of hip fractures (FRAX-HF) and major osteoporotic fractures (FRAX-MOF), and it also has been reported to be an effective tool in screening for OP. The study found that the overall AUCs for FRAX without BMD with all diagnostic criteria ranged from 0.652 to 0.785. According to the WHO criteria, the AUC for predicting osteoporosis appeared to be greater for FRAX-HF (0.709) than for FRAX-MOF (0.686), and there was a significant difference between them (p < 0.05). The optimal cutoff values for FRAX-HF and FRAX-MOF were 0.7% and 3.7%, respectively, beyond which the tools provided high sensitivity (78.68% and 72.87%, respectively) and specificity (50.31% and 51.95%, respectively). FRAX without BMD was validated to be a reliable screening tool. BFH-OST is a tool for predicting osteoporosis in postmenopausal women developed by Beijing Friendship Hospital. The study found that BFH-OST had the best prediction efficiency compared to FRAX, with an AUC range of 0.719 to 0.813. According to the WHO criteria, its sensitivity was 86.82%, and specificity was 50.51%. BFH-OST also had a high + LR (1.75) and a low -LR (0.26). There was no significant difference in AUC between BFH-OST and OSTA in predicting osteoporosis, but both were stronger than the predictive value of FRAX. These findings indicate that BFH-OST and OSTA have steady predictive efficiency in screening osteoporosis. The study shows that by missing 13.2% of patients with osteoporosis, BFH-OST can reduce the number of participants in BMD screening trials by 63.1%. Based on the comparison of various diagnostic criteria, when WHO criteria were set as the gold standard for osteoporosis in postmenopausal women, BFH-OST and OSTA showed better sensitivity and missed detection. All the participants were enrolled consecutively and long-term residents of Beijing. The results offered certain values for both general medical practitioners in general hospitals for OP identifying among the population and reducing the rate of missed diagnosis with omission of BMD measurement. The current research has several advantages. Firstly, it is a cross-sectional study rather than a retrospective study. Secondly, the study participants were postmenopausal women, and their information was collected from six different general hospitals, which represented the local population. In this study, strict inclusion and exclusion criteria were adopted to eliminate the influence of selection bias as much as possible. We recommend that more centers should participate to improve the size of the dataset and verify the accuracy of the model. Higher quality researches in randomized design with different population-based cohorts are expected in the future. Conclusions In conclusion, this study suggests that OSTA and BFH-OST are both effective tools for identifying OP in Han Postmenopausal women. Therefore, OSTA and BFH-OST have the potential to be the valuable screening tool for identifying women who require further evaluation for osteoporosis. Declarations Author contributions Each author made substantial contributions to this work. NA, SJG and QF contributed to the conception and design of the work. NA and SJG contributed to the acquisition of study data. NA, SJG, JSL contributed to the analysis and interpretation of data. ZHX, JYL, HM and NS revised this article. All authors have drafted the work or substantively revised it. All authors contributed to the article and approved the submitted version. Funding Information This work was supported by grants from the Beijing Municipal Commission of Health Technology Promotion Project (NO: BHTPP202007). Conflict of interest The authors report no conflicts of interest in this work. Sponsor’s Role The funding sources did not have any role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication. Acknowledgment We would like to show my gratitude to Dr. Li Ye from the Peking Union Medical College Hospital, Dr. Liu Zheng from Peking University Shougang Hospital, Dr. Wang Qi from Beijing Fengtai Hospital, Dr. Li Jian from Beijing Haidian Hospital, Dr. Ma Zhao from Beijing Liangxiang Hospital, and Dr. Xu Junchuan from Civil Aviation General Hospital for their support of the data in this article. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Consent for publication Not applicable. Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee of Beijing Friendship Hospital, Capital Medical University. The patients/participants provided their written informed consent to participate in this study. References Lane, N.E., Epidemiology, etiology, and diagnosis of osteoporosis. Am J Obstet Gynecol, 2006. 194 (2 Suppl): p. S3-11. 