Development and reporting of artificial intelligence in osteoporosis management.

An abundance of medical data and enhanced computational power have led to a surge in Artificial Intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17th, 2020, to February 1st, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the MI-CLAIM checklist. The systematic search yielded 97 articles that fell into five areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles) and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A 6th area, AI-driven clinical decision support, identified the studies from the five preceding areas which aimed to improve clinician efficiency, diagnostic accuracy and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

This review covers the recent advancements in artificial intelligence (AI) for managing osteoporosis, an increasingly prevalent condition that weakens bone tissues and increases fracture risk. Analyzing 97 studies from December 2020 to February 2023, the present work highlights how AI enhances bone properties assessment, osteoporosis classification, fracture detection and classification, risk prediction, and bone segmentation. A systematic qualitative assessment of the studies revealed improvements in study quality compared with the earlier review period, supported by innovative and more explainable AI approaches. AI shows promise in clinical decision support by offering novel screening tools that can help in the earlier identification of the disease, improve clinical workflows and patient prognosis. New pre-processing strategies and advanced model architectures have played a critical role in these improvements. Researchers have enhanced the accuracy and predictive performance of traditional methods by integrating clinical data with imaging data through advanced multi-factorial AI techniques. These innovations, paired with standardized development and validation processes, promise to personalize medicine and enhance patient care in osteoporosis management.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Relationship Between Overactive Bladder and Bone Fracture Risk in Female Patients.

BACKGROUND/AIM: Overactive bladder (OAB) has recently been recognized as an independent risk factor for falls and fractures. This study aimed to predict fracture risk in female patients with OAB symptoms. PATIENTS AND METHODS: We assessed and compared the fracture risk in newly diagnosed female patients with OAB to those without OAB using the Fracture Risk Assessment Tool (FRAX), and investigated the relationship between fracture risk and OAB severity. RESULTS: The present single-center, cross-sectional study included 177 female participants (79 with OAB, 98 without OAB). The OAB group was older (p=0.033) and shorter (p=0.010) compared to the non-OAB group. Compared to the non-OAB group, the OAB group had more patients with hypertension (p<0.001) and diabetes mellitus (p=0.011), as well as higher risks for major fractures (non-OAB group: 15.2±13.2%; OAB group: 23.6±14.1%; p<0.001) and hip fractures (non-OAB group: 6.3±11.0%; OAB group: 10.6±10.0%; p=0.007). In addition, those with moderate/severe OAB had the most significantly elevated risks for both major fractures (non-OAB group: 15.2±13.2%, mild-OAB: 17.6±12.5%, moderate/sever-OAB: 26.4±14.0%; p<0.001) and hip fractures (non-OAB group: 6.3±11.0%, mild-OAB: 6.5±7.6%, moderate/sever-OAB: 12.5±10.4%; p<0.001). Among the OAB symptoms, nocturia had the strongest correlation with fracture risk (major fracture, ρ=0.534; hip fracture, ρ=0.449; all p<0.001). CONCLUSION: Patients with severe OAB, and particularly severe nocturia, should be closely monitored with timely and aggressive symptom management; however, an interventional study incorporating the management of OAB symptoms is required to confirm whether the proactive management of OAB symptoms reduces the risk of fractures in older females.

Osteoporosis treatment prevents hip fracture similarly in both sexes: the FOCUS observational study.

Randomized trials have not been performed, and may never be, to determine if osteoporosis treatment prevents hip fracture in men. Addressing that evidence gap, we analyzed data from an observational study of new hip fractures in a large integrated healthcare system to compare the reduction in hip fractures associated with standard-of-care osteoporosis treatment in men versus women. Sampling from 271 389 patients age ≥ 65 who had a hip-containing computed tomography scan during care between 2005-2018, we selected all who subsequently had a first hip fracture (cases) after the CT scan (start of observation) and a sex-matched equal number of randomly selected patients. From those, we analyzed all who tested positive for osteoporosis (DXA-equivalent hip bone mineral density T-score ≤ -2.5, measured from the CT scan using VirtuOst). We defined "treated" as at least six months of any osteoporosis medication by prescription fill data during follow up; "not-treated" was no prescription fill. Sex-specific odds ratios of hip fracture for treated versus not-treated patients were calculated by logistic regression; adjustments included age, BMD T-score, a BMD-treatment interaction, body mass index, race/ethnicity, and seven baseline clinical risk factors. At two-year follow-up, 33.9% of the women (750/2211 patients) and 24.0% of the men (175/728 patients) were treated, primarily with alendronate; 51.3% and 66.3%, respectively, were not-treated; and 721 and 269, respectively, had a first hip fracture since the CT scan. Odds ratio of hip fracture for treated versus not-treated was 0.26 (95% confidence interval: 0.21-0.33) for women and 0.21 (0.13-0.34) for men; the ratio of these odds ratios (men:women) was 0.81 (0.47-1.37), indicating no significant sex effect. Various sensitivity and stratified analyses confirmed these trends, including results at five-year follow-up. Given these results and considering the relevant literature, we conclude that osteoporosis treatment prevents hip fracture similarly in both sexes.

