When services can be administered in various settings, the Company reserves the right to reimburse only those services that are furnished in the most appropriate and cost-effective setting that is appropriate to the member’s medical needs and condition. This decision is based on the member’s current medical condition and any required monitoring or additional services that may coincide with the delivery of this service.
This Medical Policy Bulletin document describes the status of medical technology at the time the document was developed. Since that time, new technology may have emerged or new medical literature may have been published. This Medical Policy Bulletin will be reviewed regularly and be updated as scientific and medical literature becomes available. For more information on how Medical Policy Bulletins are developed, go to the About This Site section of this Medical Policy Web site.
PRACTICE GUIDELINES AND POSITION STATEMENTS
AMERICAN COLLEGE OF OBSTETRICIANS AND GYNECOLOGISTS
The ACOG (2012, reaffirmed 2016) updated its guidelines on managing osteoporosis in women. The guidelines recommended that BMD screening should begin for all women at age 65 years. In addition, the ACOG recommended screening for women younger than 65 years in whom the Fracture Risk Assessment Tool indicates a 10-year risk of osteoporotic fracture of at least 9.3. Alternatively, ACOG recommended BMD screening women younger than 65 or with any of the following risk factors (they are similar, but not identical to risk factors in the Fracture Risk Assessment Tool):
NATIONAL OSTEOPOROSIS FOUNDATION
The NOF (2014) updated its practice guidelines. The NOF guidelines recommended that all postmenopausal women and men ages 50 and older be evaluated clinically for osteoporosis risk to determine the need for BMD testing.
Indications for BMD testing included:
The NOF also indicated that:
“Central DXA [dual x-ray absorptiometry] assessment of the hip or lumbar spine is the ‘gold standard’ for serial assessment of BMD. Biological changes in bone density are small compared to the inherent error in the test itself, and interpretation of serial bone density studies depends on appreciation of the smallest change in BMD that is beyond the range of error of the test. This least significant change (LSC) varies with the specific instrument used, patient population being assessed, measurement site, technologist’s skill with patient positioning and test analysis, and the confidence intervals used. Changes in the BMD of less than 3-6 % at the hip and 2-4 % at the spine from test to test may be due to the precision error of the testing itself.”
AMERICAN COLLEGE OF PHYSICIANS
The guidelines from the American College of Physicians (2017) on the treatment of osteoporosis recommended against bone density monitoring during the 5-year pharmacologic treatment period of osteoporosis in women (weak recommendation, low-quality evidence). The American College of Physicians noted that data from several studies showed a reduction in fractures with pharmacologic treatment, even when BMD did not increase. In addition, current evidence “does not support frequent monitoring of women with normal bone density for osteoporosis, because data showed that most women with normal CSA scores did not progress to osteoporosis with 15 years.”
AMERICAN COLLEGE OF RADIOLOGY
Appropriateness criteria from the American College of Radiology, updated in 2017, state that BMD measurement is indicated whenever a clinical decision is likely to be directly influenced by the result of the test. Indications for DXA of the lumbar spine and hip included but were not limited to the following patient populations:
1. All women age 65 years and older and men age 70 years and older (asymptomatic screening)
2. Women younger than age 65 years who have additional risk for osteoporosis, based on medical history and other findings. Additional risk factors for osteoporosis include:
b. A history of maternal hip fracture that occurred after the age of 50 years
c. Low body mass (less than 127 lb or 57.6 kg)
d. History of amenorrhea (more than 1 year before age 42 years)
b. Loss of height, thoracic kyphosis
5. Individuals age 50 years and older who develop a wrist, hip, spine, or proximal humerus fracture with minimal or no trauma, excluding pathologic fractures
6. Individuals of any age who develop one or more insufficiency fractures
7. Individuals being considered for pharmacologic therapy for osteoporosis.
8. Individuals being monitored to:
b. Follow-up medical conditions associated with abnormal BMD.
