Description:
Using low-dose X-rays of two different energy levels, whole-body dual-energy X-ray absorptiometry (DXA) measures lean tissue mass, total and regional body fat, as well as bone density. DXA scans have become a tool for research on body composition (e.g., as a more convenient replacement for underwater weighing). This evidence review addresses potential applications in clinical care rather than research use of the technology.

For individuals who have a clinical condition associated with abnormal body composition who receive DXA body composition studies, the evidence includes systematic reviews and several cross-sectional studies comparing DXA with other techniques. The relevant outcomes are symptoms and change in disease status. The available studies were primarily conducted in research settings and often used DXA body composition studies as a reference standard; these studies do not permit conclusions about the accuracy of DXA for measuring body composition. A systematic review exploring the clinical validity of DXA against reference methods for the quantification of intra-abdominal adipose tissue raised concerns regarding precision and reliability. More importantly, no studies were identified in which DXA body composition measurements were actively used in patient management. The evidence is insufficient to determine the effects of the technology on health outcomes.

For individuals who have a clinical condition managed by monitoring changes in body composition over time who receive serial DXA body composition studies, the evidence includes several prospective studies monitoring patients over time. The relevant outcomes are symptoms and change in disease status. The studies used DXA as a tool to measure body composition and were not designed to assess the accuracy of DXA. None of the studies used DXA findings to make patient management decisions or addressed how serial body composition assessment might improve health outcomes. The evidence is insufficient to determine the effects of the technology on health outcomes.

Background
Body Composition Measurement
Body composition measurements can be used to quantify and assess the relative proportions of specific body compartments such as fat and lean mass (e.g., bones, tissues, organs, muscles). These measurements may be more useful in informing diagnosis, prognosis, or therapy than standard assessments (e.g., body weight, body mass index) that do not identify the contributions of individual body compartments or their particular relationships with health and disease. While these body composition measurements have been most frequently utilized for research purposes, they may be useful in clinical settings to:

• Evaluate the health status of undernourished patients, those impacted by certain disease states (e.g., anorexia nervosa, cachexia), or those undergoing certain treatments (e.g., antiretroviral therapy, bariatric surgery).
• Evaluate the risk of heart disease or diabetes by measuring visceral fat vs total body fat.
• Assess body composition changes related to growth and development (e.g., infancy, childhood), aging (e.g., sarcopenia), and in certain disease states (e.g., HIV, diabetes).
• Evaluate patients in situations where body mass index is suspected to be discordant with total fat mass (e.g., body-building, edema).

A variety of techniques has been researched, including most commonly, anthropomorphic measures, bioelectrical impedance, and dual-energy X-ray absorptiometry (DXA). All of these techniques are based in part on assumptions about the distribution of different body compartments and their density, and all rely on formulas to convert the measured parameter into an estimate of body composition. Therefore, all techniques will introduce variation based on how the underlying assumptions and formulas apply to different populations of subjects (i.e., different age groups, ethnicities, or underlying conditions). Techniques using anthropomorphics, bioelectrical impedance, underwater weighing, and DXA are briefly reviewed below. 

Anthropomorphic Techniques
Anthropomorphic techniques for the estimation of body composition include measurements of skinfold thickness at various sites, bone dimensions, and limb circumference. These measurements are used in various equations to predict body density and body fat. Due to its ease of use, measurement of skinfold thickness is one of the most common techniques. The technique is based on the assumption that the subcutaneous adipose layer reflects total body fat but this association may vary with age and sex.

Bioelectrical Impedance
Bioelectrical impedance analysis is based on the relation among the volume of the conductor (ie, human body), the conductor's length (i.e., height), the components of the conductor (i.e., fat and fat-free mass), and its impedance. Estimates of body composition are based on the assumption that the overall conductivity of the human body is closely related to lean tissue. The impedance value is then combined with anthropomorphic data to give body compartment measures. The technique involves attaching surface electrodes to various locations on the arm and foot. Alternatively, the patient can stand on the pad electrodes.

Underwater Weighing
Underwater weighing requires the use of a specially constructed tank in which the subject is seated on a suspended chair. The subject is then submerged in the water while exhaling. While valued as a research tool, weighing people underwater is typically not suitable for routine clinical use. This technique is based on the assumption the body can be divided into two compartments with constant densities: adipose tissue, with a density of 0.9 g/cm³, and lean body mass (i.e., muscle and bone), with a density of 1.1 g/cm³. One limitation of the underlying assumption is the variability in density between muscle and bone, e.g., bone has a higher density than muscle, and bone mineral density varies with age and other conditions. Also, the density of body fat may vary, depending on the relative components of its constituents (e.g., glycerides, sterols, glycolipids).

Dual-Energy X-Ray Absorptiometry
While the cited techniques assume two body compartments, DXA can estimate three body compartments consisting of fat mass, lean body mass, and bone mass. DXA systems use a source that generates X-rays at two energies. The differential attenuation of the two energies is used to estimate the bone mineral content and soft tissue composition. When two X-ray energies are used, only two tissue compartments can be measured; therefore, soft tissue measurements (i.e., fat and lean body mass) can only be measured in areas in which no bone is present. DXA can also determine body composition in defined regions (i.e., the arms, legs, and trunk). DXA measurements are based in part on the assumption that the hydration of fat-free mass remains constant at 73%. Hydration, however, can vary from 67% to 85% and can vary by disease state. Other assumptions used to derive body composition estimates are considered proprietary by DXA manufacturers. The use of DXA for bone mineral density assessment in patients diagnosed with or at risk of osteoporosis is addressed separately in evidence review 60101. Vertebral fracture assessment with densitometry by DXA is addressed separately in evidence review 60144.

REGULATORY STATUS
Body composition software for several bone densitometer systems have been approved by the U.S. Food and Drug Administration through the premarket approval process. They include the Lunar iDXA systems (GE Healthcare, Madison, WI), Hologic DXA systems (Hologic, Bedford, MA), and Norland DXA systems (Norland, at Swissray, Fort Atkinson, WI).

Policy:
Dual X-ray absorptiometry (DEXA) body composition studies are investigational and/or unproven and is therefore considered NOT MEDICALLY NECESSARY.

Policy Guidelines 
Coding
Please see the Codes table for details.