Much evidence suggests that osteoporosis treatment should prevent hip fracture similarly in both sexes. However, because of their expense, randomized clinical trials to demonstrate that definitively have not been performed and may never be. As a result, osteoporosis testing and treatment is not as widely adopted for men as it is for women. Addressing that evidence gap, we analyzed data from over 250 000 patients in the Kaiser Permanente healthcare system in Southern California. Sampling a subset of all patients over a 13-year period who had had a computed tomography (CT or CAT) scan as part of their medical care for any reason, we measured bone mineral density from the CT scans to identify all patients who had osteoporosis at the hip and then used data from the electronic health records to determine statistically the risk of a future hip fracture for those who were treated for osteoporosis versus those who were not treated. We found that the reduction in risk of hip fracture associated with treatment did not differ between the sexes. These results demonstrate that treating osteoporosis in patients at high risk of hip fracture should reduce the risk of hip fracture similarly in both sexes.

The development and validation of a prediction model for imminent vertebral osteoporotic fracture in postmenopausal women.

PURPOSE: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis. METHODS: Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model. RESULTS: There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728-0.849) in the training cohort and 0.774 (95% CI, 0.705-0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742-0.839) and 0.741 (95% CI, 0.668-0.813) in the external validation cohorts. CONCLUSION: The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.

Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment.

Whether simultaneous automated ascertainments of prevalent vertebral fracture (auto-PVFx) and abdominal aortic calcification (auto-AAC) on vertebral fracture assessment (VFA) lateral spine bone density (BMD) images jointly predict incident fractures in routine clinical practice is unclear. We estimated the independent associations of auto-PVFx and auto-AAC primarily with incident major osteoporotic and secondarily with incident hip and any clinical fractures in 11 013 individuals (mean [SD] age 75.8 [6.8] years, 93.3% female) who had a BMD test combined with VFA between March 2010 and December 2017. Auto-PVFx and auto-AAC were ascertained using convolutional neural networks (CNNs). Proportional hazards models were used to estimate the associations of auto-PVFx and auto-AAC with incident fractures over a mean (SD) follow-up of 3.7 (2.2) years, adjusted for each other and other risk factors. At baseline, 17% (n = 1881) had auto-PVFx and 27% (n = 2974) had a high level of auto-AAC (≥ 6 on scale of 0 to 24). Multivariable-adjusted hazard ratios (HR) for incident major osteoporotic fracture (95% CI) were 1.85 (1.59, 2.15) for those with compared with those without auto-PVFx, and 1.36 (1.14, 1.62) for those with high compared with low auto-AAC. The multivariable-adjusted HRs for incident hip fracture were 1.62 (95% CI, 1.26 to 2.07) for those with compared to those without auto-PVFx, and 1.55 (95% CI, 1.15 to 2.09) for those high auto-AAC compared with low auto-AAC. The 5-year cumulative incidence of major osteoporotic fracture was 7.1% in those with no auto-PVFx and low auto-AAC, 10.1% in those with no auto-PVFx and high auto-AAC, 13.4% in those with auto-PVFx and low auto-AAC, and 18.0% in those with auto-PVFx and high auto-AAC. While physician manual review of images in clinical practice will still be needed to confirm image quality and provide clinical context for interpretation, simultaneous automated ascertainment of auto-PVFx and auto-AAC can aid fracture risk assessment.

