0399 Upper Limb Prostheses - Aetna Better Health...Oct 11, 2019 · Myoelectric utilizes muscle...

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Upper Limb Prostheses

Clinical Policy Bulletins Medical Clinical Policy Bulletins

Number: 0399

*Please see amendment for Pennsylvania Medicaid at the end of this CPB.

Aetna considers the following prosthetic devices medically necessary when an artificial limb is

used to anatomically replace an absent or nonfunctioning body part with an artificial substitute:

I. Artificial arms (whole extremity or a portion thereof);

II. Artificial terminal devices (e.g., hand. hook, finger).

Aetna considers myoelectric upper limb prostheses and hand prostheses (e.g., the Dynamic

Mode Control hand, the i-LIMB, the Liberty Mutual Boston Elbow prosthetic device, the LTI

Boston Digital Arm System, the Ottobock bebionic hand, the OttoBock System Electrohand, and

the Utah Elbow System) medically necessary for members with traumatic amputation or

congenital absence of upper limb at the wrist or above (e.g., forearm or elbow) when the

following criteria are met:

Person has adequate cognitive and neurologic ability to utilize a myoelectric prosthetic

device; and The remaining musculature of the arm(s) contains the minimum microvolt threshold to

allow op eration of a myoelectric prosthetic device; and

A standard body-powered prosthetic device can not be used or is insufficient to meet

the functional needs of the person in performing activities of daily living; and

Absence of a comorbidity that cou ld interfere with m aintaining function of the

prosthesis (eg, neuromuscular disease).

Last Review

10/11/2019

Effective: 10/09/2000

Next

Review: 04/10/2020

Review

History

Definitions

Clinical Policy

Bulletin

Notes

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Aetna considers myoelectric upper limb and hand prostheses experimental and investigational

for all other indications because their effectiveness for indications other than the ones listed

above has not been established.

Aetna considers implantable myoelectric sensors for upper limb prostheses and hand prostheses

experimental and investigational because their effectiveness has not been established.

Aetna considers partial-hand myoelectric prostheses (e.g., ProDigits) experimental and

investigational because their effectiveness has not beenestablished.

Aetna considers transcranial direct current stimulation for enhancing performance of myoelectric

prostheses experimental and investigational because of insufficientevidence.

Aetna considers targeted muscle re-innervation for improved control of myoelectric upper limb

prostheses and treatment of painful post-amputation neuromas experimental and investigational

because its effectiveness has not been established.

Aetna considers the following medically necessary when used in conjunction with approved

prosthetic devices:

I. Supplies and accessories necessary for effective functioning of allowed equipment;or

II. Repairs or adjustments to medically necessary prosthetic devices that are required due

to bone growth or reasonable weight loss or reasonable weight gain and normal wear

and tear during normal usage of the device, or

III. Replacement of medically necessary prosthetic devices when repairs or adjustments fail

and/or are not possible.

Non-Medically Necessary Prostheses

Aetna considers the following not medically necessary:

Duplication or upgrade of a functional prosthesis; or

Prosthetic devices or prosthetic components that are primarily for cosmesis; or

Prosthetics used for activities other than normal daily living, including, but may not be

limited to, those utilized primarily for leisure or sporting activities such as skiing or

swimming; or

Repair or replacement of a prosthesis for appearance, comfort, convenience or

individual abuse, misuse or neglect; or

Repair or replacement of parts of a duplicate prosthesis; or



Water prosthesis (designed to be used for showering, swimming, etc.).

For myoelectric prostheses of the lower extremity see

CPB 0578 - Lower Limb Prostheses (../500_599/0578.html).

Notes: Most Aetna plans cover prosthetic devices that temporarily or permanently replace all or

part of an external body part that is lost or impaired as a result of disease, injury or congenital

defect. The surgical implantation or attachment of covered prosthetics is covered, regardless of

whether the covered prosthetic is functional (i.e., regardless of whether the prosthetic improves

or restores a bodily function).

Prosthetic devices must be ordered or provided by a physician or under the direction of a

physician.

Evaluation of the member, measurement and/or casting, and fitting/adjustments of the prosthesis

are included in the allowance for the prosthesis. There is no separate payment for these

services.

There is no separate payment if CAD-CAM technology is used to fabricate a prosthesis.

Reimbursement is included in the allowance of the codes for a prosthesis.

Powered base items are those that contain the power source (battery). At the time that a base

item is billed, all necessary batteries and/or battery chargers are considered as included in the

payment for the powered base item. There is no separate payment for batteries (L7360, L7364,

and L7367) and/or battery chargers (L7362, L7366, and L7368) billed concurrently with a

powered base item.

Myoelectric utilizes muscle activity from the residual limb for control of joint movement.

Electromyographic signals from the limb stump are detected by surface electrodes, amplified and

then processed by a controller to drive battery powered motors that move the hand, wrist and

elbow. These devices operate on rechargeable batteries and require no external cables or

harnesses.




