Medicare payment cuts for osteoporosis testing reduced use despite tests' benefit in reducing fractures

U-Net-Based Deep Learning Hybrid Model: Research and Evaluation for Precise Prediction of Spinal Bone Density on Abdominal Radiographs

Osteoporosis is a metabolic bone disorder characterized by the progressive loss of bone mass, which significantly increases the risk of fractures. While dual-energy X-ray absorptiometry is the standard technique for assessing bone mineral density, its use is limited in high-risk female populations. Additionally, quantitative computed tomography offers three-dimensional evaluations of bone mineral density but is costly and prone to motion artifacts. To overcome these limitations, this study proposes a hybrid model integrating U-Net and artificial neural networks, specifically focusing on abdominal X-ray images in the anteroposterior view for detailed skeletal analysis and improved accuracy in L2 vertebra mineral density measurement. The model targets female patients, who are at a higher risk for spinal disorders and osteoporosis. The U-Net model is employed for image preprocessing to reduce background noise and enhance bone tissue features, followed by analysis with the artificial neural network model to predict bone mineral density through nonlinear regression. The performance of the model, demonstrated by a high correlation coefficient of 0.77 and a low mean absolute error of 0.08 g per square centimeter, highlights its significance and effectiveness, particularly in comparison to dual-energy X-ray absorptiometry.

Prevention of Osteoporosis in SAMP6 Mice by Rikkunshi-To: Japanese Kampo Medicine

Osteoporosis can increase the risk of fracture in elderly patients, and insufficient control affects quality of life. Rikkunshi-To (RKT) has been prescribed for elderly patients to improve gastrointestinal function. We postulated that RKT has preventive potential for the development of osteoporosis. Thus, we developed a simple method to evaluate osteoporosis using a continuous series of X-ray images of femurs in mice, and investigated the effects of RKT on the development of osteoporosis in these mice. Male senescence-accelerated mouse strain P6 (SAMP6) mice, a model of senile osteoporosis in humans, were fed diets with or without RKT (1%). We collected X-ray images of the whole body of each mouse weekly and measured the ratio of cortical thickness of the femur (C/F index). The C/F index in SAMP6 mice fed the normal diet was increased between 50 and 80 days old, but it was significantly decreased after 120 days old. On the other hand, the C/F index in SAMP6 mice fed the RKT diet was increased between 50 and 80 days old; however, it remained unchanged throughout the experimental period. We also confirmed that the C/F index in SAMP6 mice fed the RKT diet suddenly decreased on the replacement of the RKT diet with a normal diet, suggesting that we can collect data related to a series of continuous changes in bone mass, and that RKT is useful for the prevention of osteoporosis.

Time trend analysis of osteoporosis prevalence among adults 50 years of age and older in the USA, 2005-2018

Using data from NHANES for years 2005-2018, we examined temporal trends in osteoporosis prevalence and the proportion of undiagnosed osteoporosis in the United States of America. Our results suggested statistically significant increases in osteoporosis prevalence across several demographic groups. These findings carry profound implications for public health, given increased life expectancy and burden of chronic diseases.

INTRODUCTION

This is the first study to assess osteoporosis prevalence trends over time and the proportion of undiagnosed osteoporosis across gender, ethnicity/race, and age groups.

METHODS

Observational time trend analyses were conducted using the 2005-2006, 2007-2008, 2009-2010, 2013-2014, and 2017-2018 National Health and Nutrition Examination Survey (NHANES) datasets, along with a descriptive analysis using the 2017-2018 NHANES dataset to capture the proportion of undiagnosed osteoporosis.

RESULTS

The findings showed a statistically significant increase in osteoporosis prevalence among women, non-Hispanic Whites, and all age groups (except for individuals 80 years of age and older) during the study period. A subsequent analysis examining individuals by both gender and ethnicity/race demonstrated a statistically significant increase among Other Hispanic men and non-Hispanic White women. Additional descriptive analyses found that 69.12% of individuals with osteoporosis went undiagnosed. Specifically, 86.88% of men and 84.77% of individuals 50-59 years of age with osteoporosis went undiagnosed, representing the two highest groups.

DISCUSSION AND CONCLUSION

The substantial and increasing prevalence among certain groups and sub-groups, along with the lack of diagnostic capture of osteoporosis, highlights existing gaps in public health efforts and care delivery infrastructure. This paper highlights high-risk groups and sub-groups that may benefit most from accelerated initiatives to reduce the burden of illness associated with osteoporosis.

Osteoporosis testing and treatment remain low in both Traditional Medicare and Medicare Advantage

Little is known regarding osteoporosis management between Traditional Medicare (TM) and Medicare Advantage (MA). MA beneficiaries had higher rates of osteoporosis testing and higher rates of osteoporosis drug treatment initiation rates. Following an osteoporotic fragility fracture, MA beneficiaries were more likely to be prescribed osteoporosis treatment. While osteoporosis testing and treatment initiation rates are low in both TM and MA, rates tended to be higher in MA.

PURPOSE

Osteoporosis represents a substantial clinical challenge in the United States, particularly for older women, and requires effective care coordination. Medicare Advantage (MA) plans have financial incentives in the form of star ratings to improve osteoporosis testing and treatment. The objective of this study was to compare osteoporosis management practices between Traditional Medicare (TM) beneficiaries and MA female enrollees.

METHODS

We conducted a cross-sectional study using a nationally representative 20% sample of 2017-2019 TM claims and MA encounter records. We identified 2,994,203 female TM beneficiaries and 1,924,132 MA enrollees. The exposure was enrollment in MA. The primary outcomes were the rates of guideline-recommended bone mineral density (BMD) testing and osteoporosis drug initiation following a new osteoporosis diagnosis and after a new osteoporotic fragility fracture.

RESULTS

MA beneficiaries had higher unadjusted (22.0% vs. 19.8% in TM; P < 0.001) and adjusted rates (0.8 percentage points [p.p.] higher; P < 0.001) of BMD testing. Osteoporosis drug treatment initiation rates were higher in the MA cohort, both unadjusted (24.9% vs. 20.3% in TM; P < 0.001) and adjusted (4.0 p.p. higher; P < 0.001). Following an osteoporotic fragility fracture, MA beneficiaries were more likely to be prescribed pharmacologic treatment (28.7% vs. 21.1% in TM; P < 0.001), with an adjusted increase of 5.9 p.p (P < 0.001).

CONCLUSION

Overall osteoporosis testing and treatment initiation rates in both TM and MA enrollees were low, with improved rates in MA compared to TM.

Trends in hip fracture surgery in the United States from 2016 to 2021: patient characteristics, clinical management, and outcomes

INTRODUCTION

Hip fractures are a serious health concern and a major contributor to healthcare resource utilisation. We aimed to investigate nationwide trends in the USA in patient characteristics and outcomes in patients after hip fracture repair surgery.

METHODS

From the Premier Healthcare dataset, we extracted patient encounters for surgical hip fracture repair from 2016 to 2021. Patient characteristics, comorbidities, complications, and anaesthetic and surgical details were analysed. Cochran-Armitage trend tests and simple linear regression were used to determine trends.

RESULTS

We included 347 086 hip fracture repair encounters. Notable trends included the following: median patient age declined from 82 yr [interquartile range: 73-88 yr] to 81 yr [interquartile range: 73-88 yr], (P-value=0.002), the proportion of female patients decreased from 68% to 66.2% (P-value=0.019); internal fixation was the most common intervention initially, but with a declining percentage from 49.9% to 43.8% (P-value <0.001); in general, patients carried a greater comorbidity burden, with the proportion with three or more Elixhauser comorbidities increasing from 56.4% to 58.6% (P-value=0.006); general anaesthesia remained the most common anaesthetic technique, from 68.90% to 56.80% without a significant trend; per 1000 inpatient days, the most common complication remained acute renal failure; despite a higher comorbidity burden, no complication showed a statistically significant upward trend, and many showed downward trends.

CONCLUSIONS

Over the 6-yr period from 2016 to 2021, a majority of hip fracture repairs continued to be performed under general anaesthesia but with that percentage declining over time. Notable trends included a lower percentage of female patients, an increase in femoral neck fractures, a higher comorbidity burden among patients, and a decrease in complications.