INTERNATIONAL SOCIETY FOR CLINICAL DENSITOMETRY
The 2013 update of theInternational Society for Clinical Densitometry guidelines recommended bone density testing in the following patients:
AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY
The American Association of Clinical Endocrinologists and American College of Endocrinology (2016) issued updated joint guidelines on the diagnosis and treatment of postmenopausal osteoporosis. The guidelines listed the potential uses for BMD measurements in postmenopausal women as:
US PREVENTIVE SERVICES TASK FORCE RECOMMENDATIONS
The USPSTF (2018) updated its recommendations on screening for osteoporosis with bone density measurements. The USPSTF recommended screening for osteoporosis in women aged 65 years or older and in postmenopausal women younger than 65 years at increased risk of osteoporosis. The supporting document notes there are multiple instruments to predict risk for low BMD, including the Fracture Risk Assessment Tool. The updated USPSTF recommendations stated that the scientific evidence is “insufficient” to assess the balance of benefits and harms of screening for osteoporosis screening in men. The Task Force did not recommend specific screening tests but said the most commonly used tests are DXA of the hip and lumbar spine and quantitative ultrasound of the calcaneus.
The USPSTF concluded the evidence base is sparse on screening interval. While two studies showed no advantage to repeated testing, other evidence suggested that the optimal screening interval may vary by baseline BMD, age, and use of hormone replacement therapy.
CLINICAL PRACTICE GUIDELINES FOR THE CARE OF GIRLS AND WOMEN WITH TURNER SYNDROME
The Clinical Practice Guidelines for the Care of Girls and Women with Turner Syndrome (Gravholt, et al., 2017) recommends a DEXA scan every 5 years due to the increased risk of osteoporosis in these patients.
FINITE ELEMENT ANALYSIS
Quantitative computed tomography-based finite element analysis (QCT/FEA) estimates fracture strength using 3D bone mineral distribution and geometry. The finite element analysis (FEA) is a computer simulation method, which is being investigated as a tool for bone fragility prediction. FEA considers both, the individual geometric and densitometric parameters retrieved from computed tomography imaging. In homogenized continuum level voxel based FE (hvFE) models, voxels are directly converted to hexahedral elements with specific homogenized material properties depending on local bone density. An example of a FEA test is the VirtuOst test. FEA ) is the central technology used in the proprietary VirtuOst test to conduct a "virtual" stress test for determining breaking strength. This virtual stress test considers the amount of bone mass a patient has, as well as bone size, shape, and internal structure, and provides analysis called Biomechanical Computer Tomography Analysis (BCT), CT-based measurements for BMD and bone strength.
Johannesdottir and associates (2018) reviewed the ability of CT-based methods (e.g., finite element analysis [FEA]) to predict incident hip and vertebral fractures. These investigators stated that CT-based techniques with concurrent calibration all showed strong associations with incident hip and vertebral fracture, predicting hip and vertebral fractures as well as, and sometimes better than, dual-energy X-ray absorptiometry areal biomass density (DXA aBMD). There is growing evidence for use of routine CT scans for bone health assessment. These investigators noted that CT-based techniques provide a robust approach for osteoporosis diagnosis and fracture prediction. It remains to be seen if further technical advances will improve fracture prediction compared to DXA aBMD. Future work should include more standardization in CT analyses, establishment of treatment intervention thresholds, and more studies to determine whether routine CT scans can be efficiently used to expand the number of individuals who undergo evaluation for fracture risk.