Benefit Application
BlueCard/National Account Issues
State or federal mandates (e.g., FEP) may dictate that all devices approved by the U.S. Food and Drug Administration (FDA) may not be considered investigational, and thus these devices may be assessed only on the basis of their medical necessity.

Rationale 
Evidence reviews assess whether a medical test is clinically useful. A useful test provides information to make a clinical management decision that improves the net health outcome. That is, the balance of benefits and harms is better when the test is used to manage the condition than when another test or no test is used to manage the condition.

The first step in assessing a medical test is to formulate the clinical context and purpose of the test. The test must be technically reliable, clinically valid, and clinically useful for that purpose. Evidence reviews assess the evidence on whether a test is clinically valid and clinically useful. Technical reliability is outside the scope of these reviews, and credible information on technical reliability is available from other sources.

Dual-Energy X-Ray Absorptiometry as a Test To Detect Abnormal Body Composition
Clinical Context and Test Purpose
The purpose of dual-energy x-ray absorptiometry (DXA) body composition studies is to improve the diagnosis and management of patients who have a clinical condition associated with abnormal body composition.

The question addressed in this evidence review is: Does the use of DXA improve the net health outcome in patients with clinical conditions associated with abnormal body composition?

The following PICO was used to select literature to inform this review.

Populations
The relevant population of interest is individuals with clinical conditions associated with abnormal body composition.

Interventions
The test being considered is DXA body composition studies.

Comparators
The following practices are currently being used to make decisions in this patient group: standard of care without DXA or an alternative method of body composition analysis.

Outcomes
The general outcomes of interest include symptom management and change in disease status. For patients at risk of osteoporosis, outcomes of interest would include fracture incidence. For patients with human immunodeficiency virus (HIV) who are treated with antiretroviral therapy, outcomes of interest would include lipodystrophy.

Study Selection Criteria
For the evaluation of clinical validity of DXA body composition testing, studies that meet the following eligibility criteria were considered:

• Reported on the accuracy of the marketed version of the technology (including any algorithms used to calculate scores)
• Included a suitable reference standard
• Patient/sample clinical characteristics were described
• Patient/sample selection criteria were described.

Clinically Valid
A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

Review of Evidence
Systematic Reviews
A systematic review and meta-analysis comparing the accuracy of alternative comparators versus reference standard computed tomography (CT) and magnetic resonance imaging (MRI) methods for the quantification of intra-abdominal adipose tissue (IAAT) was published by Murphy et al. (2019).¹ This systematic review assessed the performance of DXA for IAAT volume quantification and compared the performance of both DXA and bioelectric impedance analysis (BIA) approaches for IAAT area quantification. The American Society for Parenteral and Enteral Nutrition (ASPEN) also conducted a systematic review to evaluate the validity of relevant body composition methods in various clinical populations.² The use of DXA, ultrasound, and BIA for body composition analysis was investigated. Fifteen studies featuring comparisons of DXA to reference standard methods (eg, MRI and CT) were identified. Nine studies using CT or MRI to validate DXA measures of abdominal fat mass (FM) or total body FM were used for pooled analyses. Characteristics and results of studies included for meta-analysis are summarized in Tables 1 and 2.

Table 1. Systematic Review & Meta-Analysis Characteristics

Study; Subgroup	Dates	Trials	Participants1	N (Range)	Design	Duration
Murphy et al. (2019)1	1995 – 2018	23	Studies: With IAAT quantified in humans by CT or MRI reference methods and one of DXA, ultrasound, BIA, or air displacement plethysmography With reference and comparator methods that quantify IAAT at the same anatomical location in the same unit of measurement With reported or quantifiable mean differences and SDs of IAAT quantity	6116 (29 to 2689)	Cross-sectional, diagnostic test accuracy studies Retrospective studies	NR
IAAT Area
DXA	2012 – 2014	3	Included population groups: Elderly adult men and women evaluated by DXA and CT at L4 to L5 Premenopausal women evaluated by DXA and CT at L4 to L5 Premenopausal women evaluated by DXA and CT at L4	381 (115 to 135)	Cross-sectional, diagnostic test accuracy studies Retrospective studies	NR
BIA	2008 – 2018	9*	Included population groups: Elderly Caucasian men and women evaluated by BIA and CT at L3 to L4 Elderly Korean adult men and women evaluated by BIA and CT at umbilicus Elderly Korean adult men and women evaluated by BIA and CT at L4 to L5 Japanese outpatients with obesity evaluated by BIA and CT at umbilicus Elderly, middle-aged, and adult Chinese men and women evaluated by BIA and CT at L4 to L5 Elderly adult men and women evaluated by BIA and MRI at L4 to L5 Elderly, middle-aged, adult, and young men and women evaluated by BIA and CT at L4 to L5	2139 (100 to 1006)	Cross-sectional, diagnostic test accuracy studies Retrospective studies	NR
IAAT Volume
DXA	2012 – 2018	7**	Included population groups: Adult men and women evaluated by DXA and CT from S1 to head region Elderly adult men and women evaluated by DXA and CT from S1 to head region Women with PCOS evaluated by DXA and MRI at L3 Middle-Eastern adult men and women evaluated by DXA and MRI at android region Adult men and women evaluated by DXA and MRI at L2 to L3 with conversion to L1 through L5	3410 (40 to 2689)	Cross-sectional, diagnostic test accuracy studies Retrospective studies	NR
IAAT Thickness
US	2010 – 2014	4	Included population groups: Obese women with infertility evaluated by US and CT at L4 to L5 Middle-aged men and women evaluated by US and CT at L2 to L3 Elderly and adult men and women evaluated by US and MRI at L2 to L3 Elderly men and women evaluated by US and MRI at L4	186 (29 to 74)	Cross-sectional, diagnostic test accuracy studies Retrospective studies	NR
Sheean et al. (2019)2, (ASPEN)	2001 – 2013	9	Studies: With body compositions assessed in clinical populations via DXA and a reference standard method (eg, MRI or CT) With correlation analyses	1660 (39 to 625)	Cross-sectional, diagnostic accuracy studies Retrospective studies	NR
Abdominal FM in any disease via DXA	2004 – 2013	4	Included population groups: Urban Asian Indians with type 2 diabetes Premenopausal women with anorexia nervosa Middle-aged Indian men with CVD Multiethnic cohort of men and women with HIV	874 (39 to 625)	Cross-sectional, diagnostic accuracy studies Retrospective studies	NR
Total FM in any disease via DXA	2001 – 2013	7	Included population groups: Women with CVD Postmenopausal women with CVD Men and women with CVD Middle-aged Indian men with CVD Individuals with myosteatosis Multiethnic cohort of men and women with HIV	1473 (66 to 625)	Cross-sectional, diagnostic accuracy studies Retrospective studies	NR
Total FM in CVD via DXA	2001 – 2013	5	Included population groups: Men and women with CVD Postmenopausal women with CVD Middle-aged Indian men with CVD	521 (66 to 132)	Cross-sectional, diagnostic accuracy studies Retrospective studies	NR