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Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Guo","suffix":""},{"id":232800016,"identity":"1d24c752-ca0a-4053-972f-f1d053f901ff","order_by":2,"name":"Jisheng Lin","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jisheng","middleName":"","lastName":"Lin","suffix":""},{"id":232800020,"identity":"396c773b-ae1c-4552-9780-1a08a339e8e4","order_by":3,"name":"Haoxiang Zhuang","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haoxiang","middleName":"","lastName":"Zhuang","suffix":""},{"id":232800026,"identity":"ce84fd42-3f7b-4a36-920c-500363cf9a8a","order_by":4,"name":"Jiayi Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Li","suffix":""},{"id":232800029,"identity":"2361789e-8cc4-4d98-bc4b-a8c34bf5319e","order_by":5,"name":"Hai Meng","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Meng","suffix":""},{"id":232800031,"identity":"5b0e3e97-5f41-4c13-80d8-0ea5329efef9","order_by":6,"name":"Nan Su","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Su","suffix":""},{"id":232800034,"identity":"af838252-990b-45be-90b9-b8be7e586eb9","order_by":7,"name":"Yong Yang","email":"","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Yang","suffix":""},{"id":232800035,"identity":"5c90e367-be9b-4b57-a139-ac78638cd834","order_by":8,"name":"Qi Fei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFACxgYGBhsGHn5m5oMPSNCSxsAj2c6WbECCTWkMDAbnecwEiFIsP7u57cGHBBsZ48MMZgwMNTbRBLUY3DnYbjgjIY3H7DBD2gOGY2m5DQS1SCS2SfP+OAzSctyAseEwYS3yM4Ba/iT85zFuZmyTIEoLww2gFoaEAzwGzMxsxGkB+qVNsichmUfiMBuzQQIxfpGf3f5M4keCnT1///mPDz7U2BDhMAlkTgJB5RhaRsEoGAWjYBRgAwBeeztv8vaktQAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Friendship Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Fei","suffix":""}],"badges":[],"createdAt":"2023-08-23 10:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3288926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3288926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43375694,"identity":"eacc1770-09e8-4f32-8aab-4b0d0d71d15a","added_by":"auto","created_at":"2023-09-19 16:18:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":382416,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of the study.\u003c/p\u003e","description":"","filename":"Fig1..png","url":"https://assets-eu.researchsquare.com/files/rs-3288926/v1/cf2e7c403327ba4166022ae8.png"},{"id":43375693,"identity":"9a113baf-caf8-4e81-83dd-337747ba05ca","added_by":"auto","created_at":"2023-09-19 16:18:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThis image is not available with this version.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3288926/v1/9ecd88bb84e9fed7d39ec71f.png"},{"id":53728315,"identity":"ce312b25-b02c-4d83-a03e-3443b7f7d741","added_by":"auto","created_at":"2024-03-29 12:22:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":625475,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3288926/v1/f0235a79-bc5a-435b-915d-d474c1705574.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Validation of Three Tools for Identifying Postmenopausal Osteoporosis in a Han Population from six General Hospitals in Beijing: A Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePostmenopausal osteoporosis (OP) is a common skeletal metabolic disorder that affects postmenopausal women and involves microarchitectural deterioration and bone mineral density (BMD) reduction caused by menopause-related estrogen deprivation and increasing age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Postmenopausal women with OP have a high susceptibility to fragility fractures, leading to an increasing burden on clinical resources and economic expenditure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to the progressive aging trend of the population, the morbidity and mortality of OP and associated fractures grows year by year. Efforts to develop convenient and reliable screening tools for postmenopausal women are crucial to reduce the clinical and economic burdens in the aging society[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, DXA is expensive, lacks portability, and exposes patients to radiation, making it unsuitable for community screening.\u003c/p\u003e \u003cp\u003eBMD measured by DXA is currently considered to be the gold standard for the diagnosis of OP. According to the World Health Organization (WHO) criteria, OP is defined by a low BMD more than 2.5 standard deviations (SD) below the mean BMD for young healthy adults at any site of the lumbar spine (L1\u0026ndash;L4), femoral neck, or total hip. However, OP identifying by DXA is not feasible due to its high cost, poor portability, and patient exposure to ionizing radiation. Previous studies have shown that asymptomatic OP and osteopenia are prevalent in postmenopausal women in China, but they often go unnoticed. The OSTA and the FRAX are two such tools that have been developed[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. OSTA is based on age and weight and has proven effective in predicting OP risk in postmenopausal women. The efficacy and cutoff value for the OSTA may vary among diverse populations and diagnostic criteria. Therefore, it is necessary to revalidate the tool according to the local demographic profile, before OSTA can be applied clinically for OP identifying. FRAX predicts the 10-year probability of hip fracture and severe osteoporotic fracture based on individual clinical risk factors. However, both tools have limitations in terms of efficacy of use[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Beijing Friendship Hospital Osteoporosis Self-Assessment Tool (BFH-OST) was developed to overcome these limitations[\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is based on four clinical risk factors, including age, weight, height, and fragile fracture history. BFH-OST is a cost-effective and powerful tool that can identify OP in postmenopausal women, especially Han Chinese women.