Individuals with calcification of their abdominal aorta (AAC) and vertebral fractures seen on lateral spine bone density images (easily obtained as part of a bone density test) are much more likely to have subsequent fractures. Prior studies have not shown if both AAC and prior vertebral fracture both contribute to fracture prediction in routine clinical practice. Additionally, a barrier to using these images to aid fracture risk assessment at the time of bone density testing has been the need for expert readers to be able to accurately detect both AAC and vertebral fractures. We have developed automated computer methods (using artificial intelligence) to accurately detect vertebral fracture (auto-PVFx) and auto-AAC on lateral spine bone density images for 11 013 older individuals having a bone density test in routine clinical practice. Over a 5-year follow-up period, 7.1% of those with no auto-PVFx and low auto-AAC, 10.1% of those with no auto-PVFx and high auto-AAC, 13.4% of those with auto-PVFx and low auto-AAC, and 18.0% of those with auto-PVFx and high auto-AAC had a major osteoporotic fracture. Auto-PVFx and auto-AAC, ascertained simultaneously on lateral spine bone density images, both contribute to the risk of subsequent major osteoporotic fractures in routine clinical practice settings.

Effect of abdominal tissue thickness on trabecular bone score and fracture risk in adults with diabetes: the Manitoba BMD registry.

Individuals with type 2 diabetes have lower trabecular bone score (TBS) and increased fracture risk despite higher bone mineral density. However, measures of trabecular microarchitecture from high-resolution peripheral computed tomography are not lower in type 2 diabetes. We hypothesized that confounding effects of abdominal tissue thickness may explain this discrepancy, since central obesity is a risk factor for diabetes and also artifactually lowers TBS. This hypothesis was tested in individuals aged 40 years and older from a large DXA registry, stratified by sex and diabetes status. When DXA-measured abdominal tissue thickness was not included as a covariate, men without diabetes had lower TBS than women without diabetes (mean difference -0.074, P < .001). TBS was lower in women with versus without diabetes (mean difference -0.037, P < .001), and men with versus without diabetes (mean difference -0.007, P = .042). When adjusted for tissue thickness these findings reversed, TBS became greater in men versus women without diabetes (mean difference +0.053, P < .001), in women with versus without diabetes (mean difference +0.008, P < .001), and in men with versus without diabetes (mean difference +0.014, P < .001). During mean 8.7 years observation, incident major osteoporotic fractures were seen in 7048 (9.6%). Adjusted for multiple covariates except tissue thickness, TBS predicted fracture in all subgroups with no significant diabetes interaction. When further adjusted for tissue thickness, HR per SD lower TBS remained significant and even increased slightly. In conclusion, TBS predicts fractures independent of other clinical risk factors in both women and men, with and without diabetes. Excess abdominal tissue thickness in men and individuals with type 2 diabetes may artifactually lower TBS using the current algorithm, which reverses after accounting for tissue thickness. This supports ongoing efforts to update the TBS algorithm to directly account for the effects of abdominal tissue thickness for improved fracture risk prediction.

Individuals with type 2 diabetes are at increased fracture risk despite having higher bone mineral density (BMD). Previous studies suggest that trabecular bone score (TBS), a measure of bone derived from spine DXA images that can be used to assess fracture risk in addition to BMD, may be lower in individuals with type 2 diabetes. However, TBS is artificially lowered by greater abdominal obesity. We showed that abdominal obesity explained the lower TBS measurements that were seen in individuals with type 2 diabetes. However, even when we considered the effect of abdominal obesity, TBS was still able to predict major fractures in both women and men, with and without diabetes.

Guidelines for fracture risk assessment and management of osteoporosis in postmenopausal women and men above the age of 50 in Qatar.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide. PURPOSE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis. METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar. RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification. CONCLUSION: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

The novel metaPGS, integrating multiple fracture-related genetic traits, surpasses traditional polygenic scores in predicting fracture risk. Demonstrating a robust association with incident fractures, this metaPGS offers significant potential for enhancing clinical fracture risk assessment and tailoring prevention strategies. INTRODUCTION: Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. METHODS: We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. CONCLUSIONS: The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Cortical and trabecular mechanical properties in the femoral neck vary differently with changes in bone mineral density.

Graphical Abstract.

Skeletal muscle index based on CT at the 12th thoracic spine level can predict osteoporosis and fracture risk: a propensity score-matched cohort study.

Background: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study. Methods: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis. Results: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001). Conclusion: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model.