Groenen and colleagues (2018) noted that current FE models predicting failure behavior comprise single vertebrae, thereby neglecting the role of the posterior elements and intervertebral discs. These investigators developed a more clinically relevant, case-specific non-linear FE model of 2 functional spinal units able to predict failure behavior in terms of the vertebra predicted to fail; deformation of the specimens; stiffness; and load to failure. In addition, they examined the effect of different bone density-mechanical properties relationships (material models) on the prediction of failure behavior. Twelve 2 functional spinal units (T6 to T8, T9 to T11, T12 to L2, and L3 to L5) with and without artificial metastases were destructively tested in axial compression. These experiments were simulated using CT-based case-specific non-linear FE models. Bone mechanical properties were assigned using 4 commonly used material models. In 10 of the 11 specimens, the FE model was able to correctly indicate which vertebrae failed during the experiments. However, predictions of the three-dimensional (3D) deformations of the specimens were less promising. Whereas stiffness of the whole construct could be strongly predicted (R2 = 0.637 to 0.688, p < 0.01), these researchers obtained weak correlations between FE predicted and experimentally determined load to failure, as defined by the total reaction force exhibiting a drop in force (R2 = 0.219 to 0.247, p > 0.05). Furthermore, they found that the correlation between predicted and experimental fracture loads did not strongly depend on the material model implemented, but the stiffness predictions did. The authors concluded that whereas the FE model was able to correctly indicate which vertebrae failed during the experiments, it had difficulties predicting the 3D deformation of the specimens. In addition, stiffness could be strongly predicted by this model, but these researchers obtained weak correlations between FE predicted and experimentally determined vertebral strength. Thus, this work showed that, in its current state, the FE models may be used to identify the weakest vertebra, but that substantial improvements are needed to quantify in-vivo failure loads.
These investigators stated that the FE model might profit from more realistic intervertebral discs (IVDs) models. In contrast to the bone material behavior, the IVD properties used in this study were not case-specific but obtained from the literature. However, both the type of material model and values for coefficients used in previous FE studies varied highly. The effect of these varying parameters on predictions of vertebral stiffness and/or bone strength is not well-studied. Thus, effort could be put in determining (case‐specific) mechanical properties of IVD tissue, and, subsequently, in examining how implementing these properties in FE models affects the failure behavior of both single vertebra and 2 functional spinal units. In addition, gaining more insight into the effect of IVD properties on endplate failure would be valuable, as endplate failure could not be captured correctly by the current FE model. For this reason, emphasis should also be put on further characterizing and adequately simulating the endplates’ mechanical properties. Furthermore, in case of sufficient resources and anatomical specimens, it would be interesting to combine testing of 2 functional spinal units with single vertebra tests; thus, the validity of the material models could be better tested. These researchers also stated that whereas in the experiments soft tissues, including the spinal ligaments and facet capsules, were left intact, these structures were not accounted for in the FE simulations. Spinal ligaments may contribute to the specimens’ stiffness and strength, especially when moving in flexion, extension, or lateral bending. As these researchers allowed the specimens to pivot around the load application point, such movements could occur. Adding ligaments and facet capsules to the FE model provides loading conditions being more realistic and better mimicking the experimental conditions, which potentially results in a better predictive capacity of the FE model.
Rajapakse and Chang (2018) noted that hip fractures have catastrophic consequences. These investigators reviewed recent developments in high-resolution magnetic resonance imaging (MRI)-guided FEA of the hip as a means to determine subject-specific bone strength. Despite the ability of DXA to predict hip fracture, the majority of fractures occur in patients who do not have BMD T scores less than - 2.5. Thus, without other detection methods, these individuals go undetected and untreated. Of methods available to image the hip, MRI is currently the only one capable of depicting bone microstructure in-vivo. Availability of micro-structural MRI allowed generation of patient-specific micro-FE models that can be used to simulate real-life loading conditions and determine bone strength. The authors concluded that MRI-based FEA enabled radiation-free approach to evaluate hip fracture strength. These researchers stated that with further validation, this technique could become a potential clinical tool in managing hip fracture risk.