\u003c/p\u003e \u003cp\u003eIn this study, the validation of BFH-OST, OSTA, and FRAX was compared to determine suitable thresholds for omitting BMD testing reasonably in a community-dwelling postmenopausal women population from six General Hospitals in Beijing[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e The study was approved by the ethics committee of Beijing Friendship Hospital, Capital Medical University. We confirm that this study was conducted in accordance with the Declaration of Helsinki. All participants provided signed informed consent for enrollment in this study. A flow diagram of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe conducted the cross-sectional study and recruited a population of postmenopausal women (aged 45 years or older) from six general hospitals (Peking Union Medical College Hospital, Peking University Shougang Hospital, Beijing Fengtai Hospital, Beijing Haidian Hospital, Beijing Liangxiang Hospital, Civil Aviation General Hospital) from January 2021 to June 2022. The main inclusion and exclusion criteria are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. None of the participants recruited for this study had been previously diagnosed with OP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and Exclusion Criteria for This Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInclusion Criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusion Criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostmenopausal women (Menopause for more than 12 months).\u003c/p\u003e \u003cp\u003eAbility to read and provide informed consent.\u003c/p\u003e \u003cp\u003eHan Chinese residency in Beijing for \u0026ge;\u0026thinsp;20 years.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of glucocorticoid use.\u003c/p\u003e \u003cp\u003eHistory of thyroid supplements, antidepressant treatment.\u003c/p\u003e \u003cp\u003eHistory of metabolic bone disease (e.g., type I diabetes, hyperparathyroidism or hypoparathyroidism, Paget\u0026rsquo;s disease, osteomalacia, renal osteodystrophy, osteogenesis imperfecta).\u003c/p\u003e \u003cp\u003eHistory of organ transplantation.\u003c/p\u003e \u003cp\u003eHistory of taking anti-OP medications. History of malignant tumor. Operation of lumbar spine or bilateral hips.\u003c/p\u003e \u003cp\u003eSignificant renal or hepatic impairment.\u003c/p\u003e \u003cp\u003erefuse informed consent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eBMD and T- BMD Measurements and Data Collection via Questionnaire\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe study enrolled postmenopausal women (age\u0026thinsp;\u0026ge;\u0026thinsp;45 years) residing in six general hospitals. The participants were consecutively recruited for BMD measurements at the hip and spine. The participants were required to complete a structured questionnaire with a trained interviewer to provide information on demographic variables and clinical risk factors for osteoporosis (OP), including previous fracture history, current smoking, alcohol consumption, and parental hip fracture history[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Height was measured using a stadiometer, and weight was measured with an electronic balance. All enrolled patients were wearing light-weight indoor clothing during the measurement process. To ensure data quality, the database was established and checked by three researchers and then rechecked by senior researchers. A well-trained technologist performed the DXA scan process to minimize subjective error. The BMD T-score was calculated automatically by the DXA software[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The mean values of young Chinese women which had been set as the reference population were used to calculate the T-scores: femoral neck 0.803\u0026thinsp;\u0026plusmn;\u0026thinsp;0.101 g/cm2, and total hip BMD 0.864\u0026thinsp;\u0026plusmn;\u0026thinsp;0.113 g/cm2, L1\u0026ndash;L4 BMD 0.967\u0026thinsp;\u0026plusmn;\u0026thinsp;0.109 g/cm. OP was defined as a T-score of \u0026minus;\u0026thinsp;2.5 or lower at different skeletal sites, according to various criteria including the lumbar spine (L1\u0026ndash;L4), femoral neck, total hip, worst hip (femoral neck or total hip), and the World Health Organization (WHO) criteria (any site)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOSTA score\u003c/h2\u003e \u003cp\u003eThe OSTA was originally developed by Koh et al in 2001, and it uses age and body weight as influencing factors in its final formula. The formula is as follows: (Body weight [kg] - Age [year]) *0.2. The result of the calculation is then rounded to the nearest integer. For instance, a 70-year-old man with a body weight of 75 kg would have an index of: (75\u0026thinsp;\u0026minus;\u0026thinsp;70) \u0026times; 0.2\u0026thinsp;=\u0026thinsp;1.