Opportunistic Assessment of Hip Fracture Risk Based on Chest CT

OBJECTIVE

Hip fracture (HF) has been described as the "last fracture of life" in the elderly, so the assessment of HF risk is extremely important. Currently, few studies have examined the relationship between imaging data from chest computed tomography (CT) and HF. This study demonstrated that pectoral muscle index (PMI) and vertebral body attenuation values could predict HF, aiming to opportunistically assess the risk of HF in patients without bone mineral density (BMD) based on chest CT for other diseases.

METHODS

In the retrospective study, 800 participants who had both BMD and chest CT were enrolled from January 2021 to January 2024. After exclusion, 472 patients were finally enrolled, divided into the healthy control (HC) group and the HF group. Clinical data were collected, and differences between the two groups were compared. A predictive model was constructed based on the PMI and CT value of the fourth thoracic vertebra (T4HU) by logistic regression analysis, and the predictive effect of the model was analyzed by using the receiver operating characteristic (ROC) curve. Finally, the clinical utility of the model was analyzed using decision curve analysis (DCA) and clinical impact curves.

RESULTS

Both PMI and T4HU were lower in the HF group than in the HC group (p < 0.05); low PMI and low T4HU were risk factors for HF. The predictive model incorporating PMI and T4HU on the basis of age and BMI had excellent diagnostic efficacy with an area under the curve (AUC) of 0.865 (95% confidence interval [CI]: 0.830-0.894, p < 0.01), sensitivity and specificity of 0.820 and 0.754, respectively. The clinical utility of the model was validated using calibration curves and DCA. The AUC of the predictive model incorporating BMD based on age and BMI was 0.865 (95% CI: 0.831-0.895, p < 0.01), with sensitivity and specificity of 0.698 and 0.711, respectively. There was no significant difference in diagnostic efficacy between the two models (p = 0.967).

CONCLUSIONS

PMI and T4HU are predictors of HF in patients. In the absence of dual-energy x-ray absorptiometry (DXA), the risk of HF can be assessed by measuring the PMI and T4HU on chest CT examination due to other diseases, and further treatment can be provided in time to reduce the incidence of HF.

Vertebral HU value and the pectoral muscle index based on chest CT can be used to opportunistically screen for osteoporosis

BACKGROUND

Existing studies have shown that computed tomography (CT) attenuation and skeletal muscle tissue are strongly associated with osteoporosis; however, few studies have examined whether vertebral HU values and the pectoral muscle index (PMI) measured at the level of the 4th thoracic vertebra (T4) are strongly associated with bone mineral density (BMD). In this study, we demonstrate that vertebral HU values and the PMI based on chest CT can be used to opportunistically screen for osteoporosis and reduce fracture risk through prompt treatment.

METHODS

We retrospectively evaluated 1000 patients who underwent chest CT and DXA scans from August 2020-2022. The T4 HU value and PMI were obtained using manual chest CT measurements. The participants were classified into normal, osteopenia, and osteoporosis groups based on the results of dual-energy X-ray (DXA) absorptiometry. We compared the clinical baseline data, T4 HU value, and PMI between the three groups of patients and analyzed the correlation between the T4 HU value, PMI, and BMD to further evaluate the diagnostic efficacy of the T4 HU value and PMI for patients with low BMD and osteoporosis.

RESULTS

The study ultimately enrolled 469 participants. The T4 HU value and PMI had a high screening capacity for both low BMD and osteoporosis. The combined diagnostic model-incorporating sex, age, BMI, T4 HU value, and PMI-demonstrated the best diagnostic efficacy, with areas under the receiver operating characteristic curve (AUC) of 0.887 and 0.892 for identifying low BMD and osteoporosis, respectively.

CONCLUSIONS

The measurement of T4 HU value and PMI on chest CT can be used as an opportunistic screening tool for osteoporosis with excellent diagnostic efficacy. This approach allows the early prevention of osteoporotic fractures via the timely screening of individuals at high risk of osteoporosis without requiring additional radiation.

Dual-energy X-ray Absorptiometry Trends Among US Medicare Beneficiaries: 2005-2019

INTRODUCTION

Bone density measured using dual-energy X-ray absorptiometry (DXA) volume, performance site and interpreters have changed in the US since 2005. The purpose of this report is to provide updated trends in DXA counts, rates, place of service and interpreter specialty for the Medicare fee-for-service population.

METHODS

The 100 % Medicare Physician/Supplier Procedure Summary Limited Data Set between 2005-2019 was used. DXA counts and annual rates per 10,000 Medicare beneficiaries were calculated. Annual distributions of scan performance location, provider type and interpreter specialty were described. Place of service trends (significance assigned at p < 0.05) of the mean annual share of DXA utilization were identified using linear regression.

RESULTS

Annual DXA use per 10,000 beneficiaries peaked in 2008 at 832, declined to 656 in 2015 then increased (p < 0.001) by 38 per year to 807 in 2019. From 2005 to 2019 DXA performance in office settings declined from 70.7 % to 47.2 %. Concurrently, outpatient hospital (OH) DXA increased from 28.6 % to 51.7 %. In 2005, 43.5 % of DXAs were interpreted by radiologists. This increased (p < 0.001) in the office and OH, averaging 0.3 and 2.0 percentage points per year respectively, reaching 73.5 % in 2019. Interpretation by most non-radiologist specialties declined (p < 0.001).

CONCLUSIONS

From 2005-2019, total DXA use among Medicare beneficiaries declined reaching a nadir in 2015 then returned to 2005 levels by 2019. Office DXA declined since 2005 with 51.7 % of all scans now occurring in an OH setting. The proportion of DXAs interpreted by radiologists increased over time, reaching 73.5 % in 2019.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria

RATIONALE AND OBJECTIVES

Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool.

MATERIALS AND METHODS

We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV).

RESULTS

Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%.

CONCLUSION

Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

Vertebral Bone Quality Score Independently Predicts Proximal Junctional Kyphosis and/or Failure After Adult Spinal Deformity Surgery

BACKGROUND

Proximal junctional kyphosis (PJK) and proximal junctional failure (PJF) can be catastrophic complications associated with adult spinal deformity (ASD) surgery. These complications are markedly influenced by osteoporosis, leading to additional vertebral fracture and pedicle screw loosening. The MRI-based vertebral bone quality score (VBQ) is a newly developed tool that can be used to assess bone quality.

OBJECTIVE

To investigate the utility of the VBQ score in predicting PJK and/or PJF (PJF/PJK) after ASD correction.

METHODS

We conducted a retrospective chart review to identify patients age ≥50 years who had received ASD surgery of 5 or more thoracolumbar levels. Demographic, spinopelvic parameters, and procedure-related variables were collected. Each patient's VBQ score was calculated using preoperative T1-weighted MRI. Univariate analysis and multivariate logistic regression were performed to determine potential risk factors of PJK/PJF. Receiver operating characteristic analysis and area-under-the-curve values were generated for prediction of PJK/PJF.

RESULTS

A total of 116 patients were included (mean age, 64.1 ± 6.8 years). Among them, 34 patients (29.3%) developed PJK/PJF. Mean VBQ scores were 3.13 ± 0.46 for patients with PJK/PJF and 2.46 ± 0.49 for patients without, which was significantly different between the 2 groups ( P < .001). On multivariate analysis, VBQ score was the only significant predictor of PJK/PJF (odds ratio = 1.745, 95% CI = 1.558-1.953, P < .001), with a predictive accuracy of 94.3%.

CONCLUSION

In patients undergoing ASD correction, higher VBQ was independently associated with PJK/PJF occurrence. Measurement of VBQ score on preoperative MRI may be a useful adjunct to ASD surgery planning.

Health Insurance Coverage as a Social Determinant of Osteoporosis Diagnosis in a Population-Based Cohort Study of Older American Adults

Social determinants of health theoretical frameworks identify health insurance coverage as a determinant of older adults' osteoporosis diagnoses, which results in health inequities. In this research, we used the longitudinal Health and Retirement Study dataset of older United States adults, sampled biennially from 2012 to 2016. Logistic regressions estimated odds of osteoporosis diagnosis with and without a bone scan and/or hip fracture, holding insurance type, and health and demographic factors constant. Results were validated using the National Health and Nutrition Examination Survey. Probable underdiagnosing is present in older adults identifying as Black/African American and as males without a bone scan, regardless of fracture status, potentially as products of structural racism and sexism. Models including a bone scan show a reduction in disparities. These findings suggest having a bone scan is still crucial for addressing health inequities in older adults, and remedying barriers to accessing a scan is paramount.