Background and Objective: Osteoporotic fractures significantly impact individuals's quality of life and exert substantial pressure on the social pension system. This study aims to develop prediction models for osteoporotic fracture and uncover potential risk factors based on Electronic Health Records (EHR). Methods: Data of patients with osteoporosis were extracted from the EHR of Xinhua Hospital (July 2012-October 2017). Demographic and clinical features were used to develop prediction models based on 12 independent machine learning (ML) algorithms and 3 hybrid ML models. To facilitate a nuanced interpretation of the results, a comprehensive importance score was conceived, incorporating various perspectives to effectively discern and mine critical features from the data. Results: A total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid model that synergistically combines the Support Vector Machine (SVM) and XGBoost algorithms demonstrated the best predictive performance in terms of accuracy and precision (above 90%) among all benchmark models. Blood Calcium, Alkaline phosphatase (ALP), C-reactive Protein (CRP), Apolipoprotein A/B ratio and High-density lipoprotein cholesterol (HDL-C) were statistically found to be associated with osteoporotic fracture. Conclusions: The hybrid machine learning model can be a reliable tool for predicting the risk of fracture in patients with osteoporosis. It is expected to assist clinicians in identifying high-risk fracture patients and implementing early interventions.

A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture.

An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in osteoporosis treatment in primary care, thereby lessening the societal burden imposed by fragility fractures. BACKGROUND: Osteoporotic fractures represent a major cause of morbidity and, in older adults, a precursor of disability, loss of independence, poor quality of life and premature death. Despite the detrimental health impact, osteoporosis remains largely underdiagnosed and undertreated worldwide. Subjects at risk for osteoporosis-related fractures are identified either via organised screening or case finding. In the absence of a population-based screening policy, subjects at high risk of fragility fractures are opportunistically identified when a fracture occurs or because of other clinical risk factors (CRFs) for osteoporotic fracture and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA). PURPOSE: This paper describes the development of a novel case-finding strategy, named Osteoporosis Diagnostic and Therapeutic Pathway (ODTP), which enables to identify subjects with osteoporosis or at varying risk of fragility fracture. This strategy is based on a specifically designed software tool, named "Bone Fragility Query" (BFQ), which analyses the electronic health record (EHR) databases of General Practitioners (GPs) to systematically identify individuals who should be prescribed DXA-BMD measurement, vertebral fracture assessment (VFA) and anti-osteoporosis medications (AOM). CONCLUSIONS: The ODTP through BFQ tool is a feasible, convenient and time-saving osteoporosis model of care for GPs during routine clinical practice. It enables GPs to shift their focus from what to do (clinical guidelines) to how to do it in the primary health care setting. It also allows a systematic approach to primary and secondary prevention of fragility fractures, thereby overcoming clinical inertia and contributing to closing the gap between evidence and practice for the management of osteoporosis in primary care.

Ten-year fracture risk in Japanese patients with myasthenia gravis: A comprehensive assessment using the fracture risk assessment tool.

BACKGROUND: Myasthenia gravis (MG) is an immune disorder that causes muscle weakness with an increasing prevalence, particularly among the elderly in Japan. Glucocorticoid treatment for MG is problematic for bone health because of reduced bone density and increased fracture risk. The fracture risk assessment tool (FRAX®) can estimate fracture risk, but its applicability in patients with MG remains uncertain. METHODS: A prospective cohort study was conducted on 54 patients with MG between April and July 2012. Bone mineral density (BMD) was measured, and FRAX® scores were calculated with and without BMD. We also adjusted FRAX® scores based on glucocorticoid dosage. Patients were monitored for major osteoporotic fractures (MOF) until June 2022. Statistical analyses included Kaplan-Meier curves and Cox proportional hazards models. RESULTS: The study group included 12 men and 42 women with a mean age of 62 years. Higher FRAX® scores correlated with increased fracture risk, particularly in the hip and lumbar regions. The 10-year fracture-free rate was significantly lower in the high-FRAX® score group. The FRAX® score using BMD is a significant predictor of MOF risk. The hazard ratio for FRAX® scores was 1.17 (95% CI 1.10-1.26). CONCLUSION: We demonstrated the effectiveness of the FRAX® tool in assessing fracture risk among patients with MG. High FRAX® scores correlated with increased fracture risk, emphasizing its importance. These findings support the incorporation of FRAX® assessment into clinical management to enhance patient care and outcomes. However, the small sample size and observational nature suggest a need for further research.

Geriatric nutritional risk index as a predictor for fragility fracture risk in elderly with type 2 diabetes mellitus: A 9-year ambispective longitudinal cohort study.