ASPEN: American Society for Parenteral and Enteral Nutrition; BIA: bioelectrical impedance analysis; CT: computed tomography; CVD: cardiovascular disease; DXA: dual-energy x-ray absorptiometry; FM: fat mass; HIV: human immunodeficiency virus; IAAT: intra-abdominal adipose tissue; MRI: magnetic resonance imaging; NR: not reported; PCOS: polycystic ovarian syndrome; SD: standard deviation; US: ultrasound. 
1 Key study eligibility criteria and demographics of included subgroup participants.
* 3 of 9 trials were sampled twice for a total of 12 result sets due to use of multiple techniques for IAAT quantification via BIA. 
** 1 of 8 trials was categorized as an outlier and excluded from pooled analysis. 

Table 2. Systematic Review & Meta-Analysis Results

Study	Mean Difference in IAAT Volume		Mean Difference in IAAT Area			Mean Difference in IAAT Thickness
Murphy et al. (2019)1	DXA*		DXA	BIA		US
Total N	3410		381	2139		186
Pooled mean difference (95% LoA)	-10 (-280 to 300) (cm3)		8.09 (-98.88 to 115.07) (cm2)	-11.63 (-43.12 to 19.85) (cm2)		-0.32 (-3.82 to 3.17) (cm)
Significance of mean difference (p)	.808		.061	.004		.400
I2 (p)	99% (< .001)		98% (< .001)	94% (< .001)		93% (< .001)
Q	Q(6)  = 458		Q(2)  = 31	Q(11)  = 544		Q(3)  = 41
Range of N	40 to 2689		115 to 135	100 to 1006		29 to 74
Range of pooled mean differences	(-451 to 262) (cm3)		(3.78 to 16.70) (cm2)	(-57.20 to 10.96) (cm2)		(-1.10 to 0.40) (cm)
DXA Subgroup Analysis	Mean Difference in IAAT Volume by DXA and Gender			Mean Difference in IAAT Volume by DXA and Reference Method
Subgroup	Men		Women	CT		MRI
Subgroup N (Total N)	1483 (3287)		1804 (3287)	377 (3410)		3033 (3410)
Pooled mean difference (95% LoA) (cm3)	144.04 (-512.29 to 800.38)		59.96 (-381.08 to 492.99)	-41.15 (-881.96 to 930.25)		49.52 (-498.42 to 586.23)
Significance for subgroup comparison (p)	.042			.311
I2	95%		90%	100%		90%
Range of Subgroup N	20 to 1212		20 to 1477	109 to 145		40 to 2689
Range of pooled mean differences (cm3)	-43 to 379		4 to 143	451 to 262		4 to 104
Sheean et al. (2019)2 (ASPEN)	DXA-derived Abdominal FM		DXA-derived Total FM
	DXA vs. CT-derived VAT in any disease		DXA vs. CT/MRI-derived VAT in any disease		DXA vs. CT/MRI-derived VAT in CVD
Total N	874		1473		521
Pooled random effects correlation (95% CI)	0.74 (0.52 to 0.86)		0.71 (0.45 to 0.86)		0.71 (0.45 to 0.84)
I2 (p)	87% (< .01)		98% (< .01)		95% (< .01)
Range of N	39 to 625		66 to 625		66 to 132
Range of individual correlations	0.52 to 0.86		0.49 to 0.80		0.49 to 0.87

ASPEN: American Society for Parenteral and Enteral Nutrition; BIA: bioelectrical impedance analysis; CI: confidence interval; CT: computed tomography; CVD: cardiovascular disease; DXA: dual-energy x-ray absorptiometry; FM: fat mass; IAAT: intra-abdominal adipose tissue; LoA: limits of agreement; MRI: magnetic resonance imaging; US: ultrasound; VAT: visceral adipose tissue. 
* Results following the removal of a study due to identification as an outlier. 

While this analysis was primarily focused on the utilization of the different body composition methods for the management of obesity, direct effects on key health outcomes were not explored and patient populations included for analysis displayed extensive heterogeneity and largely featured healthy populations. Measurements of IAAT volume were deemed comparable to the reference methods, however, 95% limits of agreement (LoA) were wide and these results were not seen until the removal of an outlying study. Rationale for identifying the study as an outlier and removing it from the meta-analysis was limited. Prior to the removal of the outlier, the pooled mean difference was significant compared to the reference methods at -124 cm3 (95% LoA, -479 to 230; p = .013; I2 = 99% [p < .001]; Q(7) = 773). Performance of DXA for the measurement of IAAT volume also varied significantly between male and female subgroups. Furthermore, included studies did not pre-determine clinically meaningful LoA. The authors' further caution that DXA measurement of IAAT volume has the capacity to differ from reference methods by more than 100%, however, the clinical significance of these margins of error are uncertain in individuals with obesity. While IAAT area cutoff points have been described for the determination of metabolic risk and visceral obesity based on single-slice CT, the authors do not recommend utilization of DXA IAAT area measurements for this purpose due to wide LoA. The clinical utility of existing IAAT area cutpoints is also uncertain as these parameters were found to have applicability for women and cannot necessarily be extrapolated to mixed populations.

ASPEN recommends the use of DXA for the assessment of FM in patients with a specific disease or clinical outcome with a strong recommendation rating based on their analysis. Due to the lack of studies reporting on the validity of DXA for lean mass measurements, no recommendations could be made for assessments of this body compartment. The systematic review acknowledges that while the quality of the included evidence was low, the strong recommendation rating was applied with the rationale that the net benefits of FM assessment via DXA outweigh potential harms. However, the use of DXA findings to make patient management decisions and reporting of adverse events was not featured in the included studies.