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

BACKGROUND

Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation.

OBJECTIVE

The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique.

METHODS

We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined.

RESULTS

Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature's integrated contribution and interpretation for individual risk were determined.

CONCLUSIONS

The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.

A computed tomography-based radiomics nomogram for predicting osteoporotic vertebral fractures: A longitudinal study

CONTEXT

Fractures are serious consequence of osteoporosis in old adults. However, few longitudinal studies showed the role of computed tomography (CT)-based radiomics in predicting osteoporotic fractures.

OBJECTIVE

We evaluated the performance of CT radiomics-based model for osteoporotic vertebral fractures (OVF) in a longitudinal study.

METHODS

7906 subjects without OVF who were aged over 50 years, and underwent CT scans between 2016 and 2019 were enrolled and followed up until 2021. Seventy-two cases of new OVF were identified. One hundred and forty-four people without OVF during follow-up were selected as control. Radiomics features were extracted from baseline CT images. CT values of trabecular bone, and area and density of erector spinae were determined. Cox regression analysis was used to identify the independent associated factors. The predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration curve and decision curve.

RESULTS

CT value of vertebra (adjusted hazard ratio (aHR) = 2.04, 95% confidence interval (CI): 1.07, 3.89), radiomics score (aHR = 6.56, 95%CI:3.47, 12.38) and area of erector spinae (aHR = 1.68, 95%CI: 1.02, 2.78) were independently associated with OVF. Radscore was associated with severe OVF (aHR = 6.00, 95% CI:2.78-12.93). The nomogram showed good discrimination with a C-index of 0.82 (95%CI: 0.77, 0.87). The area under the curve of nomogram and radscore were both higher than osteoporosis + muscle area for 3-year and 4-year risk of fractures (p < 0.05). Decision curve also demonstrated that the radiomics nomogram was useful.

CONCLUSIONS

Bone radiomics is associated with OVF and the nomogram based on radiomics signature and muscle provides a tool for the prediction of OVF.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria

RATIONALE AND OBJECTIVES

Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool.

MATERIALS AND METHODS

The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture.

RESULTS

Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively.

CONCLUSION

Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

Utilizing Graphical Analysis of Chest Radiographs for Primary Screening of Osteoporosis

Background and Objectives: Osteoporosis is a major risk of fractures, harming patients’ quality of life. Dual-energy X-ray absorptiometry (DXA), which can detect osteoporosis early, is too expensive to be conducted on a regular basis. Therefore, we aimed to evaluate a screening method using chest radiographs developed in Japan applied to another population. Materials and Methods: Fifty-five patients who had a chest radiograph and DXA and applied within three months of each test were recruited from the patient database of Semmelweis University (Budapest, Hungary). Graphical analysis of the chest radiographs was conducted to identify the ratio of the cortical bone in the clavicle of each patient. Two researchers performed the analysis, and multiple regression was conducted to determine the bone mineral density of each patient provided by DXA. Results: The Pearson correlation between two examiners’ determinations of the cortical bone ratio was 0.769 (p < 0.001). The multiple regression model proved to be statistically significant in identifying osteoporosis, but the model adopted for the Hungarian population was different compared to the Japanese population. Conclusions: This simple, economic Japanese graphical analysis method for chest radiographs may be feasible in detecting osteoporosis. Further studies with a larger population of patients with greater variety of ethnicity would be of value in improving the accuracy of this model.

Trends in osteoporosis diagnosis and management in Australia

Trends in bone mineral density monitoring, and drug treatment for osteoporosis, in Australia were examined. Rates of DEXA scanning have increased in response to changes to government policy affecting reimbursement. The drug denosumab is being utilised at an increasing rate, while bisphosphonate use has declined. Osteoporosis prevalence remained stable over the same timeframe, while rate of hip fractures declined, suggesting that introduction of osteoporosis screening was associated with a reduction in adverse osteoporosis outcomes, but may also have been associated with overutilisation.

INTRODUCTION

Radiology interventions to diagnose and medications to manage osteoporosis in Australia are reimbursed under the Medicare benefits schedule (MBS) and Pharmaceutical Benefits Scheme (PBS). Monitoring of these databases enables changes in utilisation of these practices to be monitored over time.

METHODS

This study examined rates of utilisation for bone mineral density (BMD) measurement and osteoporosis pharmacotherapy subsidised under the MBS. Rates of osteoporosis and hip fracture were estimated using data reported by the Australian Bureau of Statistics (ABS) and Australian Institute for Health and Welfare (AIHW).

RESULTS

Rates of BMD measurement increased since the technology was first reimbursed, with changes to policy regarding reimbursement for screening for individuals over 70 leading to an increase in BMD measurement after 2007. Prescribing rates also increased over time, initially with the introduction of oral bisphosphonates and subsequently for denosumab, which has subsequently become the most commonly prescribed agent for osteoporosis management in Australia, while bisphosphonate use has declined. Osteoporosis prevalence in Australia has remained relatively static at 3-4% of the population since 2001 to 2017, while rates of minimal trauma hip fracture hospitalisations have declined from 195 per 100,000 to 174 per 100,000 in the same timeframe.

CONCLUSION

Available data indicates that osteoporosis screening rates changed over time from 2001 to 2018 and that changes to government policy had a significant effect on the rates at which screening was performed. Over the same timeframe, there was a sustained reduction in hip fracture hospitalisation rates, with no change to reported osteoporosis prevalence. This suggests that policy changes permitting unlimited access to BMD measurement were associated with a reduction in osteoporotic fractures, but may also have been associated with overutilisation. Prospective studies to assess the efficacy of specific policies to ensure screening is performed in accordance with best-practice guidelines may be desirable.

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning-based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual-energy X-ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1089 chest radiographs, 2010 and 2017). Using a well-performed deep learning model, we trained the OsPor-screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T-score ≤ -2.5) in a supervised learning manner. The OsPor-screen model was assessed in the internal and external test sets. We performed substudies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor-screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient-weighted class activation maps (Grad-CAMs). The OsPor-screen model showed promising performances. Osteoporosis screening with the OsPor-screen model achieved an AUC of 0.91 (95% confidence interval [CI], 0.90-0.92) and an AUC of 0.88 (95% CI, 0.85-0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad-CAMs is unclear, these results suggest that a deep learning-based model using chest radiographs could have the potential to be used for opportunistic automated screening of patients with osteoporosis in clinical settings. © 2021 American Society for Bone and Mineral Research (ASBMR).

The Role of Secondary Imaging Techniques for Assessing Bone Mineral Density in Elderly Ankle Fractures

Elderly ankle fractures in the elderly represent a substantial healthcare burden. Dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosis of osteoporosis. However, there is emerging research regarding secondary imaging techniques to evaluate bone mineral density (BMD). The purpose of this systematic review was to summarize the role of secondary imaging techniques for measuring BMD in elderly ankle fractures. A literature search was undertaken using relevant search terms. Articles were screened for suitability and data extracted where studies met inclusion criteria and were of sufficient quality. Eight studies were included in the systematic review. Computed tomography (CT) may have a role in preoperative surgical planning, provide an explanation for injury patterns in elderly patients, and may be correlated with clinical outcomes. High-resolution peripheral quantitative CT may be better suited than DXA for the assessment of ankle fractures due to the resolution of the image and its ability to distinguish between bone compartments, as well as provide a more accurate estimation of bone quality. Quantitative ultrasound has shown promise as a tool for measuring BMD in patients with osteoporosis, but is not able to detect osteoporosis in patients with ankle fractures. This paper helps define the role of each modality in the spectrum of care in the evaluation of osteoporosis as it pertains to elderly ankle fractures.

Assessing the roles of demographic, social, economic, environmental, health-related, and political factors on risk of osteoporosis diagnosis among older adults

Chronic stress from social/environmental pressures has been proposed to affect bone health through increased inflammation. We demonstrate that inflammation from prolonged stress does not cause changes to bone health through inflammation but instead impacts access to health care, social inequalities, and overall health, which in turn impact bone health.