The myoelectric hand prosthesis is an alternative to conventional hook prostheses for patients

with traumatic or congenital absence of forearm(s) and hand(s). The myoelectric prostheses are

user controlled by contraction of specific muscles triggering prosthesis movement through

electromyographic (EMG) signals. These prostheses have a stronger pinch force, better grip,

and are more flexible and easier to use than conventionalhooks..

Myoelectric control is used to operate electric motor-driven hands, wrist, and elbows. Surface

electrodes embedded in the prosthesis socket make contact with the skin and detect and amplify

muscle action potentials from voluntarily contracting muscle in the residual limb. The amplified

electrical signal turns on an electric motor to provide a function (e.g., terminal device operation,

wrist rotation, elbow flexion). The newest electronic control systems perform multiple functions,

and allow for sequential operation of elbow motion, wrist rotation and hand motions.

Myoelectric hand prostheses provide improved function and range of functional position as

compared to “hook” prostheses. Myoelectrical hand prostheses can be used for patients with

congenital limb deficiencies and for patients with amputations sustained as a result of trauma or

surgery. The device is appropriate for both above-the-elbow and below-the-elbow amputees,

and for both unilateral and bilateral amputees. Patients must possess a minimum microvolt

threshold (i.e., minimum strength of microvolt signals emitting from the remaining musculature of

the arm) and pass a control test to be considered acandidate.

Myoelectrical hand prostheses are indicated for persons at least 1 year of age or older. Children

with congenital absence of the forearm(s) and hand(s) are usually fitted with a conventional

passive prosthesis until approximately age 12 to 16 months, at which time they may be fitted with

a myoelectrical prosthesis.

Myoelectrical hand prostheses generally come with a 1-year warranty for parts and labor. The

motor and drive mechanisms typically last 2 to 3 years and may need to be replaced after this

period. When used on a child, the sockets may need to be replaced every 12 to 18 months due

to growth. With heavy use the entire prosthesis might require replacement by the 5th year.

The Work Loss Data Institute's clinical guideline on "Shoulder (acute & chronic)" (2011) listed

myoelectric upper extremity (hand and/or arm) prosthesis as one of the interventions/procedures

that were considered and recommended.

Ostlie and colleagues (2012) described patterns of prosthesis wear, perceived prosthetic

usefulness, as well as the actual use of prostheses in the performance of activities of daily life

(ADL) tasks in adult acquired upper-limb amputees (ULAs). Cross-sectional study analyzing

population-based questionnaire data (n = 224) and data from interviews and clinical testing in a



referred/convenience sample of prosthesis-wearing ULAs (n = 50). Effects were analyzed using

linear regression; 80.8 % wore prostheses and 90.3 % reported their most worn prosthesis as

useful. Prosthetic usefulness profiles varied with prosthetic type. Despite demonstrating good

prosthetic skills, the amputees reported actual prosthesis use in only about 50 % of the ADL

tasks performed in everyday life. In unilateral amputees, increased actual use was associated

with sufficient prosthetic training and with the use of myoelectric versus cosmetic prostheses,

regardless of amputation level. Prosthetic skills did not affect actual prosthesis use. No

background factors showed significant effect on prosthetic skills. The authors concluded that

most major ULAs wear prostheses. They stated that individualized prosthetic training and fitting

of myoelectric rather than passive prostheses may increase actual prosthesis use in ADL.

There are many brands of myoelectric hand prostheses on the market. Brands of myoelectrical

hand prostheses include the Otto Bock myoelectrical prosthesis (Otto Bock, Minneapolis, MN),

the Liberty Mutual Boston Elbow prosthetic device (Liberty Mutual, Boston, MA), and the Utah

Elbow System (Motion Control, Salt Lake City, UT).

Partial-hand myoelectric prostheses are designed to replace the function of digits in individuals

missing 1 or more fingers as a result of a partial-hand amputation. This type of prosthetic device

requires a very specific range of amputation, i.e., amputation level through, or just proximal to,

the metacarpal-phalangeal level of 1 or more digits.

Putzi (1992) reported the case of a young man who had 2 traumatic amputations and burns

covering 80 % of his body. Due to his severe burns, fitting a conventional prosthesis was a

problem because normal procedures did not apply in his case. The patient was fitted with a

myoelectric partial-hand prosthesis. The author concluded that this reconstruction of the

myoelectric prosthesis was a satisfactory solution in providing the patient with as much hand and

arm mobility as possible in light of his condition. By using basic principles of orthotics and

prosthetics, and exercising ingenuity in using existing proven components, it is possible to

provide improvement in function and cosmetics to an individual with a partial-hand amputation.