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eFRAX score\u003c/h2\u003e \u003cp\u003eFRAX is a widely used computer algorithm that calculates fracture risk in women based on clinical risk factors[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It takes into account both the risk of fracture and the risk of death and includes four models to calculate the probability of fracture. By combining clinical risk factors with bone mineral density (BMD) of the femoral neck, the 10-year fracture probability can be more accurately predicted. The version of FRAX used in this study was the one used in the Chinese mainland, which includes the Major Osteoporotic Fracture (FRAX-MOF) and Hip Fracture (FRAX-HF) models.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBFH-OST score\u003c/h2\u003e \u003cp\u003eIn our previous study, we developed the BFH-OST, which uses a multivariate regression model based on weight and history of previous fracture. The BFH-OST was shown to effectively identify postmenopausal women at increased risk for osteoporosis, leading to more prudent use of DXA BMD measurements. The model is calculated using the formula:\u003c/p\u003e \u003cp\u003e[Body weight (kg) \u0026ndash; Age (years)] \u0026times; 0.5\u0026thinsp;+\u0026thinsp;0.1 \u0026times; Height (cm) \u0026ndash; [Previous fracture (0/1)].\u003c/p\u003e \u003cp\u003eThe four key factors included in the model are body weight, age, height, and previous fracture. For example, a 60-year-old woman weighing 45 kg and measuring 160 cm with a previous fracture would have a BFH-OST index of 7.5, calculated as: (45\u0026ndash;60) \u0026times; 0.5\u0026thinsp;+\u0026thinsp;0.1 \u0026times; 160\u0026ndash;1\u0026thinsp;=\u0026thinsp;7.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe performance of the different tools (OSTA, FRAX without BMD, and BFH-OST) in predicting OP at the lumbar spine, total hip, femoral neck, worst hip (femoral neck or total hip), and any site (WHO criteria) was evaluated and compared. Data input and initial calculations were performed using Microsoft Excel software version 2021, and basic descriptive statistical analysis was conducted. To perform single-factor analysis, independent-samples Student\u0026rsquo;s t-test, non-parametric test, and one-way analysis of variance were used in SPSS software version 25.0 (IBM Corporation, Armonk, NY, USA). BMD T-scores were summarized at each site, and the performance of the three tools for predicting OP at each site was compared. To estimate the 95% confidence interval (CI), a ROC curve was constructed using MedCalc software version 11.5.0.0 (MedCalc Software, Ostend, Belgium). The predictive value of the three tools was determined based on the AUC as follows: perfectly predictive (AUC\u0026thinsp;=\u0026thinsp;1), highly predictive (0.9\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;1), moderately predictive (0.7\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.9), less predictive (0.5\u0026thinsp;\u0026lt;\u0026thinsp;AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.7), and non-predictive (AUC\u0026thinsp;\u0026lt;\u0026thinsp;0.5)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In all statistical analyses, a p-value less than 0.01 was considered statistically significant. Additionally, we calculated ideal thresholds based on the AUC results to maximize the diagnostic benefit and minimize missed diagnoses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn our study, a total of 925 participants were recruited for participation. According to the inclusion and exclusion criteria, 745 individuals were eligible for analysis. The characteristics of the participants are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. We observed several differences between the osteoporosis and healthy individual groups, including age, weight, height, and previous fracture. However, there were no significant differences in terms of drinking, smoking, or family history. Specifically, the mean height and weight were lower in the osteoporosis group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of descriptive characteristics of Osteoporosis Group and Non- Osteoporosis Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOsteoporosis Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Osteoporosis Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP (t/\u0026chi;2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubjects, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.79\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001(8.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight, cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001(5.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45/258(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51/487(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007(7.298)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMD, g/cm2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemoral neck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605\u0026thinsp;\u0026plusmn;\u0026thinsp;0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778\u0026thinsp;\u0026plusmn;\u0026thinsp;0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001(20.405)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal hip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.682\u0026thinsp;\u0026plusmn;\u0026thinsp;0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.868\u0026thinsp;\u0026plusmn;\u0026thinsp;0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001(19.