PURPOSE

The study provides a comprehensive assessment of how determinants of health across demographic, psychological, mobility-related, health, environmental, and economic domains are associated with the diagnosis of osteoporosis and tests three hypotheses: (1) a diverse set of variables across domains will predict osteoporosis, (2) chronic inflammation as a result of stress (represented by high-sensitivity C-reactive protein) will not be associated with osteoporosis, and (3) the model developed will have high accuracy in predicting osteoporosis.

METHODS

Logistic regression and Cox proportional hazards models of osteoporosis diagnosis were estimated using data from 14,792 and 13,169 participants (depending on model) in the 2012-2016 waves of the Health and Retirement Study, including the Biomarker Study, the Contextual Data Resource, and validated measures of childhood socioeconomic status. Predictive accuracy was assessed using k-Nearest Neighbors Discriminant Analysis.

RESULTS

Demographic, environmental, and health-related factors were associated with osteoporosis diagnosis, and predictive accuracy of the models was good. High-sensitivity C-reactive protein was not associated with osteoporosis diagnosis.

CONCLUSION

Social determinants identified indicate access to health care, inequalities in the greater social environment (e.g., access to resources), and overall health (i.e., underlying medical conditions) are key components for developing osteoporosis and indicate underlying health inequities in this sample. There is a need to further address the interplay between primary health care and social determinants of health.

Opportunistic CT screening of osteoporosis on thoracic and lumbar spine: a meta-analysis

BACKGROUND

Osteoporotic fractures are a major contributor to late life morbidity and mortality, and impose a substantial societal cost, yet osteoporosis remains substantially underdiagnosed and undertreated. The purpose of this meta-analysis was to assess the pooled diagnostic sensitivity and specificity of computed tomography (CT) images for diagnosing osteoporosis in patients who meet WHO dual X-ray absorptiometry (DXA) osteoporosis criteria using specific Hounsfield unit (HU) values as a threshold.

METHODS

Systematic literature searches in PubMed, Embase, Web of Science and Google Scholar were performed from the earliest available date through 1 July 2018, restricted to publications in English. Participants in all studies underwent CT scans that included the lumbar and/or thoracic spine for different indications and HU measurements were used to identify osteoporosis. DXA scans served as the reference standard.

RESULTS

Ten eligible studies were identified. The mean area under the hierarchical summary receiver operating characteristic (ROC) curve for diagnosis osteoporosis was 0.84 (95% CI: 0.81, 0.87). The pooled diagnostic sensitivity and specificity of CT images to identify osteoporosis were 0.83 (95% CI: 0.73, 0.90) and 0.74 (95% CI: 0.69, 0.79). The positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio were 3.4 (95% CI: 2.7, 4.5), 0.21 (95% CI: 0.12, 0.36), and 16.4 (95% CI: 7.8, 34.3), respectively. The bias-adjusted sensitivity and specificity of CT were 0.73 and 0.71. Meta-regression demonstrated that country of study, DXA criteria and scanner manufacturer were significant factors associated with the sensitivity of CT in detecting osteoporosis while scanner manufacturer was the only factor associated with specificity of CT.

CONCLUSIONS

This meta-analysis showed reasonable pooled sensitivity and specificity for using threshold values measured on CT scans to identify osteoporosis opportunistically.

Bone Mineral Density Testing in Patients Undergoing Total Ankle Arthroplasty: Should We Pay More Attention to the Bone Quality?

Total ankle arthroplasty (TAA) has become one of the standard treatments for end-stage ankle arthritis. Long-term TAA survivorship is reported from 63% to 95%, with aseptic loosening being the most common mode of failure. Several studies have shown that low bone mineral density (BMD) of the hip affects the longevity of prosthetic implants. The role of Dual energy X-ray absorptiometry for TAA has not been established. The purpose of this review was to define the role of BMD in TAA outcomes and the role of Dual energy X-ray absorptiometry in measuring periprosthetic BMD. There is a paucity of information and published literature regarding the relationship between BMD and TAA. From attempting this systematic review, we hope to highlight that much of the focus in total ankle arthroplasty has emphasized implants and relatively little has focused on the quality of bone into which the prostheses are implanted.

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study

Background

Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (APCT) is one of the most widely conducted medical tests. Artificial intelligence and radiomics analysis have recently been spotlighted. This is the first study to evaluate the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT.

Materials and methods

500 patients (M: F = 70:430; mean age, 66.5 ± 11.8yrs; range, 50–96 years) underwent both dual-energy X-ray absorptiometry and APCT within 1 month. The volume of interest of the left proximal femur was extracted and 41 radiomics features were calculated using 3D volume of interest analysis. Top 10 importance radiomic features were selected by the intraclass correlation coefficient and random forest feature selection. Study cohort was randomly divided into 70% of the samples as the training cohort and the remaining 30% of the sample as the validation cohort. Prediction performance of machine-learning analysis was calculated using diagnostic test and comparison of area under the curve (AUC) of receiver operating characteristic curve analysis was performed between training and validation cohorts.

Results

The osteoporosis prevalence of this study cohort was 20.8%. The prediction performance of the machine-learning analysis to diagnose osteoporosis in the training and validation cohorts were as follows; accuracy, 92.9% vs. 92.7%; sensitivity, 86.6% vs. 80.0%; specificity, 94.5% vs. 95.8%; positive predictive value, 78.4% vs. 82.8%; and negative predictive value, 96.7% vs. 95.0%. The AUC to predict osteoporosis in the training and validation cohorts were 95.9% [95% confidence interval (CI), 93.7%-98.1%] and 96.0% [95% CI, 93.2%-98.8%], respectively, without significant differences (P = 0.962).

Conclusion

Prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value.

Opportunistic screening for osteoporosis and osteopenia by routine computed tomography scan: A heterogeneous, multiethnic, middle-eastern population validation study

PURPOSE

We aimed define thresholds for HU values observed on opportunistic CT scans that suggest abnormal bone mineral density (BMD) in a heterogeneous Middle Eastern population.

METHODS

Consecutive patients who had undergone CT and dual-energy X-ray absorptiometry (DXA) test of the lumbar spine within 6 months were included in this retrospective study. Hounsfield units (HU) on lateral lumbar spine CT and BMD at the spine and hip on DXA were compared. Potential HU thresholds suggestive of abnormal BMD were established using receiver operating characteristic (ROC) analysis.

RESULTS

246 patients (mean age of 64 ± 11.6 years; 83 % female) were included. On DXA, 27 % had osteoporosis, 56 % had osteopenia, and 17 % had normal BMD. To distinguish osteoporosis from non-osteoporosis (osteopenia, normal BMD), a threshold of HU160 had sensitivity 95 % and the balanced threshold was HU121 (sensitivity 74 %, specificity 61 %). To distinguish normal from abnormal BMD (osteoporosis, osteopenia), a threshold of HU110 had specificity 93 % and the balanced threshold was HU149 (sensitivity 76 %, specificity 74 %).

CONCLUSIONS

In a heterogeneous Middle-Eastern population, our study supports the reported correlation between HU values on lumbar spine CT and BMD on DXA. In this population, HU > 160 correlates with low probability of osteoporosis on DXA, and screening examination is not warranted unless a vertebral fracture is detected; for HU ≤ 110 there is high probability of abnormal (osteoporosis or osteopenia) BMD, DXA examination is warranted; Finally, for HU 110-160, there is an intermediate chance of abnormal BMD, DXA examination may be warranted in specific patients with other risk factors.

Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study

Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years). DXA-derived bone mineral density (BMD) measures were used as the reference standard. We categorized patients into three groups according to DXA BMD T-score: normal (T ≥ -1.0), osteopenia (-2.5 < T < -1.0), and osteoporosis (T ≤ -2.5). T-scores were calculated by using the BMD dataset of young Chinese female aged 20-40 years as a reference. A 3-class DCNN model was trained to classify normal BMD, osteoporosis, and osteopenia. Model performance was tested in a validation dataset (204 images from 102 patients) and two test datasets (396 images from 198 patients and 348 images from 147 patients respectively). Model performance was assessed by the receiver operating characteristic (ROC) curve analysis. The results showed that in the test dataset 1, the model diagnosing osteoporosis achieved an AUC of 0.767 (95% confidence interval [CI]: 0.701-0.824) with sensitivity of 73.7% (95% CI: 62.3-83.1), the model diagnosing osteopenia achieved an AUC of 0.787 (95% CI: 0.723-0.842) with sensitivity of 81.8% (95% CI: 67.3-91.8); In the test dataset 2, the model diagnosing osteoporosis yielded an AUC of 0.726 (95% CI: 0.646-0.796) with sensitivity of 68.4% (95% CI: 54.8-80.1), the model diagnosing osteopenia yielded an AUC of 0.810 (95% CI, 0.737-0.870) with sensitivity of 85.3% (95% CI, 68.9-95.0). Accordingly, a deep learning diagnostic network may have the potential in screening osteoporosis and osteopenia based on lumbar spine radiographs. However, further studies are necessary to verify and improve the diagnostic performance of DCNN models.

Osteoporotic fracture trends in a population of US managed care enrollees from 2007 to 2017

This study expands on previous findings that hip fracture rates may no longer be declining. We found that age- and sex-adjusted fracture rates in the US plateaued or increased through mid-2017 in a population of commercially insured and Medicare Advantage health plan enrollees, in contrast to a decline from 2007 to 2013.

INTRODUCTION

The purpose of this study was to evaluate fracture trends in US commercial and Medicare Advantage health plan members aged ≥ 50 years between 2007 and 2017.

METHODS

Retrospective analysis of the Optum Research Database from January 1, 2007, to May 31, 2017.

RESULTS

Of 1,841,263 patients identified with an index fracture, 930,690 were case-qualifying and included in this analysis. The overall age- and sex-adjusted fracture rate decreased from 14.67/1000 person-years (py) in 2007 to 11.79/1000 py in 2013, followed by a plateau for the next 3 years and then an increase to 12.50/1000 py in mid-2017. In females aged ≥ 65 years, fracture rates declined from 27.49/1000 py in 2007 to 22.08/1000 py in 2013, then increased to 24.92/1000 py in mid-2017. Likewise, fracture rates in males aged ≥ 65 years declined from 2007 (12.00/1000 py) to 2013 (10.72/1000 py), then increased to 12.04/1000 py in mid-2017. The age- and sex-adjusted fracture rates for most fracture sites declined from 2007 to 2013 by 3.7% per year (P = 0.310).

CONCLUSIONS

Following a consistent decline in fracture rate from 2007 to 2013, trends from 2014 to 2017 indicate fracture rates are no longer declining and, for some fracture types, rates are rising.

Biomechanical Computed Tomography analysis (BCT) for clinical assessment of osteoporosis

The surgeon general of the USA defines osteoporosis as "a skeletal disorder characterized by compromised bone strength, predisposing to an increased risk of fracture." Measuring bone strength, Biomechanical Computed Tomography analysis (BCT), namely, finite element analysis of a patient's clinical-resolution computed tomography (CT) scan, is now available in the USA as a Medicare screening benefit for osteoporosis diagnostic testing. Helping to address under-diagnosis of osteoporosis, BCT can be applied "opportunistically" to most existing CT scans that include the spine or hip regions and were previously obtained for an unrelated medical indication. For the BCT test, no modifications are required to standard clinical CT imaging protocols. The analysis provides measurements of bone strength as well as a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) T-score at the hip and a volumetric BMD of trabecular bone at the spine. Based on both the bone strength and BMD measurements, a physician can identify osteoporosis and assess fracture risk (high, increased, not increased), without needing confirmation by DXA. To help introduce BCT to clinicians and health care professionals, we describe in this review the currently available clinical implementation of the test (VirtuOst), its application for managing patients, and the underlying supporting evidence; we also discuss its main limitations and how its results can be interpreted clinically. Together, this body of evidence supports BCT as an accurate and convenient diagnostic test for osteoporosis in both sexes, particularly when used opportunistically for patients already with CT. Biomechanical Computed Tomography analysis (BCT) uses a patient's CT scan to measure both bone strength and bone mineral density at the hip or spine. Performing at least as well as DXA for both diagnosing osteoporosis and assessing fracture risk, BCT is particularly well-suited to "opportunistic" use for the patient without a recent DXA who is undergoing or has previously undergone CT testing (including hip or spine regions) for an unrelated medical condition.

Hip Fracture Trends in America Between 2009 and 2016

Background

Hip fractures are a common condition associated with high morbidity and mortality. In this study, we assess (1) yearly incidences, (2) demographic factors, (3) postoperative outcomes, (4) primary diagnoses, and (5) primary procedures.

Materials and Methods

The National Inpatient Sample was queried for patients admitted with hip fractures from 2009 to 2016 (n = 2 761 850). Variables analyzed were age, sex, race, obesity status, Charlson Comorbidity Index, smoking status, osteoporosis status, lengths of stay (LOS), discharge dispositions, charges, costs, mortalities, inpatient complications, primary and secondary diagnoses, and primary procedures.

Results

From 2009 to 2016, the overall gross number of hip fractures decreased (P < .001). At the conclusion of the study, more patients were male, obese, and smokers, while fewer had a diagnosis of osteoporosis (P < .001 for all). Mean LOS significantly decreased (P < .001), while charges and costs increased (P < .001 for both). Both mortality and the overall complication rate decreased (P < .001 for both). Specifically, complications that decreased included myocardial infarctions, deep vein thromboses, pulmonary emboli, pneumoniae, hematomas/seromas, urinary tract infections, and transfusions (P < .001 for all). Complications that increased included cardiac arrests, respiratory failures, mechanical complications, and sepsis (P < .001 for all). The most common diagnosis was "closed fracture of intertrochanteric section of neck of femur." The procedure performed most often was "open reduction of fracture with internal fixation, femur."

Conclusion

An increasing number of males and smokers have sustained hip fractures, although fewer patients with osteoporosis experienced these injuries. A decreasing overall complication rate may indicate improving perioperative courses for hip fracture patients. However, several shortcomings still exist and can be improved to further decrease negative outcomes.

Practical management of fracture risk among peri- and postmenopausal women

Fractures and their consequences are the clinically important manifestation of osteoporosis; preventing fractures is the primary goal of management. Effective management is achievable given present knowledge and tools but is seldom prescribed. This review will cover the individual and social burden of fracture, essential information about fracture risk and its estimation, an approach to patient care emphasizing specific information to elicit and therapeutic strategies to pursue, and existing gaps in knowledge and important questions for future research.

Repeating Measurement of Bone Mineral Density when Monitoring with Dual-energy X-ray Absorptiometry: 2019 ISCD Official Position

Bone mineral density (BMD) can be measured at multiple skeletal sites using various technologies to aid clinical decision-making in bone and mineral disorders. BMD by dual-energy X-ray absorptiometry (DXA) has a critical role in predicting risk of fracture, diagnosis of osteoporosis, and monitoring patients. In clinical practice, DXA remains the most available and best validated tool for monitoring patients. A quality baseline DXA scan is essential for comparison with all subsequent scans. Monitoring patients with serial measurements requires technical expertise and knowledge of the least significant change in order to determine when follow-up scans should be repeated. Prior ISCD Official Positions have clarified how and when repeat DXA is useful as well as the interpretation of results. The 2019 ISCD Official Positions considered new evidence and clarifies if and when BMD should be repeated. There is good evidence showing that repeat BMD measurement can identify people who experience bone loss, which is an independent predictor of fracture risk. There is good evidence showing that the reduction in spine and hip fractures with osteoporosis medication is proportional to the change in BMD with treatment. There is evidence that measuring BMD is useful following discontinuation of osteoporosis treatment. There is less documentation addressing the effectiveness of monitoring BMD to improve medication adherence, whether monitoring of BMD reduces the risk of fracture, or effectively discriminates patients who should and should not recommence treatment following an interruption of medication. Further research is needed in all of these areas.