BACKGROUND & AIMS: The elderly are prone to fragility fractures, especially those suffering from type 2 diabetes mellitus (T2DM) combined with osteoporosis. Although studies have confirmed the association between GNRI and the prevalence of osteoporosis, the relationship between GNRI and fragility fracture risk and the individualized 10-year probability of osteoporotic fragility fractures estimated by FRAX remains unclear. This study aims to delve into the association between the GNRI and a fragility fracture and the 10-year probability of hip fracture (HF) and major osteoporotic fracture (MOF) evaluated by FRAX in elderly with T2DM. METHODS: A total of 580 patients with T2DM aged ≥60 were recruited in the study from 2014 to 2023. This research is an ambispective longitudinal cohort study. All participants were followed up every 6 months for 9 years with a median of 3.8 years through outpatient services, medical records, and home fixed-line telephone interviews. According to the tertiles of GNRI, all subjects were divided into three groups: low-level (59.72-94.56, n = 194), moderate-level (94.56-100.22, n = 193), and high-level (100.22-116.45, n = 193). The relationship between GNRI and a fragility fracture and the 10-year probability of HF and MOF calculated by FRAX was assessed by receiver operating characteristic (ROC) analysis, Spearman correlation analyses, restricted cubic spline (RCS) analyses, multivariable Cox regression analyses, stratified analyses, and Kaplan-Meier survival analysis. RESULTS: Of 580 participants, 102 experienced fragile fracture events (17.59%). ROC analysis demonstrated that the optimal GNRI cut-off value was 98.58 with a sensitivity of 75.49% and a specificity of 47.49%, respectively. Spearman partial correlation analyses revealed that GNRI was positively related to 25-hydroxy vitamin D [25-(OH) D] (r = 0.165, P < 0.001) and bone mineral density (BMD) [lumbar spine (LS), r = 0.088, P = 0.034; femoral neck (FN), r = 0.167, P < 0.001; total hip (TH), r = 0.171, P < 0.001]; negatively correlated with MOF (r = -0.105, P = 0.012) and HF (r = -0.154, P < 0.001). RCS analyses showed that GNRI was inversely S-shaped dose-dependent with a fragility fracture event (P < 0.001) and was Z-shaped with the 10-year MOF (P = 0.03) and HF (P = 0.01) risk assessed by FRAX, respectively. Multivariate Cox regression analysis demonstrated that compared with high-level GNRI, moderate-level [hazard ratio (HR) = 1.950; 95% confidence interval (CI) = 1.076-3.535; P = 0.028] and low-level (HR = 2.538; 95% CI = 1.378-4.672; P = 0.003) had an increased risk of fragility fracture. Stratified analysis exhibited that GNRI was negatively correlated with the risk of fragility fracture, which the stratification factors presented in the forest plot were not confounding factors and did not affect the prediction effect of GNRI on the fragility fracture events in this overall cohort population (P for interaction > 0.05), despite elderly females aged ≥70, with body mass index (BMI) ≥24, hypertension, and with or without anemia (all P < 0.05). Kaplan-Meier survival analysis identified that the lower-level GNRI group had a higher cumulative incidence of fragility fractures (log-rank, all P < 0.001). CONCLUSION: This study confirms for the first time that GNRI is negatively related to a fragility fracture and the 10-year probability of osteoporotic fragility fractures assessed by FRAX in an inverse S-shaped and Z-shaped dose-dependent pattern in elderly with T2DM, respectively. GNRI may serve as a valuable predictor for fragility fracture risk in elderly with T2DM. Therefore, in routine clinical practice, paying attention to the nutritional status of the elderly with T2DM and giving appropriate dietary guidance may help prevent a fragility fracture event.

The American Society for Bone and Mineral Research Task Force on clinical algorithms for fracture risk report.