Allaire and co-workers (2019) stated that previous studies showed vertebral strength from CT-based FEA may be associated with vertebral fracture risk. These investigators found vertebral strength had a strong association with new vertebral fractures, suggesting that vertebral strength measures may identify those at risk for vertebral fracture and may be a useful clinical tool. In a case-control study, these researchers examined the association between vertebral strength by QCT-based FEA and incident vertebral fracture (VF). In addition, they determined sensitivity and specificity of previously proposed diagnostic thresholds for fragile bone strength and low BMD in predicting VF. A total of 26 incident VF cases (13 men, 13 women) and 62 age- and sex-matched controls aged 50 to 85 years were selected from the Framingham multi-detector CT cohort. Vertebral compressive strength, integral volumetric BMD (vBMD), trabecular vBMD, CT-based BMC, and CT-based aBMD were measured from CT scans of the lumbar spine. Lower vertebral strength at baseline was associated with an increased risk of new or worsening VF after adjusting for age, BMI, and prevalent VF status (OR = 5.2 per 1 SD decrease, 95 % CI: 1.3 to 19.8). Area under receiver operating characteristic (ROC) curve comparisons revealed that vertebral strength better predicted incident VF than CT-based aBMD (AUC = 0.804 versus 0.715, p = 0.05); but was not better than integral vBMD (AUC = 0.815) or CT-based BMC (AUC = 0.794). Furthermore, proposed fragile bone strength thresholds trended toward better sensitivity for identifying VF than that of aBMD-classified osteoporosis (0.46 versus 0.23, p = 0.09). The authors concluded that the findings of this study showed an association between vertebral strength measures and incident vertebral fracture in men and women. These researchers stated that although limited by a small sample size (n = 26), these findings also suggested that bone strength estimates by CT-based FEA provided equivalent or better ability to predict incident vertebral fracture compared to CT-based aBMD. These findings need to be validated by well-designed studies.
Westbury and colleagues (2019) noted that high-resolution peripheral QCT (HRpQCT) is increasingly used for examining associations between bone micro-architectural and FEA parameters and fracture. These researchers hypothesized that combining bone micro-architectural parameters, geometry, BMD and FEA estimates of bone strength from HRpQCT may improve discrimination of fragility fractures. The analysis sample comprised of 359 subjects (aged 72 to 81 years) from the Hertfordshire Cohort Study (HCS). Fracture history was determined by self-report and vertebral fracture assessment. Subjects underwent HRpQCT scans of the distal radius and DXA scans of the proximal femur and lateral spine. Poisson regression with robust variance estimation was used to derive relative risks (RRs) for the relationship between individual bone micro-architectural and FEA parameters and previous fracture. Cluster analysis of these parameters was then performed to identify phenotypes associated with fracture prevalence. Receiver operating characteristic analysis suggested that bone micro-architectural parameters improved fracture discrimination compared to areal BMD (aBMD) alone, whereas further inclusion of FEA parameters resulted in minimal improvements. Cluster analysis (k-means) identified 4 clusters. The 1st had lower Young modulus, cortical thickness, cortical volumetric density and Von Mises stresses compared to the wider sample; fracture rates were only significantly greater among women (RR [95 % CI] compared to lowest risk cluster: 2.55 [1.28 to 5.07], p = 0.008). The 2nd cluster in women had greater trabecular separation, lower trabecular volumetric density and lower trabecular load with an increase in fracture rate compared to lowest risk cluster (1.93 [0.98 to 3.78], p = 0.057). These findings may help inform intervention strategies for the prevention and management of osteoporosis. The authors concluded that micro-architectural deterioration, bone geometry and, in women, FEA-derived bone strength contributed to an increased risk of previous fracture. Cluster analysis revealed a cortical and a trabecular deficiency phenotype, which both showed lower aBMD in men and women. Only women with the cortical deficiency phenotype had significantly increased risk of previous fractures. In this cohort, adding bone micro-architectural parameters to aBMD could better predict previous fracture, but further addition of FEA conferred little benefit.
The authors stated that this study had several drawbacks. First, a healthy responder bias has been observed in HCS and examining subject characteristics according to inclusion status has revealed healthier lifestyles at baseline for subjects included in the analysis sample compared to those who were not. However, these analyses were internal, so bias would only arise if the associations of Interest differed systematically between those who were included in the analysis sample and those who were not; this appeared unlikely. Second, temporal causation could not be inferred as this study had a cross-sectional design. It may be that the differences in bone microstructure observed were secondary to re-modelling in response to fracture, rather than properties of the bone that predispose to fracture, especially as these researchers had only collected information regarding previous fractures. Third, fracture status was missing for some subjects, although this information was available for the vast majority (91.9 %) of the analysis sample. Fourth, the low numbers of reported fractures and a relatively small sample size, along with the lack of stability regarding cluster analysis algorithms in general, may limit the generalizability of findings. However, the similarity of the clusters observed to those in other analyses and their biological plausibility suggested that they were robust.