Cost-Effectiveness of Osteoporosis Screening Using Biomechanical Computed Tomography for Patients With a Previous Abdominal CT

Osteoporosis screening rates by DXA are low (9.5% women, 1.7% men) in the US Medicare population aged 65 years and older. Addressing this care gap, we estimated the benefits of a validated osteoporosis diagnostic test suitable for patients age 65 years and older with an abdominal computed tomography (CT) scan taken for any indication but without a recent DXA. Our analysis assessed a hypothetical cohort of 1000 such patients in a given year, and followed them for 5 years. Separately for each sex, we used Markov modeling to compare two mutually exclusive scenarios: (i) utilizing the CT scans, perform one-time "biomechanical computed tomography" (BCT) analysis to identify high-risk patients on the basis of both femoral strength and hip BMD T-scores; (ii) ignore the CT scan, and rely instead on usual care, consisting of future annual DXA screening at typical Medicare rates. For patients with findings indicative of osteoporosis, 50% underwent 2 years of treatment with alendronate. We found that BCT provided greater clinical benefit at lower cost for both sexes than usual care. In our base case, compared to usual care, BCT prevented hip fractures over a 5-year window (3.1 per 1000 women; 1.9 per 1000 men) and increased quality-adjusted life years (2.95 per 1000 women; 1.48 per 1000 men). Efficacy and savings increased further for higher-risk patient pools, greater treatment adherence, and longer treatment duration. When the sensitivity and specificity of BCT were set to those for DXA, the prevented hip fractures versus usual care remained high (2.7 per 1000 women; 1.5 per 1000 men), indicating the importance of high screening rates on clinical efficacy. Therefore, for patients with a previously taken abdominal CT and without a recent DXA, osteoporosis screening using biomechanical computed tomography may be a cost-effective alternative to current usual care. © 2019 American Society for Bone and Mineral Research.

Factors Associated With Osteoporosis Care of Men Hospitalized for Hip Fracture: A Retrospective Cohort Study

Rates of postfracture DXA and pharmacotherapy appear to be declining despite their known benefits in fracture reduction. We sought to identify factors associated with osteoporosis care among male veterans aged 50 years and older after hip fracture and to evaluate trends in rates of care with an observational cohort design using US Department of Veterans Affairs’ (VA) inpatient, pharmacy, and outpatient encounters and Centers for Medicare and Medicaid Services outpatient pharmacy claims (2007 to 2014) from men aged 50 years and older treated for hip fracture (N = 7317). We used the Cox proportional hazards model with random effects for the admitting facility. A sensitivity analysis was performed for a subset of patients aged 65 to 99 dually enrolled in Medicare ( N = 5821). Overall, approximately 13% of patients had evidence of osteoporosis care within one year of fracture. In the adjusted model, rural residence was associated with lower likelihood of care, and several comorbidities were associated with higher likelihood of receiving care. In sensitivity analyses of patients dually enrolled in Medicare, rural residence remained associated with lower likelihood of osteoporosis care. Overall rates of care decreased over time, but rates of DXA in the VA remained stable. These findings highlight the ongoing problem of low rates of postfracture care among a population with the highest risk of future fracture and its associated morbidity and mortality. The rural disparity in care and differences in rates of care across healthcare delivery systems illustrates the importance of healthcare delivery systems in promoting pharmacotherapy and DXA after sentinel events. Because the VA removes a majority of cost barriers to care, this integrated healthcare system may outperform the private sector in access to care. However, declining rates of pharmacotherapy imply knowledge gaps that undermine quality care. © 2019 The Authors. JBMR Plus is published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

The trend of osteoporosis and osteoporotic fragility fractures in inpatients: results from a national database

Osteoporosis is associated with an increased risk of pathologic fractures; however, most patients do not receive diagnosis and adequate treatment. The aim of our study was to compare the yearly trends of osteoporosis and osteoporotic fragility fractures in the USA (US) inpatients. We used National (Nationwide) Inpatient Sample database to identify adults ≥18 years with diagnoses of osteoporosis and pathologic fractures and excluded pathologic fractures due to other etiologies. We then studied the annual trends, in terms of annual percentage change (APC), of osteoporosis and osteoporotic fractures. Among overall hospitalizations, osteoporosis was noted to have an increasing trend from 2000 to 2009 (APC = 5.81, p < 0.05) with a decline thereafter (APC = - 3.88, p < 0.05). In contrast, osteoporotic fracture showed an initial downward trend from 2000 to 2010 (APC = -7.31, p < 0.05), followed by a slowly rising trend (APC = 2.0, p = NS). The initially increasing trend of osteoporosis was followed by a decreasing trend thereafter. In contrast, there was a halt in a previously declining trend of osteoporotic fracture. Potential explanations include inadequate screening and treatment per guidelines along with decreasing patient compliance. In conclusion, primary and secondary prevention measures for osteoporosis have been underutilized by both physicians and patients alike.

Healthcare Policy Changes in Osteoporosis Can Improve Outcomes and Reduce Costs in the United States

In the United States, osteoporosis affects over 10 million adults, has high societal costs ($22 billion in 2008), and is currently being underdiagnosed and undertreated. Given an aging population, this burden is expected to rise. We projected the fracture burden in US women by modeling the expected demographic shift as well as potential policy changes. With the anticipated population aging and growth, annual fractures are projected to increase from 1.9 million to 3.2 million (68%), from 2018 to 2040, with related costs rising from $57 billion to over $95 billion. Policy‐driven expansion of case finding and treatment of at‐risk women could lower this burden, preventing 6.1 million fractures over the next 22 years while reducing payer costs by $29 billion and societal costs by $55 billion. Increasing use of osteoporosis‐related interventions can reduce fractures and result in substantial cost‐savings, a rare and fortunate combination given the current landscape in healthcare policy. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Sex differences in osteoporosis self-efficacy among community-residing older adults presenting for DXA

The Osteoporosis Self Efficacy Scale was determined to equivalently measure calcium and exercise beliefs in both sexes. Despite data illustrating men's and women's similar self-efficacy, gender differences in clinical predictors of self-efficacy imply that efforts to improve care must account for more than self-efficacy.

INTRODUCTION

To understand the extent to which the Osteoporosis Self Efficacy (OSE) Scale is reliable for both men and women. A secondary objective was to evaluate sex differences in OSE.

METHODS

For this cross-sectional study, we analyzed data collected as part of the Patient Activation after DXA Result Notification (PAADRN) pragmatic trial which enrolled 7749 community-residing adults aged 50 and older reporting for bone densitometry. We used univariable methods, item analysis, exploratory and confirmatory factor analyses, and linear regression to evaluate sex differences in OSE responses and measurement.

RESULTS

In this sample, the confirmatory factor analysis model for OSE both overall and within groups indicated a poor fit. The sex differences in the measurement model, however, were minor and reflected configural invariance (i.e., constructs were measuring the same things in both men and women), confirming that the OSE was measuring the same constructs in men and women. Men overall had higher exercise self-efficacy and women higher calcium self-efficacy. Overall, education, hip fracture, and self-reported health status predicted exercise self-efficacy whereas prior DXA, self-reported osteoporosis, and history of pharmacotherapy use did not. Predictors of calcium self-efficacy differed by gender.

CONCLUSION

The OSE can be used to measure calcium and exercise self-efficacy in all older adults. However, gender differences in clinical predictors of self-efficacy and the lack of an association of prior DXA with self-efficacy imply that interventions to improve self-efficacy may be insufficient to drive significant improvement in rates of osteoporosis evaluation and treatment.

TRIAL REGISTRATION

Patient Activation after DXA Result Notification (PAADRN), NCT01507662, https://clinicaltrials.gov/ct2/show/NCT01507662.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade

PURPOSE

Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm.

METHODS

Between January 2010 and September 2014, 610 CT studies of the abdomen and pelvis were used to develop spinal column and L1 to L4 multiclass segmentation. DEXA simulation training and validation used 1,843 pairs of CT studies accompanied by DEXA results obtained within a 6-month interval from the same individual. Machine learning-based regression was used to determine correlation between calculated grade (on the basis of vertebrae L1-L4) and DEXA t score.

RESULTS

Analysis of the t score equivalent, generated by the algorithm, revealed true positives in 1,144 patients, false positives in 92 patients, true negatives in 245 patients, and false negatives in 212 patients, resulting in an accuracy of 82%. Sensitivity for the detection of osteoporosis or osteopenia was 84.4% (95% confidence interval, 82.3%-86.2%), and specificity was 72.7% (95% confidence interval, 67.7%-77.2%).