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL1-L4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780\u0026thinsp;\u0026plusmn;\u0026thinsp;0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993\u0026thinsp;\u0026plusmn;\u0026thinsp;0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001(18.483)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698(0.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.862(0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol 30 g/d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Data are presented as n (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. BMD T-scores classified according to WHO criteria: osteoporosis (\u0026le;-2.5), non-Osteoporosis (\u0026gt;-2.5).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMD, bone mineral density\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eROC curves outcomes\u003c/h2\u003e\n \u003cp\u003eIn our study, we evaluated the ROC curves and AUCs for each tool according to different diagnostic criteria. A summary of the cutoff values and AUCs is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The AUC values of the tools for predicting OP ranged from 0.717 to 0.810 (OSTA), 0.652 to 0.751 (FRAX-MOF), 0.749 to 0.785 (FRAX-HF), and 0.719 to 0.813 (BFH-OST), depending on the five diagnostic criteria.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest Performance in Identifying OP at Defined Low-Risk Thresholds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTest Performance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOSTA, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBFH-OST, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFRAX without BMD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMOF, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHF, %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLumbar spine BMD\u0026le;-2.5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemoral neck BMD\u0026le;-2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal hip BMD\u0026le;-2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorst hip BMD\u0026le;-2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorst any set BMD\u0026le;-2.5(WHO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBMD, body mineral density; OSTA, Osteoporosis self-Assessment Tool for Asians; BFH-OST, Beijing Friendship Hospital Osteoporosis Self-Assessment Tool; FRAX, fracture risk assessment tool; MOF, major osteoporotic fractures; HF, hip fractures\u003c/p\u003e\n \u003cp\u003eBased on our results, the BFH-OST and OSTA yielded the best predictive value among these tools. With the WHO criteria, BFH-OST and OSTA had the highest AUC values (0.752 and 0.748), and there was no significant difference between them (p\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e\n \u003cp\u003eWe further compared the ROC curve results under different diagnostic criteria and selected a more appropriate diagnostic criterion. Specifically, we found that according to the WHO criteria, Our BFH-OST is similar to OSTA in identifying OP (with a cut-off value of 9.1) in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, with a sensitivity of 73.6%, a specificity of 72.7%, and AUC values of 0.797. However, since this cut-off value is far from the previous cut-off, we carefully evaluated the differences in ROC curve results under different diagnostic criteria to select a more appropriate criterion.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLower Thresholds results\u003c/h2\u003e\n \u003cp\u003eIn this study, the AUCs for the three tools revealed the best results in the total hip criterion. Based on the prevalence of OP and previous research reports, we selected the WHO criteria as the optimal threshold for our analysis. The low-risk thresholds were set at 16.6 for BFH-OST, 0.2 for OSTA, 3.7 for FRAX-MOF, and 0.7 for FRAX-HF. The performances of the different tools at these thresholds are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. When compared to the sensitivity and negative predictive value of FRAX-MOF, FRAX-HF, BFH-OST and OSTA performed better. With the WHO criteria, the AUC values for the BFH-OST and for the OSTA were 0.752 and 0.748, with corresponding sensitivities of 86.82% and 86.05% and specificities of 50.51% and 51.13%, respectively. At defined thresholds, the BFH-OST and OSTA allowed avoidance of DXA in 63.1\u0026ndash;67.9% of participants, at a cost of missing 13.2\u0026ndash;26.0% of individuals with OP.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study compared the performances of the OSTA, FRAX without BMD, and BFH-OST as prediction tools for postmenopausal OP in a Han Beijing women aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years and attempted to define the optimal thresholds beyond which the unnecessary BMD testing for OP screening could be avoided. The prevalence of osteoporosis in postmenopausal women ranged from 12.1\u0026ndash;34.6%, which is consistent with previous reports. The high prevalence of OP emphasizes the need for a reliable and convenient screening tool to identify Chinese postmenopausal women at risk, because most OP patients are asymptomatic, making the diagnosis easy to be missed.