Calella et al. (2019) performed a systematic review exploring various methods for body composition analysis in patients with cystic fibrosis (CF).³ A previous systematic review by Calella et al. (2018) presented on differences in body composition between patients with CF and healthy controls evaluated by DXA and other methods.⁴ DXA was most frequently used to measure lean body or fat-free mass which was significantly reduced in CF patients. While several included studies showed a correlation between lower fat-free mass and impaired pulmonary function, application, and use of this measure in patient management and its impact on health outcomes was not explored and requires further clarification. Since these reviews featured qualitative analyses, data on clinical validity could not be extracted.

A systematic review by Bundred et al. (2019) evaluated body composition assessment and sarcopenia in patients with pancreatic ductal adenocarcinoma.⁵ Meta-analyses revealed that sarcopenia was associated with lower overall survival in both operable (harms ratio, 1.95; 95% confidence interval [CI], 1.35 to 2.81; p < .001) and unresectable patients (harms ratio, 2.49; 95% CI, 1.38 to 4.48; p = .002). However, of the 42 included studies, only 1 utilized measurements obtained by DXA, limiting the relevance of the overall findings to this technology and preventing extraction of pertinent clinical validity data. Furthermore, the authors caution that many studies failed to account for variation introduced by gender, race, tumor stage, and other factors. Additionally, clear criteria for the diagnosis of sarcopenia or cachexia via body composition assessments with DXA are lacking.

Cross-Sectional Studies
Most of the literature on DXA as a diagnostic test to detect abnormal body composition involves the use of the technology in the research setting, often as a reference test; studies have been conducted in different populations of patients and underlying disorders.^{6,7,8,9,10,11,12,13,14,15,16,17,18} In some cases, studies have compared other techniques with DXA to identify simpler methods of determining body composition. In general, these studies have shown that DXA is highly correlated to various methods of body composition assessment. For example, a study by Alves et al. (2014) compared 2 bioelectrical impedance devices with DXA for the evaluation of body composition in heart failure.⁶ Ziai et al. (2014) compared bioelectric impedance analysis with DXA for evaluating body composition in adults with CF.⁷ The literature on DXA in population-based cohorts (e.g., National Health and Nutrition Examination Survey [NHANES], Prospective Epidemiological Risk Factor Study)^19,20 involves the use of the technology to predict risk of overall mortality or cancer incidence. These studies often use DXA as a reference test to assess whether agreement with anthropometric measures (e.g., body mass index [BMI], relative fat mass [RFM]) is present.¹⁹ or absent.²⁰ Whether or not a DXA scan is considered the reference standard, the key consideration regarding its routine clinical use is whether the results of the scan can be used to manage patients and improve health outcomes.

Case-Control Studies
As a single diagnostic measure, it is important to establish diagnostic cutoff points for normal and abnormal values. This is problematic because normal values will require the development of normative databases for the different components of body composition (i.e., bone, fat, lean mass) for different populations of patients at different ages. Regarding measuring bone mineral density (BMD), normative databases have largely focused on postmenopausal white women, and these values cannot necessarily be extrapolated to men or to different races. DXA determinations of BMD are primarily used for fracture risk assessment in postmenopausal women and to select candidates for various pharmacologic therapies to reduce fracture risk. In an example regarding lean mass, Reina et al. (2019) conducted a case-control study to assess the correlation of BMI or serum albumin levels to DXA-derived parameters of nutritional status and sarcopenia in women (N = 89) with rheumatoid arthritis.²¹ While 44% of cases met diagnostic criteria for sarcopenia based on quantification of the skeletal muscle index, a reference technique was not clearly identified in this study. Skeletal muscle index is calculated by dividing appendicular skeletal muscle mass by the square of the patient's height. A previously identified threshold of ≤ 5.75 kg/m2 in women was applied, however, this metric was established through the use of BIA in a slightly older patient population. Given that DXA provides measures of lean mass which may be influenced by body compartments other than skeletal muscle, the relevance of this diagnostic cutoff point is uncertain. Furthermore, the study utilized a control group composed of patients affected by non-inflammatory rheumatic disorders as opposed to healthy controls, further limiting the relevance of applied cutoff points. In addition to the aforementioned uncertainties of establishing and applying normal values for components of body composition, it also is unclear how a single measure of body composition would be used in patient management. Studies discussing appropriate use and determination of DXA-derived lean mass cutoffs for sarcopenia in various populations of patients and underlying disorders continue to be featured in the literature.^22,23

Clinically Useful
A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.

Direct Evidence
Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from randomized controlled trials (RCTs).

No RCTs were identified to support the utility of DXA for this indication.

Chain of Evidence
Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

Because the clinical validity of DXA for this population is limited, a chain of evidence cannot be constructed.

Section Summary: Dual-energy X-ray Absorptiometry as a Test to Detect Abnormal Body Composition
The available evidence was generated primarily in research settings and often used DXA body composition studies as a reference standard; these studies do not permit conclusions about the accuracy of DXA for measuring body composition. A systematic review exploring the clinical validity of DXA measurements against reference methods for the quantification of IAAT raised concerns regarding precision and reliability. Additionally, no studies were identified in which DXA body composition measurements were actively used in patient management.

Dual-Energy X-Ray Absorptiometry as a Test To Monitor Changes in Body Composition
Clinical Context and Test Purpose
The purpose of serial DXA body composition studies in patients who have a clinical condition managed by monitoring body composition changes over time is to improve disease management.

The question addressed in this evidence review is: Does serial DXA improve the net health outcome in patients with clinical conditions managed by monitoring body composition changes over time?

The following PICO was used to select literature to inform this review.

Populations
The relevant population of interest is individuals with clinical conditions managed by monitoring body composition changes over time.

Interventions
The test being considered is serial DXA body composition studies.

Comparators
The following practices are currently being used to make decisions in this patient group: standard of care without DXA or an alternative method of body composition analysis.

Outcomes
The general outcomes of interest include symptom management and change in disease status. For patients with anorexia nervosa, outcomes of interest would include disease-related morbidity, disease-related mortality, and rate of remission.

Study Selection Criteria
For the evaluation of clinical validity of DXA body composition testing, studies that meet the following eligibility criteria were considered:

• Reported on the accuracy of the marketed version of the technology (including any algorithms used to calculate scores)
• Included a suitable reference standard
• Patient/sample clinical characteristics were described
• Patient/sample selection criteria were described.