CONCLUSIONS

The presented algorithm can identify osteoporosis and osteopenia with a high degree of accuracy (82%) and a small proportion of false positives. Efforts to cull greater information using machine-learning algorithms from pre-existing data have the potential to have a marked impact on population health efforts such as bone mineral density screening for osteoporosis, in which gaps in screening currently exist.

Quantitative diagnosis of osteoporosis using lumbar spine signal intensity in magnetic resonance imaging

OBJECTIVE

Osteoporosis is the most common metabolic bone disease that is not recognized in many elderly people. To determine the cause of low back pain, lumbosacral MRI is done for a large population who may not have gone under dual energy X-ray absorptiometry (DXA). The aim of this study was to predict bone density using lumbar spine signals in lumbosacral MRI in high risk patients for osteoporosis including post-menopausal females and calculate a threshold for a new quantitative MRI-based score to be used in estimation of lumbar spine bone mass density.

METHODS

82 menopaused females, who had undergone DXA before, were selected and MRI was done within 6 months after DXA. 69 healthy females aged 20-29 years who had undergone lumbar MRI were selected as reference group. Results were analyzed and threshold and diagnostic performance of MRI-based score (M-score) on the method of T-score was calculated.

RESULTS

Negative correlation between M-score and T-score was detected. Cut off point of 2.05 was found for M-score with near sensitivity of 90% and specificity of 87% for detecting osteoporotic patients from non-osteoporotic individuals.

CONCLUSION

M-score is a MRI-based method which can identify patients at risk of osteoporosis. Early diagnosis of osteoporosis can reduce morbidity and mortality caused by it.

ADVANCES IN KNOWLEDGE

The research introduced cut of points for M-score as a new MRI quantitative method to be used as an opportunistic technique for detecting osteoporotic patients.

Lower Bone Density on Preoperative Computed Tomography Predicts Periprosthetic Fracture Risk in Total Ankle Arthroplasty

BACKGROUND:

The effect of bone mineral density (BMD) on outcomes from total ankle arthroplasty (TAA) has not been studied. BMD can be estimated by measuring Hounsfield units (HU) on standard computed tomography (CT), which is frequently performed prior to TAA. We aimed to identify whether tibial and talar HU measured from preoperative CT scans were associated with periprosthetic fracture or revision risk in patients undergoing TAA.

METHODS:

A prospectively collected database was used to retrospectively screen all patients undergoing primary TAA. Only patients with a preoperative CT within 1 year of surgery were included. Primary outcomes were periprosthetic fracture and prosthetic revision. HU were measured on axial CT cuts in the distal tibia and talus. Additional patient factors analyzed included age, sex, weight, body mass index (BMI), tobacco use, presence of rheumatoid arthritis, and preoperative deformity. A total of 198 ankles were included, with a mean 2.4 years of follow-up.

RESULTS:

There were 7 intraoperative and 9 postoperative periprosthetic fractures (3.5% and 4.5%, respectively). Seven patients (3.5%) underwent prosthetic removal or revision. Lower tibial and talar HU, lower weight, and lower BMI were associated with periprosthetic fractures ( P < .05). After controlling for age, sex, and weight, only tibial HU was significantly associated with periprosthetic fracture ( P = .018). All intraoperative fractures occurred in patients with tibial HU less than 200. None of the patient factors analyzed were associated with revision.

CONCLUSIONS:

Lower tibial HU on preoperative CT was strongly associated with periprosthetic fracture risk with TAA. In patients with tibial HU less than 200, surgeons may consider prophylactic internal fixation of the medial malleolus.

LEVEL OF EVIDENCE:

Level III, retrospective cohort study.

Osteoporosis and Hip Fracture Risk From Routine Computed Tomography Scans: The Fracture, Osteoporosis, and CT Utilization Study (FOCUS)

Methods now exist for analyzing previously taken clinical computed tomography (CT) scans to measure a dual-energy X-ray absorptiometry (DXA)-equivalent bone mineral density (BMD) at the hip and a finite element analysis-derived femoral strength. We assessed the efficacy of this "biomechanical CT" (BCT) approach for identifying patients at high risk of incident hip fracture in a large clinical setting. Using a case-cohort design sampled from 111,694 women and men aged 65 or older who had a prior hip CT scan, a DXA within 3 years of the CT, and no prior hip fracture, we compared those with subsequent hip fracture (n = 1959) with randomly selected sex-stratified controls (n = 1979) and analyzed their CT scans blinded to all other data. We found that the age-, race-, and body mass index (BMI)-adjusted hazard ratio (HR; per standard deviation) for femoral strength was significant before (women: HR = 2.8, 95% confidence interval [CI] 2.2-3.5; men: 2.8, 2.1-3.7) and after adjusting also for the (lowest) hip BMD T-score by BCT (women: 2.1, 1.4-3.2; men: 2.7, 1.6-4.6). The hazard ratio for the hip BMD T-score was similar between BCT and DXA for both sexes (women: 2.1, 1.8-2.5 BCT versus 2.1, 1.7-2.5 DXA; men: 2.8, 2.1-3.8 BCT versus 2.5, 2.0-3.2 DXA) and was higher than for the (lowest) spine/hip BMD T-score by DXA (women: 1.6, 1.4-1.9; men: 2.1, 1.6-2.7). Compared with the latter as a clinical-practice reference and using both femoral strength and the hip BMD T-score from BCT, sensitivity for predicting hip fracture was higher for BCT (women: 0.66 versus 0.59; men: 0.56 versus 0.48), with comparable respective specificity (women: 0.66 versus 0.67; men: 0.76 versus 0.78). We conclude that BCT analysis of previously acquired routine abdominal or pelvic CT scans is at least as effective as DXA testing for identifying patients at high risk of hip fracture. © 2018 American Society for Bone and Mineral Research.

Vertebral Imaging in the Diagnosis of Osteoporosis: a Clinician's Perspective

PURPOSE OF REVIEW

Vertebral fractures are the most common osteoporotic fracture and result in functional decline and excess mortality. Dual-energy x-ray absorptiometry (DXA) is the gold standard for the diagnosis of osteoporosis to identify patients at risk for fragility fractures; however, advances in imaging have expanded the role of computed tomography (CT) and magnetic resonance imaging (MRI) in evaluating bone health.

RECENT FINDINGS

The utility of CT and MRI in the assessment of bone density is starting to gain traction, particularly when used opportunistically. DXA, conventional radiography, CT, and MRI can all be used to assess for vertebral fractures, and MRI can determine the acuity of fractures. Finally, advances in imaging allow for non-invasive assessment of measures of bone quality, including microarchitecture, bone strength, and bone turnover, to help identify and treat at-risk patients prior to sustaining a vertebral fracture. CT and MRI techniques remain primarily research tools to assess metabolic bone dysfunction, while use of DXA can be clinically expanded beyond measurement of bone density to assess for vertebral fractures and bone architecture to improve fracture risk assessment and guide treatment.

Opportunistic Use of CT Imaging for Osteoporosis Screening and Bone Density Assessment: A Qualitative Systematic Review

BACKGROUND

The purpose of this study was to determine the clinical opportunities for the use of computed tomography (CT) imaging for inferring bone quality and to critically analyze the correlation between dual x-ray absorptiometry (DXA) and diagnostic CT as reported in the literature.

METHODS

A systematic review of the MEDLINE database was performed in February 2016 using the PubMed interface. The inclusion criteria were English language, studies performed using living human subjects, studies pertaining to orthopaedics, use of conventional diagnostic CT scans, studies that measured cancellous bone, and studies that reported Hounsfield unit (HU) measurements directly rather than a computed bone mineral density.

RESULTS

Thirty-seven studies that reported on a total of 9,109 patients were included. Of these, 10 studies correlated HU measurements of trabecular bone with DXA-based bone assessment. Reported correlation coefficients ranged between 0.399 and 0.891, and 5 of the studies reported appropriate threshold HU levels for diagnosing osteoporosis or osteopenia.

CONCLUSIONS

Direct HU measurement from diagnostic CT scans has the potential to be used opportunistically for osteoporosis screening, but in its current state it is not ready for clinical implementation. There is a lack of exchangeability among different machines that limits its broad applicability. Future research efforts should focus on identifying thresholds at specific anatomic regions in high-risk patients in order to have the greatest impact on patients. However, using diagnostic CT to infer region-specific osteoporosis could be extraordinarily valuable to orthopaedic surgeons and primary care physicians, and merits further research.