Using race and ethnicity in clinical algorithms potentially contributes to health inequities. The American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee convened the ASBMR Task Force on Clinical Algorithms for Fracture Risk to determine the impact of race and ethnicity adjustment in the US Fracture Risk Assessment Tool (US-FRAX). The Task Force engaged the University of Minnesota Evidence-based Practice Core to conduct a systematic review investigating the performance of US-FRAX for predicting incident fractures over 10 years in Asian, Black, Hispanic, and White individuals. Six studies from the Women's Health Initiative (WHI) and Study of Osteoporotic Fractures (SOF) were eligible; cohorts only included women and were predominantly White (WHI > 80% and SOF > 99%), data were not consistently stratified by race and ethnicity, and when stratified there were far fewer fractures in Black and Hispanic women vs White women rendering area under the curve (AUC) estimates less stable. In the younger WHI cohort (n = 64 739), US-FRAX without bone mineral density (BMD) had limited discrimination for major osteoporotic fracture (MOF) (AUC 0.53 (Black), 0.57 (Hispanic), and 0.57 (White)); somewhat better discrimination for hip fracture in White women only (AUC 0.54 (Black), 0.53 (Hispanic), and 0.66 (White)). In a subset of the older WHI cohort (n = 23 918), US-FRAX without BMD overestimated MOF. The Task Force concluded that there is little justification for estimating fracture risk while incorporating race and ethnicity adjustments and recommends that fracture prediction models not include race or ethnicity adjustment but instead be population-based and reflective of US demographics, and inclusive of key clinical, behavioral, and social determinants (where applicable). Research cohorts should be representative vis-à-vis race, ethnicity, gender, and age. There should be standardized collection of race and ethnicity; collection of social determinants of health to investigate impact on fracture risk; and measurement of fracture rates and BMD in cohorts inclusive of those historically underrepresented in osteoporosis research.

Using race or ethnicity when calculating disease risk may contribute to health disparities. The ASBMR Task Force on Clinical Algorithms for Fracture Risk was created to understand the impact of the US Fracture Risk Assessment Tool (US-FRAX) race and ethnicity adjustments. The Task Force reviewed the historical development of FRAX, including the assumptions underlying selection of race and ethnicity adjustment factors. Furthermore, a systematic review of literature was conducted, which revealed an overall paucity of data evaluating the performance of US-FRAX in racially and ethnically diverse groups. While acknowledging the existence of racial and ethnic differences in fracture epidemiology, the Task Force determined that currently there is limited evidence to support the use of race and ethnicity­specific adjustments in US-FRAX. The Task Force also concluded that research is needed to create generalizable fracture risk calculators broadly applicable to current US demographics, which do not include race and ethnicity adjustments. Until such population­based fracture calculators are available, clinicians should consider providing fracture risk ranges for Asian, Black, and/or Hispanic patients and should engage in shared decision-making with patients about fracture risk interpretation. Future studies are required to evaluate fracture risk tools in populations inclusive of those historically underrepresented in research.

An Emergency Department-based system intervention to improve osteoporosis screening for older adults at high-risk of fracture.

Falls and osteoporosis are risk factors for fragility fractures. Bone mineral density (BMD) assessment is associated with better preventative osteoporosis care, but it is underutilized by those at high fracture risk. We created a novel electronic medical record (EMR) alert-driven protocol to screen patients in the Emergency Department (ED) for fracture risk and tested its feasibility and effectiveness in generating and completing referrals for outpatient BMD testing after discharge. The EMR alert was configured in 2 tertiary-care EDs and triggered by the term "fall" in the chief complaint, age (≥65 years for women, ≥70 years for men), and high fall risk (Morse score ≥ 45). The alert electronically notified ED study staff of potentially eligible patients. Participants received osteoporosis screening education and had BMD testing ordered. From November 15, 2020 to December 4, 2021, there were 2,608 EMR alerts among 2,509 patients. We identified 558 patients at high-risk of fracture who were screened for BMD testing referral. Participants were excluded for: serious illness (N = 141), no documented health insurance to cover BMD testing (N = 97), prior BMD testing/recent osteoporosis care (N = 58), research assistant unavailable to enroll (N = 53), concomitant fracture (N = 43), bedridden status (N = 38), chief complaint of fall documented in error (N = 38), long-term care residence (N = 34), participation refusal (N = 32), or hospitalization (N = 3). Of the 16 participants who had BMD testing ordered, 7 scheduled and 5 completed BMD testing. EMR alerts can help identify subpopulations who may benefit from osteoporosis screening, but there are significant barriers to identifying eligible and willing patients for screening in the ED. In our study targeting an innovative venue for osteoporosis care delivery, only about 1% of patients at high-risk of fracture scheduled BMD testing after an ED visit. Adequate resources during and after an ED visit are needed to ensure that older adults participate in preventative osteoporosis care.