\u003c/p\u003e \u003cp\u003eFive different diagnostic criteria (WHO, lumbar spine, worst hip, femoral neck, and total hip criteria) were included in the present analysis, and comparisons were made among these different screening tools. It has been reported that BMD of the femoral neck or total hip may be a better choice due to calcification of the abdominal aorta and interference of osteophytes in the lumbar spine. However, the low prevalence of osteoporosis in the femoral neck and total hip led to the adoption of the WHO criteria as the reference diagnostic standard in this study[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the average height, weight, and BMD index of the osteoporosis group were significantly lower than those of the non-osteoporosis group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the OP group had more fragility fractures in the past compared to the non-OP group, which is consistent with previous studies. Our results also indicate that OP patients have a shorter height compared to the non-OP control group, which may be due to the physiological characteristics of the spine[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The vertebral bodies of osteoporosis patients are more prone to compression, and morphological changes in vertebral bodies and intervertebral spaces can lead to the shortening of the spine's length and even vertebral compression fractures[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, smoking, drinking, and family history did not show any statistical difference, which may be related to the sample size of this experiment[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOSTA is a simple and effective screening tool for predicting OP. Due to its simple calculation, its prediction performance is better. Its AUC ranges from 0.717 to 0.810, and it has a higher OP recognition level in total hip (0.810) than in lumbar spine (0.717). According to WHO standards, the best cut-off value is -1. Compared to our data, the sensitivity is 86.05%, and the specificity is 51.13%. These results verify that OSTA is a reliable tool to predict OP among the Han population in Beijing. However, considering that racial and regional differences may affect these results, we suggest that the tool should be locally re-verified to adjust the critical value and improve prediction effectiveness.\u003c/p\u003e \u003cp\u003eFRAX is well known as a prediction tool for the 10year probability of hip fractures (FRAX-HF) and major osteoporotic fractures (FRAX-MOF), and it also has been reported to be an effective tool in screening for OP. The study found that the overall AUCs for FRAX without BMD with all diagnostic criteria ranged from 0.652 to 0.785. According to the WHO criteria, the AUC for predicting osteoporosis appeared to be greater for FRAX-HF (0.709) than for FRAX-MOF (0.686), and there was a significant difference between them (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The optimal cutoff values for FRAX-HF and FRAX-MOF were 0.7% and 3.7%, respectively, beyond which the tools provided high sensitivity (78.68% and 72.87%, respectively) and specificity (50.31% and 51.95%, respectively). FRAX without BMD was validated to be a reliable screening tool.\u003c/p\u003e \u003cp\u003eBFH-OST is a tool for predicting osteoporosis in postmenopausal women developed by Beijing Friendship Hospital. The study found that BFH-OST had the best prediction efficiency compared to FRAX, with an AUC range of 0.719 to 0.813. According to the WHO criteria, its sensitivity was 86.82%, and specificity was 50.51%. BFH-OST also had a high\u0026thinsp;+\u0026thinsp;LR (1.75) and a low -LR (0.26). There was no significant difference in AUC between BFH-OST and OSTA in predicting osteoporosis, but both were stronger than the predictive value of FRAX. These findings indicate that BFH-OST and OSTA have steady predictive efficiency in screening osteoporosis. The study shows that by missing 13.2% of patients with osteoporosis, BFH-OST can reduce the number of participants in BMD screening trials by 63.1%. Based on the comparison of various diagnostic criteria, when WHO criteria were set as the gold standard for osteoporosis in postmenopausal women, BFH-OST and OSTA showed better sensitivity and missed detection. All the participants were enrolled consecutively and long-term residents of Beijing. The results offered certain values for both general medical practitioners in general hospitals for OP identifying among the population and reducing the rate of missed diagnosis with omission of BMD measurement.\u003c/p\u003e \u003cp\u003eThe current research has several advantages. Firstly, it is a cross-sectional study rather than a retrospective study. Secondly, the study participants were postmenopausal women, and their information was collected from six different general hospitals, which represented the local population. In this study, strict inclusion and exclusion criteria were adopted to eliminate the influence of selection bias as much as possible. We recommend that more centers should participate to improve the size of the dataset and verify the accuracy of the model. Higher quality researches in randomized design with different population-based cohorts are expected in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study suggests that OSTA and BFH-OST are both effective tools for identifying OP in Han Postmenopausal women. Therefore, OSTA and BFH-OST have the potential to be the valuable screening tool for identifying women who require further evaluation for osteoporosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach author made substantial contributions to this work. NA, SJG and QF contributed to the conception and design of the work. NA and SJG contributed to the acquisition of study data. NA, SJG, JSL contributed to the analysis and interpretation of data. ZHX, JYL, HM and NS revised this article. All authors have drafted the work or substantively revised it. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the\u0026nbsp;Beijing Municipal Commission of Health Technology Promotion Project (NO: BHTPP202007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSponsor’s Role\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding sources did not have any role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to show my gratitude to Dr. Li Ye from the Peking Union Medical College Hospital, Dr. Liu Zheng from Peking University Shougang Hospital, Dr. Wang Qi from Beijing Fengtai Hospital, Dr. Li Jian from Beijing Haidian Hospital, Dr. Ma Zhao from Beijing Liangxiang Hospital, and Dr. Xu Junchuan from Civil Aviation General Hospital for their support of the data in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Ethics Committee of Beijing Friendship Hospital, Capital Medical University. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLane, N.E., \u003cem\u003eEpidemiology, etiology, and diagnosis of osteoporosis.\u003c/em\u003e Am J Obstet Gynecol, 2006. \u003cstrong\u003e194\u003c/strong\u003e(2 Suppl): p. S3-11.\u003c/li\u003e\n \u003cli\u003eTella, S.H. and J.C. Gallagher, \u003cem\u003ePrevention and treatment of postmenopausal osteoporosis.\u003c/em\u003e J Steroid Biochem Mol Biol, 2014. \u003cstrong\u003e142\u003c/strong\u003e: p. 155-70.\u003c/li\u003e\n \u003cli\u003eLupsa, B.C. and K. 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Fei, \u003cem\u003eBeijing Friendship Hospital Osteoporosis Self-Assessment Tool for Elderly Male (BFH-OSTM) vs Fracture Risk Assessment Tool (FRAX) for identifying painful new osteoporotic vertebral fractures in older Chinese men: a cross-sectional study.\u003c/em\u003e BMC Musculoskelet Disord, 2021. \u003cstrong\u003e22\u003c/strong\u003e(1): p. 596.\u003c/li\u003e\n \u003cli\u003eGuo, S., et al., \u003cem\u003eComparison of four tools to identify painful new osteoporotic vertebral fractures in the postmenopausal population in Beijing.\u003c/em\u003e Front Endocrinol (Lausanne), 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 1013755.\u003c/li\u003e\n \u003cli\u003eLin, J., et al., \u003cem\u003eValidation of three tools for identifying painful new osteoporotic vertebral fractures in older Chinese men: bone mineral density, Osteoporosis Self-Assessment Tool for Asians, and fracture risk assessment tool.\u003c/em\u003e Clin Interv Aging, 2016. \u003cstrong\u003e11\u003c/strong\u003e: p. 461-9.\u003c/li\u003e\n \u003cli\u003eRud, B., et al., \u003cem\u003eThe Osteoporosis Self-Assessment Tool versus alternative tests for selecting postmenopausal women for bone mineral density assessment: a comparative systematic review of accuracy.\u003c/em\u003e Osteoporos Int, 2009. \u003cstrong\u003e20\u003c/strong\u003e(4): p. 599-607.\u003c/li\u003e\n \u003cli\u003eFan, Z., et al., \u003cem\u003eComparison of OSTA, FRAX and BMI for Predicting Postmenopausal Osteoporosis in a Han Population in Beijing: A Cross Sectional Study.\u003c/em\u003e Clin Interv Aging, 2020. \u003cstrong\u003e15\u003c/strong\u003e: p. 1171-1180.\u003c/li\u003e\n \u003cli\u003eLeBoff, M.S., et al., \u003cem\u003eThe clinician\u0026apos;s guide to prevention and treatment of osteoporosis.\u003c/em\u003e Osteoporos Int, 2022. \u003cstrong\u003e33\u003c/strong\u003e(10): p. 2049-2102.\u003c/li\u003e\n \u003cli\u003eJohnston, C.B. and M. Dagar, \u003cem\u003eOsteoporosis in Older Adults.\u003c/em\u003e Med Clin North Am, 2020. \u003cstrong\u003e104\u003c/strong\u003e(5): p. 873-884.\u003c/li\u003e\n \u003cli\u003eAibar-Almaz\u0026aacute;n, A., et al., \u003cem\u003eCurrent Status of the Diagnosis and Management of Osteoporosis.\u003c/em\u003e Int J Mol Sci, 2022. \u003cstrong\u003e23\u003c/strong\u003e(16).\u003c/li\u003e\n \u003cli\u003eBoyanov, M.A., et al., \u003cem\u003eClinical Management of Women with Newly Diagnosed Osteoporosis: Data from Everyday Practice in Bulgaria.\u003c/em\u003e Rheumatol Ther, 2021. \u003cstrong\u003e8\u003c/strong\u003e(4): p. 1477-1491.\u003c/li\u003e\n \u003cli\u003eMagrey, M.N., S. Lewis, and M. Asim Khan, \u003cem\u003eUtility of DXA scanning and risk factors for osteoporosis in ankylosing spondylitis-A prospective study.\u003c/em\u003e Semin Arthritis Rheum, 2016. \u003cstrong\u003e46\u003c/strong\u003e(1): p. 88-94.\u003c/li\u003e\n \u003cli\u003eKanis, J.A., et al., \u003cem\u003eFRAX(\u0026reg;) with and without bone mineral density.\u003c/em\u003e Calcif Tissue Int, 2012. \u003cstrong\u003e90\u003c/strong\u003e(1): p. 1-13.\u003c/li\u003e\n \u003cli\u003eKanis, J.A., et al., \u003cem\u003eFRAX and its applications to clinical practice.