Clinically Valid
A test must detect the presence or absence of a condition, the risk of developing a condition in the future, or treatment response (beneficial or adverse).

The ability to detect a change in body composition over time is related in part to the precision of the technique, defined as the degree to which repeated measurements of the same variable give the same value. For example, DXA measurements of bone mass are thought to have a precision error of 1% to 3% and, given the slow rate of change in BMD in postmenopausal women treated for osteoporosis, it is likely that DXA scans would only be able to detect a significant change in BMD in the typical patient after 2 years of therapy. Of course, changes in body composition are anticipated to be larger and more rapid than changes in BMD in postmenopausal women; therefore, precision errors in DXA scans become less critical in interpreting results. However, precision errors for other body compartments such as lean and fat mass may differ and impact clinical validity. Coefficients of variation as high as 42.2% have been reported for FM.²⁴

Review of Evidence
Prospective Studies
Several studies have reported on DXA measurement of body composition changes over time in clinical populations; none of these studies used DXA findings to make patient management decisions and few addressed how serial body composition assessment might improve health outcomes.^{25,26,24,27,28} A long-term prospective study assessing the association between body fat and breast cancer risk in postmenopausal women with a normal BMI was published by Iyengar et al. (2019), featuring the ad hoc secondary analysis of results from the Women's Health Initiative RCT and observational study cohorts.²⁷ Women (N = 3,460) were assessed at baseline and during years 1, 3, 6, and 9 for BMI and via DXA. Multivariable-adjusted hazard ratios (HR) for the association of various body fat measures with the risk of developing invasive or estrogen receptor positive (ER+) breast cancer were reported. Median follow-up duration was 16.9 years. Characteristics and results of clinical validity for breast cancer risk assessment are summarized in Tables 3 and 4.

Table 3. Study Characteristics of Clinical Validity of Risk Assessment

Study	Study Population	Designa	Reference Standard	Timing of Reference and Index Tests	Blinding of Assessors	Commentb
Iyengar et al. (2019)27	Postmenopausal women aged 50 to 79 years enrolled in the Women's Health Initiative (WHI) RCT or observational study were considered for study. Women from 3 WHI trial centers were assessed longitudinally for body fat composition. Data from women with normal BMIs were assessed for correlations with breast cancer outcomes.	Prospective, sample selection NR	Clinical outcomes were confirmed via questionnaires. Breast cancer cases were confirmed via review of medical records and pathology reports.	NR	NR	Risk outcomes for women in the RCT and observational cohorts were not analyzed separately. Given that treatments utilized in the RCT group may have had an impact on breast cancer risk and outcomes, the relevance and utility of this study is uncertain.

BMI: body mass index; NR: not reported; RCT: randomized controlled trial.
a Note 2 aspects of design: prospective, retrospective or nonconcurrent prospective and sample selection random or consecutive
b Note other characteristics that could cause bias or limit relevance such as timeframe or practice setting.

Table 4. Clinical Validity of Breast Cancer Risk Assessment With Dual-Energy X-Ray Absorptiometry

Study; Subgroup; Body Fat DXA Measurement (Cutoff)	Initial N	Final N Cases/Person-Years	Excluded Samples	Prevalence of Condition	Clinical Validity Outcome: Multivariable Adjusted HR (95% CI)
					Baseline Body Fat Measures			Serial Body Fat Measures
Iyengar et al. (2019)27 Invasive Breast Cancer	3464*	3460	4*	182	Highest Quartile	p-value for trend	Per 5-unit increase	Cutoff	Time-Dependent
Whole-body fat mass, kg (> 25.1)	NR	NR	NR	57	1.89 (1.21 to 2.95)	.004	1.28 (1.10 to 1.49)	≥ 22.1	1.43 (1.06 to 1.93)
Whole-body fat, % (> 41.3)	NR	NR	NR	52	1.79 (1.14 to 2.83)	.03	1.19 (1.03 to 1.37)	≥ 38.0	1.45 (1.07 to 1.95)
Fat mass of trunk, kg (> 11.4)	NR	NR	NR	50	1.88 (1.18 to 2.98)	.002	1.46 (1.14 to 1.87)	≥ 9.4	1.50 (1.12 to 2.03)
Ratio of trunk fat mass to mean of legs (> 2.6)	NR	NR	NR	43	1.30 (0.83 to 2.02)	.10	NR	NR	NR
Iyengar et al. (2019)27 ER+ Breast Cancer	3464	3460	4*	146	Highest Quartile	p-value for trend	Per 5-unit increase	Cutoff	Time-Dependent
Whole-body fat mass, kg (> 25.1)	NR	NR	NR	48	2.21 (1.23 to 3.67)	.002	1.35 (1.14 to 1.60)	≥ 22.1	1.41 (1.01 to 1.97)
Whole-body fat, % (> 41.3)	NR	NR	NR	44	2.17 (1.29 to 3.66)	.01	1.27 (1.08 to 1.48)	≥ 38.0	1.50 (1.07 to 2.10)
Fat mass of trunk, kg (> 11.4)	NR	NR	NR	41	1.98 (1.18 to 3.31)	.003	1.56 (1.18 to 2.06)	≥ 9.4	1.46 (1.05 to 2.04)
Ratio of trunk fat mass to mean of legs (> 2.6)	NR	NR	NR	34	1.28 (0.78 to 2.10)	.13	NR	NR	NR

CI: confidence interval; DXA: dual-energy x-ray absorptiometry; ER+: estrogen receptor-positive; HR: hazard ratio; NR: not reported. 
* Excluded cases were lost to follow-up with ER+ status not reported. 
These results suggest that standard BMI categorization may be inadequate for the risk assessment of invasive breast cancers in postmenopausal women. However, the clinical utility of DXA findings on patient management protocols and health outcomes requires further study.

Arthur et al. (2020) published additional results from the Women's Health Initiative cohort of postmenopausal women (N = 10,931), reporting additional associations between DXA-derived measures of body fat and breast cancer risk.²⁹ The multivariable-adjusted HR for risk of invasive breast cancer per standard deviation (SD) increase in trunk fat mass was 1.21 (95% CI, 1.12 to 1.31) and whole body fat mass was 1.21 (95% CI, 1.12 to 1.30). The multivariable-adjusted HR for risk of ER+ breast cancer per SD increase in trunk fat mass was 1.21 (95% CI, 1.11 to 1.31) and whole body fat mass was 1.22 (95% CI, 1.11 to 1.33). Multivariable-adjusted HR for invasive breast cancer per SD increase in BMI was also significant, with an HR of 1.19 (95% CI, 1.10 to 1.28). Trends of time-dependent analyses of anthropometric measures and overall ER + incident breast cancer cases were significant for BMI (p < .001) and waist circumference (p < .001). Therefore, the added clinical utility of DXA-derived fat measures is unclear for this population.