Decomposing and simplifying the Fracture Risk Assessment Tool-a module from the Taiwan-specific calculator.

The Fracture Risk Assessment Tool (FRAX®) is a widely utilized country-specific calculator for identifying individuals with high fracture risk; its score is calculated from 12 variables, but its formulation is not publicly disclosed. We aimed to decompose and simplify the FRAX® by utilizing a nationwide community survey database as a reference module for creating a local assessment tool for osteoporotic fracture community screening in any country. Participants (n = 16384; predominantly women (75%); mean age = 64.8 years) were enrolled from the Taiwan OsteoPorosis Survey, a nationwide cross-sectional community survey collected from 2008 to 2011. We identified 11 clinical risk factors from the health questionnaires. BMD was assessed via dual-energy X-ray absorptiometry in a mobile DXA vehicle, and 10-year fracture risk scores, including major osteoporotic fracture (MOF) and hip fracture (HF) risk scores, were calculated using the FRAX®. The mean femoral neck BMD was 0.7 ± 0.1 g/cm2, the T-score was -1.9 ± 1.2, the MOF was 8.9 ± 7.1%, and the HF was 3.2 ± 4.7%. Following FRAX® decomposition with multiple linear regression, the adjusted R2 values were 0.9206 for MOF and 0.9376 for HF when BMD was included and 0.9538 for MOF and 0.9554 for HF when BMD was excluded. The FRAX® demonstrated better prediction for women and younger individuals than for men and elderly individuals after sex and age stratification analysis. Excluding femoral neck BMD, age, sex, and previous fractures emerged as 3 primary clinical risk factors for simplified FRAX® according to the decision tree analysis in this study population. The adjusted R2 values for the simplified country-specific FRAX® incorporating 3 premier clinical risk factors were 0.8210 for MOF and 0.8528 for HF. After decomposition, the newly simplified module provides a straightforward formulation for estimating 10-year fracture risk, even without femoral neck BMD, making it suitable for community or clinical osteoporotic fracture risk screening.

Use of Fracture Risk Assessment Tool in clinical practice and Fracture Risk Assessment Tool future directions.

Fracture Risk Assessment Tool is a free, online fracture risk calculator which can be used to predict 10-year fracture risk for women and men over age 50 years. It incorporates seven clinical risk factors and bone density to give a 10-year risk of major osteoporotic fracture and hip fracture. This dynamic tool can be used with patients at the bedside to help guide treatment decisions. There are some limitations to Fracture Risk Assessment Tool, with the most central limitation being the fact that inputs are binary. Much research has been done to try to refine Fracture Risk Assessment Tool to allow for more accurate risk prediction, and this article describes the data for adjusting Fracture Risk Assessment Tool depending on the clinical scenario such as the dose of glucocorticoid use, presence of diabetes and others. Recently, the new FRAXplus tool has been developed to address many of these concerns and will likely replace the old Fracture Risk Assessment Tool in the future. At the current time, it is available in beta form.

Methods for Refining the FRAX® Tool in Patients with Low Bone Density to Help Improve the Accuracy of Osteoporotic Fracture Risk PredictionMany patients who have low bone density develop fragility fractures, even those whose bone density is not yet within the osteoporosis range. Thus, in patients with low bone density, the health care team should estimate the risk of fracture to decide which patients should take medications to prevent fractures. Factors such as age, body mass index, steroid use, family history and other clinical factors can influence the fracture risk, in addition to bone density. There is an online calculator called the Fracture Risk Assessment Tool (FRAX®) which allows patients and doctors to integrate these risk factors with bone density in order to estimate the 10 year risk of osteoporotic fractures. FRAX® asks a series of yes/no questions about the patient's risks for fracture, and also takes into account the patient's country of residence, age, gender, race and bone density at the femur neck. However, there are some important limitations of this calculator. For example, we think that steroid medications increase the risk of fractures, and the higher the dose, the higher the risk of fractures. However, FRAX® only allows a "yes" or "no" input to the steroid use question. This paper aims to descibe methods for refining the FRAX® calculation to make the fracture risk prediction more accurate. For example, it describes a mathematical adjustment to FRAX® to account for the dose of steroids used. It also reviews methods for FRAX® adjustment for diabetes type 1 and 2, and severity of rheumatoid arthritis, among other considerations. Importantly, there is a new FRAX® tool that is currently in beta testing which will also further refine the accuracy of fracture risk prediction.