\u003c/em\u003e Bone, 2009. \u003cstrong\u003e44\u003c/strong\u003e(5): p. 734-43.\u003c/li\u003e\n \u003cli\u003eZhang, X., et al., \u003cem\u003eMetabolomics Insights into Osteoporosis Through Association With Bone Mineral Density.\u003c/em\u003e J Bone Miner Res, 2021. \u003cstrong\u003e36\u003c/strong\u003e(4): p. 729-738.\u003c/li\u003e\n \u003cli\u003eYang, J., et al., \u003cem\u003eOpportunistic osteoporosis screening using chest CT with artificial intelligence.\u003c/em\u003e Osteoporos Int, 2022. \u003cstrong\u003e33\u003c/strong\u003e(12): p. 2547-2561.\u003c/li\u003e\n \u003cli\u003eWang, Y., et al., \u003cem\u003eOsteoporosis in china.\u003c/em\u003e Osteoporos Int, 2009. \u003cstrong\u003e20\u003c/strong\u003e(10): p. 1651-62.\u003c/li\u003e\n \u003cli\u003eCheng, X., et al., \u003cem\u003eOpportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study.\u003c/em\u003e J Bone Miner Res, 2021. \u003cstrong\u003e36\u003c/strong\u003e(3): p. 427-435.\u003c/li\u003e\n \u003cli\u003eFerrar, L., et al., \u003cem\u003ePrevalence of non-fracture short vertebral height is similar in premenopausal and postmenopausal women: the osteoporosis and ultrasound study.\u003c/em\u003e Osteoporos Int, 2012. \u003cstrong\u003e23\u003c/strong\u003e(3): p. 1035-40.\u003c/li\u003e\n \u003cli\u003eCohen, A., et al., \u003cem\u003eClinical characteristics and medication use among premenopausal women with osteoporosis and low BMD: the experience of an osteoporosis referral center.\u003c/em\u003e J Womens Health (Larchmt), 2009. \u003cstrong\u003e18\u003c/strong\u003e(1): p. 79-84.\u003c/li\u003e\n \u003cli\u003eKelsey, J.L., \u003cem\u003eRisk factors for osteoporosis and associated fractures.\u003c/em\u003e Public Health Rep, 1989. \u003cstrong\u003e104 Suppl\u003c/strong\u003e(Suppl): p. 14-20.\u003c/li\u003e\n \u003cli\u003eXia, J., et al., \u003cem\u003eSystemic evaluation of the relationship between psoriasis, psoriatic arthritis and osteoporosis: observational and Mendelian randomisation study.\u003c/em\u003e Ann Rheum Dis, 2020. \u003cstrong\u003e79\u003c/strong\u003e(11): p. 1460-1467.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"osteoporosis, Osteoporosis Self-Assessment Tool for Asians (OSTA), Beijing Friendship Hospital Osteoporosis Self-assessment Tool (BFH-OST), Fracture Risk Assessment Tool (FRAX)","lastPublishedDoi":"10.21203/rs.3.rs-3288926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3288926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To evaluate the validation of three screening tools for identifying Postmenopausal Osteoporosis (OP) including the Osteoporosis Self-Assessment Tool for Asians (OSTA), Fracture Risk Assessment Tool (FRAX), and Beijing Friendship Hospital Osteoporosis Self-assessment Tool (BFH-OST).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 745 community-dwelling Han Beijing postmenopausal females aged ≥45 years from six general hospitals were enrolled in this cross-sectional study. All participants completed a questionnaire and BMD was measured by dual-energy X-ray absorptiometry (DXA). Osteoporosis was defined by a T-score at least −2.5 SD less than that of average young adults in different diagnostic criteria [lumbar spine, femoral neck, total hip, worst hip, and World Health Organization (WHO)]. The abilities of the OSTA, FRAX, and BFH-OST to identify osteoporosis were analyzed by receiver operating characteristic (ROC) curves. Sensitivity, specificity, and area under the ROC curves (AUC) were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOsteoporosis prevalence ranged from 12.1% to 34.6% according to five different diagnostic criteria. The AUC range for the BFH-OST (0.726–0.813) was similar to the OSTA (0.723– 0.810), which revealed that both tools identified OP reliably. The AUC range for FRAX was 0.66–0.784, with corresponding sensitivities of 78.68% and specificities of 50.31%, suggesting limited predictive value. According to WHO criteria, the AUC values for the BFH-OST and for the OSTA were 0.752 and 0.748, with corresponding sensitivities of 86.82% and 86.05% and specificities of 50.51% and 51.13%, respectively. At defined thresholds, the BFH-OST and OSTA allowed avoidance of DXA in 63.1%–67.9% of participants, at a cost of missing 13.2%–26.0% of individuals with OP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e OSTA and BFH-OST are both simple and effective tools for identifying postmenopausal osteoporosis in the Han Beijing population.\u003c/p\u003e","manuscriptTitle":"Validation of Three Tools for Identifying Postmenopausal Osteoporosis in a Han Population from six General Hospitals in Beijing: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-19 16:18:04","doi":"10.21203/rs.3.rs-3288926/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5bfd6660-9353-4cb5-93c1-3d5adc9abb73","owner":[],"postedDate":"September 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T12:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-19 16:18:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3288926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3288926","identity":"rs-3288926","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}