Relevance and study design and conduct limitations are summarized in Tables 5 and 6.

Table 5. Study Relevance Limitations

Study	Populationa	Interventionb	Comparatorc	Outcomesd	Duration of Follow-Upe
Arthur et al. (2020)29	1. Study population is unclear.	2. Version used unclear regarding both DXA and patient participation in RCT treatment or observational groups.	3. Not compared to other tests used for same purpose.	3, 5. Key clinical validity outcomes not reported; adverse events of the test not described.
Iyengar et al. (2019)27	1, 4. Study population is unclear; study population not representative of intended use.	2. Version used unclear regarding both DXA and patient participation in RCT treatment or observational groups.	3. Not compared to other tests used for same purpose.	3, 5. Key clinical validity outcomes not reported; adverse events of the test not described.

 
DXA: dual-energy x-ray absorptiometry; RCT: randomized controlled trial.
The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment.
a Population key: 1. Intended use population unclear; 2. Clinical context is unclear; 3. Study population is unclear; 4. Study population not representative of intended use.
b Intervention key: 1. Classification thresholds not defined; 2. Version used unclear; 3. Not intervention of interest.
c Comparator key: 1. Classification thresholds not defined; 2. Not compared to credible reference standard; 3. Not compared to other tests in use for same purpose.
d Outcomes key: 1. Study does not directly assess a key health outcome; 2. Evidence chain or decision model not explicated; 3. Key clinical validity outcomes not reported (sensitivity, specificity, and predictive values); 4. Reclassification of diagnostic or risk categories not reported; 5. Adverse events of the test not described (excluding minor discomforts and inconvenience of venipuncture or noninvasive tests).
e Follow-Up key: 1. Follow-up duration not sufficient with respect to natural history of disease (true-positives, true-negatives, false-positives, false-negatives cannot be determined).

Table 6. Study Design and Conduct Limitations

Study	Selectiona	Blindingb	Delivery of Testc	Selective Reportingd	Data Completenesse	Statisticalf
Arthur et al. (2020)29	1. Selection not described.	1. Blinding not described.	1, 4. Timing of delivery of index or reference tests not clear; expertise of evaluators not described.	2. Evidence of selective reporting (covariates did not have to be pre-specified).
Iyengar et al. (2019)27	1. Selection not described.	1. Blinding not described.	1, 4. Timing of delivery of index or reference tests not clear; expertise of evaluators not described.	2. Evidence of selective reporting (covariates did not have to be pre-specified).	1. Inadequate description of indeterminate and missing samples.	2. Comparison with other tests not reported.

The study limitations stated in this table are those notable in the current review; this is not a comprehensive gaps assessment.
a Selection key: 1. Selection not described; 2. Selection not random or consecutive (i.e., convenience).
b Blinding key: 1. Not blinded to results of reference or other comparator tests.
c Test Delivery key: 1. Timing of delivery of index or reference test not described; 2. Timing of index and comparator tests not same; 3. Procedure for interpreting tests not described; 4. Expertise of evaluators not described.
d Selective Reporting key: 1. Not registered; 2. Evidence of selective reporting; 3. Evidence of selective publication.
e Data Completeness key: 1. Inadequate description of indeterminate and missing samples; 2. High number of samples excluded; 3. High loss to follow-up or missing data.
f Statistical key: 1. Confidence intervals and/or p values not reported; 2. Comparison with other tests not reported.

Clinically Useful
A test is clinically useful if the use of the results informs management decisions that improve the net health outcome of care. The net health outcome can be improved if patients receive correct therapy, or more effective therapy, or avoid unnecessary therapy, or avoid unnecessary testing.

Direct Evidence
Direct evidence of clinical utility is provided by studies that have compared health outcomes for patients managed with and without the test. Because these are intervention studies, the preferred evidence would be from RCTs.

No RCTs were identified to support the utility of DXA for this indication.

Chain of Evidence
Indirect evidence on clinical utility rests on clinical validity. If the evidence is insufficient to demonstrate test performance, no inferences can be made about clinical utility.

Because the clinical validity of DXA for this population cannot be established, a chain of evidence cannot be constructed.

Section Summary: Dual-Energy X-ray Absorptiometry as a Test to Monitor Changes in Body Composition
Studies assessing serial DXA used it as a tool to measure body composition and were not designed to assess the accuracy of DXA. None of the studies used DXA findings to make patient management decisions or addressed how serial body composition assessment might improve health outcomes.

Summary of Evidence
For individuals who have a clinical condition associated with abnormal body composition who receive DXA body composition studies, the evidence includes systematic reviews and several cross-sectional studies comparing DXA with other techniques. Relevant outcomes are symptoms and change in disease status. The available studies were primarily conducted in research settings and often used DXA body composition studies as a reference standard; these studies do not permit conclusions about the accuracy of DXA for measuring body composition. A systematic review exploring the clinical validity of DXA against reference methods for the quantification of IAAT raised concerns regarding precision and reliability. More importantly, no studies were identified in which DXA body composition measurements were actively used in patient management. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

For individuals who have a clinical condition managed by monitoring changes in body composition over time who receive serial DXA body composition studies, the evidence includes several prospective studies monitoring patients over time. Relevant outcomes are symptoms and change in disease status. The studies used DXA as a tool to measure body composition and were not designed to assess the accuracy of DXA. None of the studies used DXA findings to make patient management decisions or addressed how serial body composition assessment might improve health outcomes. The evidence is insufficient to determine that the technology results in an improvement in the net health outcome.

The purpose of the following information is to provide reference material. Inclusion does not imply endorsement or alignment with the evidence review conclusions.

Practice Guidelines and Position Statements
Guidelines or position statements will be considered for inclusion in Supplemental Information if they were issued by, or jointly by, a U.S. professional society, an international society with U.S. representation, or National Institute for Health and Care Excellence (NICE). Priority will be given to guidelines that are informed by a systematic review, include strength of evidence ratings, and include a description of management of conflict of interest.