In a review by Lewiecki, 2019 on “Osteoporotic fracture risk assessment,” the author lists FEA as one of the new and emerging technologies. The review states that “Finite element analysis (FEA) uses computer models of images and data from QCT of the spine or hip to assess bone strength. QCT-based FEA can be used to predict vertebral fracture in postmenopausal women and is comparable with spine DXA in predicting vertebral fractures in men; it is also comparable with hip DXA in predicting hip fractures in postmenopausal women and older men. FEA cannot be used to diagnose osteoporosis, initiate therapy, or monitor therapy. While all of these technologies have provided insight into skeletal properties other than BMD that determine bone strength, their role in clinical practice has not been defined. These techniques are used primarily in research settings”.
Bone mineral density (BMD) studies can be used to identify individuals with osteoporosis and monitor response to osteoporosis treatment, with the goal of reducing the risk of fracture. Bone density is most commonly evaluated with dual x-ray absorptiometry (DXA); other technologies are available.
For individuals who are eligible for screening of BMD based on risk factor assessment who receive DXA analysis of central sites (hip or spine), the evidence includes systematic reviews of randomized controlled trials and cohort studies. The relevant outcomes are morbid events, functional outcomes, quality of life, hospitalizations, and medication use. Central DXA is the most widely accepted method for measuring BMD and is the reference standard against which other screening tests are evaluated. BMD measurements with central DXA identify individuals at increased risk of fracture, and osteoporosis medications reduce fracture risk in the population identified as osteoporotic by central DXA. Therefore, test results with initial central DXA can be used to guide therapy.
For individuals without osteoporosis on initial screen who receive repeat DXA analysis of central sites (hip or spine), the evidence includes systematic reviews of large cohort and observational studies. The relevant outcomes are morbid events, functional outcomes, quality of life, hospitalizations, and medication use. Little research has been done on the frequency of BMD monitoring for osteoporosis. The available research has evaluated repeat measurement with central DXA. Evidence on whether repeat measurements add to risk prediction compared with a single measurement is mixed. Although the optimal interval may differ depending on risk factors, current evidence does not support repeat monitoring in patients with BMD on DXA in the normal range.
For individuals who are receiving pharmacologic treatment for osteoporosis who receive repeat DXA analysis of central sites (hip or spine), the evidence includes systematic reviews of randomized controlled trials and observational studies. The relevant outcomes are morbid events, functional outcomes, quality of life, hospitalizations, and medication use. There is no high-quality evidence to guide how often to monitor BMD during osteoporosis treatment. Within-person variation in measurement may exceed treatment effects, and fracture risk has been shown to be reduced in some treatment studies in the absence of changes in BMD. Together, these results suggest that frequent (i.e., every two years) repeat monitoring has low value. It is unclear whether DXA at the end of the initial five years of therapy is sufficiently accurate to guide subsequent therapy.
For individuals who are eligible for screening of BMD based on risk factor assessment who receive ultrasound densitometry, or quantitative computed tomography (including CT-based methods such as finite element analysis [FEA]), or DXA analysis of peripheral sites, the evidence includes observational studies and systematic reviews. The relevant outcomes are morbid events, functional outcomes, quality of life, hospitalizations, and medication use. In comparison with central DXA, other measures of bone health showed area under the curves around 0.80 for the identification of osteoporosis. These technologies are not commonly used for BMD measurements in practice, and no studies have shown that they can select patients who benefit from treatment for osteoporosis. There is little to no evidence on the usefulness of repeat measurement of BMD using these techniques.
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76977, 77078, 77080, 77081
NOT MEDICALLY NECESSARY
0508T, 0554T, 0555T, 0556T, 0557T, 0558T
Policy: 08.00.94m:Denosumab (Prolia®, Xgeva®), Romosozumab-aqqg (Evenity™)
Policy: 09.00.40d:Screening for Vertebral Fracture with Dual-Energy X-ray Absorptiometry (DEXA/DXA)
Policy: 00.06.02ac:Preventive Care Services (Independence)
Policy: 09.00.46ab:High-Technology Radiology Services (Independence)