The Bone Health Team: A Team-Based Approach to Improving Osteoporosis Care for Primary Care Patients

BACKGROUND

Significant improvements in secondary prevention of osteoporotic fractures have been noted with fracture liaison services. However, similar models for the primary prevention of such fractures have not been reported.

OBJECTIVE

To determine the impact of a Bone Health Team (BHT) on osteoporosis screening and treatment rates in U.S. veterans in primary care practices.

DESIGN

Historical cohort study of a primary care-based intervention of a BHT from February 2013 to February 2015.

SETTING

Community-based outpatient clinics of the Salt Lake City Veterans Affairs Health Care System.

PARTICIPANTS

Men aged 70 years and older and women aged 65 years and older.

INTERVENTION

Enrollment in the BHT.

MEASUREMENTS

Rates of dual energy x-ray absorptiometry (DXA) completion, chart diagnosis of osteoporosis or osteopenia, completion of vitamin D measurement, and initiation of fracture reducing medication.

RESULTS

Our cohort consisted of 7644 individuals, 975 of whom were exposed to the BHT and 6669 of whom were not. Comparison of patients exposed to the BHT versus non-exposed subjects demonstrated a substantial increase in all outcome measures studied. Hazard ratios (HRs) from multivariable cox proportional hazard models were: measurement of vitamin D, HR = 1.619 ( P < .001); chart diagnosis of osteopenia, HR = 37.00 ( P < .001); chart diagnosis of osteoporosis, HR = 16.38 ( P < .001); osteoporosis medication, HR = 17.03 ( P < .001); and completion of DXA, HR = 139.9 ( P < .001).

CONCLUSIONS AND RELEVANCE

The implementation of a dedicated BHT produced significantly increased rates of intermediate osteoporosis outcome measures in US veterans in primary care practices. Additional research describing medication adherence rates and cost-effectiveness is forthcoming.

Predicting Future Hip Fractures on Routine Abdominal CT Using Opportunistic Osteoporosis Screening Measures: A Matched Case-Control Study

OBJECTIVE

Hip fracture is a major consequence of low bone mineral density, which is treatable but underdiagnosed. The purpose of this case-control study is to determine whether lumbar vertebral trabecular attenuation, vertebral compression fractures, and femoral neck T scores readily derived from abdominopelvic CT scans obtained for various indications are associated with future hip fragility fracture.

MATERIALS AND METHODS

A cohort of 204 patients with hip fracture (130 women and 74 men; mean age, 74.3 years) who had undergone abdominopelvic CT before fracture occurred (mean interval, 24.8 months) was compared with an age- and sex-matched control cohort without hip fracture. L1 trabecular attenuation, vertebral compression fractures of grades 2 and 3, and femoral neck T scores derived from asynchronous quantitative CT were recorded. The presence of one or more clinical risk factor for fracture was also recorded. Multivariate logistic regression models were used to determine the association of each measurement with the occurrence of hip fracture.

RESULTS

The mean L1 trabecular attenuation value, the presence of one or more vertebral compression fracture, and CT-derived femoral neck T scores were all significantly different in patients with hip fracture versus control subjects (p < 0.01). Logistic regression models showed a significant association of all measurements with hip fracture outcome after adjustments were made for age, sex, and the presence of one or more clinical risk factor. L1 trabecular attenuation and CT-derived femoral neck T scores showed moderate accuracy in differentiating case and control patients (AUC, 0.70 and 0.78, respectively).

CONCLUSION

L1 trabecular attenuation, CT-derived femoral neck T scores, and the presence of at least one vertebral compression fracture on CT are all associated with future hip fragility fracture in adults undergoing routine abdominopelvic CT for a variety of conditions.

Acceptability and Feasibility of an Evidence-Based Requisition for Bone Mineral Density Testing in Clinical Practice

Introduction. The purpose of this study is to understand the experience of primary care providers (PCPs) using an evidence-based requisition for bone mineral density (BMD) testing. Methods. A qualitative descriptive approach was adopted. Participants were given 3 BMD Recommended Use Requisitions (RUR) to use over a 2-month period. Twenty-six PCPs were interviewed before using the RUR. Those who had received at least one BMD report resulting from RUR use were then interviewed again. An inductive thematic analysis was performed. Results. We identified four themes in interview data: (1) positive and negative characteristics of the RUR, (2) facilitators and barriers for implementation, (3) impact of the RUR, and (4) requisition preference. Positive characteristics of the RUR related to both its content and format. Negative characteristics related to the increased amount of time needed to complete the form. Facilitators to implementation included electronic availability and organizational endorsement. Time constraints were identified as a barrier to implementation. Participants perceived that the RUR would promote appropriate referrals and the majority of participants preferred the RUR to their current requisition. Conclusions. Findings from this study provide support for the RUR as an acceptable point-of-care tool for PCPs to promote appropriate BMD testing.

Predictors of bone mineral density testing among older women on Medicare

Although dual-energy X-ray absorptiometry (DXA) is recommended for all women ≥65 and is covered by Medicare, 40 % of women on Medicare report never having had a DXA. In a longitudinal cohort of 3492 women followed for two decades, we identified several risk factors that should be targeted to improve DXA testing rates.

INTRODUCTION

DXA is used to measure bone mineral density, screen for osteoporosis, and assess fracture risk. DXA is recommended for all women ≥65 years old. Although Medicare covers DXA every 24 months for women, about 40 % report never having had a DXA test, and little is known from prospective cohort studies about which subgroups of women have low use rates and should be targeted for interventions. Our objective was to identify predictors of DXA use in a nationally representative cohort of women on Medicare.

METHODS

We used baseline and biennial follow-up survey data (1993-2012) for 3492 women ≥70 years old from the nationally representative closed cohort known as the Survey on Assets and Health Dynamics among the Oldest Old (AHEAD). The survey data for these women were then linked to their Medicare claims (1991-2012), yielding 17,345 person years of observation. DXA tests were identified from the Medicare claims, and Cox proportional hazard regression models were used with both fixed and time-dependent predictors from the survey interviews including demographic characteristics, socioeconomic factors, health status, health habits, and the living environment.

RESULTS

DXA use was positively associated with being Hispanic American, better cognition, higher income, having arthritis, using other preventative services, and living in Florida or other southern states. DXA use was negatively associated with age, being African-American, being overweight or obese, having mobility limitations, and smoking.

CONCLUSIONS

Interventions to increase DXA use should target the characteristics that were observed here to be negatively associated with such screening.

Opportunistic Quantitative CT Bone Mineral Density Measurement at the Proximal Femur Using Routine Contrast-Enhanced Scans: Direct Comparison With DXA in 355 Adults

For patients undergoing routine contrast-enhanced CT examinations, an opportunity exists for concurrent osteoporosis screening without additional radiation exposure or patient time using proximal femur CT X-ray absorptiometry (CTXA). We investigated the effect of i.v. contrast enhancement on femoral neck CTXA T-score measurement compared with DXA. This cohort included 355 adults (277 female; mean age, 59.7 ± 13.3 years; range, 21 to 90 years) who underwent standard contrast-enhanced CT assessment at 120 kV_p over an 8-year interval, as well as DXA BMD assessment within 100 days of the CT study (mean 46 ± 30 days). Linear regression and a Bland-Altman plot were performed to compare DXA and CTXA results. CTXA diagnostic sensitivity and specificity was evaluated with DXA as the reference standard. There was good correlation between DXA and CTXA (r²  = 0.824 for both areal BMD and T-scores) and the SD of the distribution of residuals was 0.063 g/cm² or 0.45 T-score units. There was no trend in differences between the two measurements and a small bias was noted with DXA T-score +0.18 units higher than CTXA. CTXA had a sensitivity for discriminating normal from low bone mineral density of 94.9% (95% CI, 90.6% to 97.4%). For opportunistic osteoporosis screening at routine post-contrast abdominopelvic CT scans, CTXA produces T-scores similar to DXA. Because femoral neck CTXA BMD measurement is now included in the WHO Fracture Risk Assessment Tool (FRAX) tool, this opportunistic method could help to increase osteoporosis screening because it can be applied regardless of the clinical indication for CT scanning. © 2016 American Society for Bone and Mineral Research.