American College of Radiology et al.
The American College of Radiology (ACR), the Society for Pediatric Radiology (SPR), and the Society of Skeletal Radiology (SRR) (2018) issued a collaborative practice parameter to assist practitioners in providing appropriate radiologic care for their patients.³⁰ Dual X-ray absorptiometry (DXA) was described as a "clinically proven, accurate and reproducible method of measuring bone mineral density (BMD) in the lumbar spine, proximal femur, forearm, and whole body," that "may also be used to measure whole-body composition, including nonbone lean mass (LM) and fat mass (FM)." DXA measurement of BMD, LM, or FM is indicated whenever a clinical decision is likely to be directly influenced by the test result. In particular, LM and FM may be useful in assessing conditions such as sarcopenia and cachexia. Specifically, DXA may be indicated as a tool for the measurement of regional and whole body FM and LM in patients afflicted with conditions such as malabsorption, cancer, or eating disorders.

International Society for Clinical Densitometry
The International Society for Clinical Densitometry (2019) updated its statements on the use of DXA for body composition.³¹ Use of DXA for measurement of body composition was suggested for use in the following clinical conditions:

• To assess fat distribution in patients with human immunodeficiency virus (HIV) who are using antiretroviral agents known to increase the risk of lipoatrophy.
• To assess fat and lean mass changes in obese patients undergoing bariatric surgery (or medical, diet, or weight loss regimens with anticipated large weight loss) when weight loss exceeds approximately 10%. The statement noted that the impact of DXA studies on clinical outcomes in these patients is uncertain.
• To assess fat and lean mass in patients with muscle weakness and poor physical functioning. The impact on clinical outcomes is uncertain.

Of note, pregnancy is a contraindication to use of DXA to measure body composition. The statement also adds that the clinical utility of DXA measurements of adiposity and lean mass (e.g., visceral adipose tissue, lean mass index, fat mass index) is uncertain. Furthermore, while the use of DXA adiposity measures such as fat mass index may be useful in risk-stratifying patients for cardio-metabolic outcomes, specific thresholds to define obesity have not been established.

International Conference on Sarcopenia and Frailty Research Task Force
Evidence-based clinical practice guidelines for the screening, diagnosis, and management of sarcopenia were developed by the International Conference on Sarcopenia and Frailty Research task force in 2018.32, The following recommendations were made:

• Screening for sarcopenia can be performed using gait speed analysis or SARC-F questionnaire.
• Individuals screened as positive for sarcopenia should be referred for further assessment to confirm the presence of the disease.
• DXA imaging should be used to determine low levels of lean body mass when diagnosing sarcopenia.

The recommendation regarding the diagnostic use of DXA received a conditional (weak) recommendation. The certainty of the evidence for DXA assessment was ranked low due to the following:

• DXA studies featuring populations from low to middle income countries are lacking.
• DXA measurement of lean body mass rather than muscle mass may potentially misclassify body composition in certain individuals.
• Incorporation of DXA measurements of lean body mass may have limited additional benefit for the prediction of relevant health outcomes (eg, falls, fractures, lowered physical performance, mobility).

U.S. Preventive Services Task Force Recommendations
No U.S. Preventive Services Task Force recommendations for whole-body DXA have been identified.

Ongoing and Unpublished Clinical Trials
Some currently unpublished trials that might influence this review are listed in Table 7.

Table 7. Summary of Key Trials

NCT No.	Trial Name	Planned Enrollment	Completion Date
Ongoing
NCT03621306	Precision and Reliability of Dual X-ray Absorptiometry (DXA) Testing	400	Aug 2028 (recruiting)

NCT: national clinical trial. 

References:

1. Murphy J, Bacon SL, Morais JA, et al. Intra-Abdominal Adipose Tissue Quantification by Alternative Versus Reference Methods: A Systematic Review and Meta-Analysis. Obesity (Silver Spring). Jul 2019; 27(7): 1115-1122. PMID 31131996
2. Sheean P, Gonzalez MC, Prado CM, et al. American Society for Parenteral and Enteral Nutrition Clinical Guidelines: The Validity of Body Composition Assessment in Clinical Populations. JPEN J Parenter Enteral Nutr. Jan 2020; 44(1): 12-43. PMID 31216070
3. Calella P, Valerio G, Brodlie M, et al. Tools and Methods Used for the Assessment of Body Composition in Patients With Cystic Fibrosis: A Systematic Review. Nutr Clin Pract. Oct 2019; 34(5): 701-714. PMID 30729571
4. Calella P, Valerio G, Brodlie M, et al. Cystic fibrosis, body composition, and health outcomes: a systematic review. Nutrition. Nov 2018; 55-56: 131-139. PMID 29981489
5. Bundred J, Kamarajah SK, Roberts KJ. Body composition assessment and sarcopenia in patients with pancreatic cancer: a systematic review and meta-analysis. HPB (Oxford). Dec 2019; 21(12): 1603-1612. PMID 31266698
6. Alves FD, Souza GC, Biolo A, et al. Comparison of two bioelectrical impedance devices and dual-energy X-ray absorptiometry to evaluate body composition in heart failure. J Hum Nutr Diet. Dec 2014; 27(6): 632-8. PMID 24684316
7. Ziai S, Coriati A, Chabot K, et al. Agreement of bioelectric impedance analysis and dual-energy X-ray absorptiometry for body composition evaluation in adults with cystic fibrosis. J Cyst Fibros. Sep 2014; 13(5): 585-8. PMID 24522087
8. Elkan AC, Engvall IL, Tengstrand B, et al. Malnutrition in women with rheumatoid arthritis is not revealed by clinical anthropometrical measurements or nutritional evaluation tools. Eur J Clin Nutr. Oct 2008; 62(10): 1239-47. PMID 17637600
9. Jensky-Squires NE, Dieli-Conwright CM, Rossuello A, et al. Validity and reliability of body composition analysers in children and adults. Br J Nutr. Oct 2008; 100(4): 859-65. PMID 18346304
10. Kullberg J, Brandberg J, Angelhed JE, et al. Whole-body adipose tissue analysis: comparison of MRI, CT and dual energy X-ray absorptiometry. Br J Radiol. Feb 2009; 82(974): 123-30. PMID 19168691
11. Liem ET, De Lucia Rolfe E, L'Abee C, et al. Measuring abdominal adiposity in 6 to 7-year-old children. Eur J Clin Nutr. Jul 2009; 63(7): 835-41. PMID 19127281
12. Bedogni G, Agosti F, De Col A, et al. Comparison of dual-energy X-ray absorptiometry, air displacement plethysmography and bioelectrical impedance analysis for the assessment of body composition in morbidly obese women. Eur J Clin Nutr. Nov 2013; 67(11): 1129-32. PMID 24022260
13. Monteiro PA, Antunes Bde M, Silveira LS, et al. Body composition variables as predictors of NAFLD by ultrasound in obese children and adolescents. BMC Pediatr. Jan 29 2014; 14: 25. PMID 24476029
14. Tompuri TT, Lakka TA, Hakulinen M, et al. Assessment of body composition by dual-energy X-ray absorptiometry, bioimpedance analysis and anthropometrics in children: the Physical Activity and Nutrition in Children study. Clin Physiol Funct Imaging. Jan 2015; 35(1): 21-33. PMID 24325400
15. Alves Junior CAS, de Lima LRA, de Souza MC, et al. Anthropometric measures associated with fat mass estimation in children and adolescents with HIV. Appl Physiol Nutr Metab. May 2019; 44(5): 493-498. PMID 30286302
16. Barr RD, Inglis D, Athale U, et al. The Influence of Body Composition on Bone Health in Long-term Survivors of Acute Lymphoblastic Leukemia in Childhood and Adolescence: Analyses by Dual-energy X-ray Absorptiometry and Peripheral Quantitative Computed Tomography. J Pediatr Hematol Oncol. Apr 27 2022. PMID 35482464
17. Chang CC, Chen YK, Chiu HC, et al. Assessment of Sarcopenia and Obesity in Patients with Myasthenia Gravis Using Dual-Energy X-ray Absorptiometry: A Cross-Sectional Study. J Pers Med. Nov 03 2021; 11(11). PMID 34834491
18. Smoot BJ, Mastick J, Shepherd J, et al. Use of Dual-Energy X-Ray Absorptiometry to Assess Soft Tissue Composition in Breast Cancer Survivors With and Without Lymphedema. Lymphat Res Biol. Nov 18 2021. PMID 34793255
19. Woolcott OO, Bergman RN. Defining cutoffs to diagnose obesity using the relative fat mass (RFM): Association with mortality in NHANES 1999-2014. Int J Obes (Lond). Jun 2020; 44(6): 1301-1310. PMID 31911664
20. Staunstrup LM, Nielsen HB, Pedersen BK, et al. Cancer risk in relation to body fat distribution, evaluated by DXA-scans, in postmenopausal women - the Prospective Epidemiological Risk Factor (PERF) study. Sci Rep. Mar 29 2019; 9(1): 5379. PMID 30926844
21. Reina D, Gomez-Vaquero C, Diaz-Torne C, et al. Assessment of nutritional status by dual X-Ray absorptiometry in women with rheumatoid arthritis: A case-control study. Medicine (Baltimore). Feb 2019; 98(6): e14361. PMID 30732168
22. Sinclair M, Hoermann R, Peterson A, et al. Use of Dual X-ray Absorptiometry in men with advanced cirrhosis to predict sarcopenia-associated mortality risk. Liver Int. Jun 2019; 39(6): 1089-1097. PMID 30746903
23. Lindqvist C, Brismar TB, Majeed A, et al. Assessment of muscle mass depletion in chronic liver disease: Dual-energy x-ray absorptiometry compared with computed tomography. Nutrition. May 2019; 61: 93-98. PMID 30703575
24. Dordevic AL, Bonham M, Ghasem-Zadeh A, et al. Reliability of Compartmental Body Composition Measures in Weight-Stable Adults Using GE iDXA: Implications for Research and Practice. Nutrients. Oct 12 2018; 10(10). PMID 30321991
25. Bazzocchi A, Ponti F, Cariani S, et al. Visceral fat and body composition changes in a female population after RYGBP: a two-year follow-up by DXA. Obes Surg. Mar 2015; 25(3): 443-51. PMID 25218013
26. Franzoni E, Ciccarese F, Di Pietro E, et al. Follow-up of bone mineral density and body composition in adolescents with restrictive anorexia nervosa: role of dual-energy X-ray absorptiometry. Eur J Clin Nutr. Feb 2014; 68(2): 247-52. PMID 24346474
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28. Ashby-Thompson M, Heshka S, Rizkalla B, et al. Validity of dual-energy x-ray absorptiometry for estimation of visceral adipose tissue and visceral adipose tissue change after surgery-induced weight loss in women with severe obesity. Obesity (Silver Spring). May 2022; 30(5): 1057-1065. PMID 35384351
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Coding Section

Codes	Number	Description
CPT	76499	Unlisted diagnostic radiographic procedure
ICD-10-CM		Investigational for all diagnoses
ICD-10-PCS		ICD-10-PCS codes are only used for inpatient services. There is no specific ICD-10-PCS code for this imaging.
	BW0KZZZ, BW0LZZZ	Imaging, anatomical regions, plain radiography, codes for whole body or whole skeleton
Type of service	Radiology
Place of service	Outpatient/inpatient

Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each policy. They may not be all-inclusive.

This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross Blue Shield Association technology assessment program (TEC) and other nonaffiliated technology evaluation centers, reference to federal regulations, other plan medical policies and accredited national guidelines.

"Current Procedural Terminology © American Medical Association. All Rights Reserved" 

History From 2013 Forward     

02/17/2023	Annual review, no change to policy intent. Updating rationale
02/01/2022	Annual review, no change to policy intent. Updating rationale and references.
02/03/2021	Annual review, no change to policy intent. Updating guidelines, coding, rationale and references.
02/11/2020	Annual review, no change to policy intent. Updating description, background, rationale and references.
02/13/2019	Annual review, no change to policy intent. Updating rationale and references.
02/21/2018	Annual review, no change to policy intent. Updating background, description, regulatory status, coding in guidelines and references.
02/06/2017	Annual review, no change to policy intent.
02/15/2016	Annual review, no change to policy intent. Updating background, description, rationale and references.
02/24/2015	Annual review, no change to policy intent. Updated background, description, rationale, references. Added guidelines, regulatory status and coding.
02/5/2014	Updated rationale and references. No change to policy intent.