Lake (2009) provided a review of progressive partial-hand prosthetic management. The author

noted that partial-hand prosthetic management represents an exciting new frontier in the

specialty of upper limb prosthetics. The application and benefit of treating this level are

apparent. Presently, this level is very difficult because of the vast surgical presentations,

traumatic nature of the resultant limb difference, as well as the complicated biomechanics

present as a result of the afore-mentioned 2 issues. Lake (2009) noted that electric prosthetic

management requires specialized care that does not have its foundation rooted in any of the

current, yet progressive upper limb care protocols used by today's specialists. Future research

will entail electronic handling, fabrication, fitting protocols and techniques, as well as surgical



considerations. As fitting techniques and componentry evolve, so will the clinical protocols. The

author stated that an unique opportunity exists at the partial-hand level as this specialty enters a

new prosthetic paradigm where evidence-based rehabilitation and sound research practices are

expected by both the medical community as well as reimbursement agencies.

Currently, there is insufficient peer-reviewed evidence that examined the clinical value (e.g.,

improved function and health-related quality of life) of partial-hand myoelectric prostheses.

Dutta et al (2014) noted that functional electrical stimulation (FES) can electrically activate

paretic muscles to assist movement for post-stroke neurorehabilitation. Here, sensory-motor

integration may be facilitated by triggering FES with residual EMG activity. However, muscle

activity following stroke often suffers from delays in initiation and termination which may be

alleviated with an adjuvant treatment at the central nervous system (CNS) level with transcranial

direct current stimulation (tDCS) thereby facilitating re-learning and retaining of normative

muscle activation patterns. This study on 12 healthy volunteers was conducted to investigate

the effects of anodal tDCS of the primary motor cortex (M1) and cerebellum on latencies during

isometric contraction of tibialis anterior (TA) muscle for myoelectric visual pursuit with quick

initiation/termination of muscle activation, i.e., “ballistic EMG control” as well as modulation of

EMG for “proportional EMG control”. The normalized delay in initiation and termination of

muscle activity during post-intervention “ballistic EMG control” trials showed a significant main

effect of the anodal tDCS target: cerebellar, M1, sham (F(2) = 2.33, p < 0.1), and interaction

effect between tDCS target and step-response type: initiation/termination of muscle activation

(F(2) = 62.75, p < 0.001), but no significant effect for the step-response type (F(1) = 0.03, p =

0.87). The post-intervention population marginal means during “ballistic EMG control” showed 2

important findings at 95 % confidence interval (CI [critical values from Scheffe's S procedure]): (i)

Offline cerebellar anodal tDCS increased the delay in initiation of TA contraction while M1

anodal tDCS decreased the same when compared to sham tDCS; and (ii) Offline M1 anodal

tDCS increased the delay in termination of TA contraction when compared to cerebellar

anodal tDCS or sham tDCS. Moreover, online cerebellar anodal tDCS decreased the learning

rate during “proportional EMG control” when compared to M1 anodal and sham tDCS. The

authors concluded that these preliminary findings from healthy subjects showed specific, and at

least partially antagonistic effects, of M1 and cerebellar anodal tDCS on motor performance

during myoelectric control. They stated that these results are encouraging, but further studies

are needed to better define how tDCS over particular regions of the cerebellum may facilitate

learning of myoelectric control for brain machine interfaces.



Pan et al (2015) stated that most prosthetic myoelectric control studies have shown good

performance for unimpaired subjects. However, performance is generally unacceptable for

amputees. The primary problem is the poor quality of EMG signals of amputees compared with

healthy individuals. To improve clinical performance of myoelectric control, these researchers

explored tDCS to modulate brain activity and enhance EMG quality. These investigators tested 6

unilateral transradial amputees by applying active and sham anodal tDCS separately on 2

different days. Surface EMG signals were acquired from the affected and intact sides for eleven

hand and wrist motions in the pre-tDCS and post-tDCS sessions. Auto-regression (AR)

coefficients and linear discriminant analysis (LDA) classifiers were used to process the EMG

data for pattern recognition of the 11 motions. For the affected side, active anodal tDCS

significantly reduced the average classification error rate (CER) by 10.1 %, while sham tDCS

had no such effect. For the intact side, the average CER did not change on the day of sham

tDCS but increased on the day of active tDCS. The authors concluded that these findings

demonstrated that tDCS could modulate brain function and improve EMG-based classification

performance for amputees. They stated that iIt has great potential in dramatically reducing the

length of learning process of amputees for effectively using myoelectrically-controlled multi-

functional prostheses.

Implantable Myoelectric Sensors

Pasquina and colleagues (2015) stated that advanced motorized prosthetic devices are currently

controlled by EMG signals generated by residual muscles and recorded by surface electrodes on

the skin. These surface recordings are often inconsistent and unreliable, leading to high

prosthetic abandonment rates for individuals with upper limb amputation. Surface electrodes are

limited because of poor skin contact, socket rotation, residual limb sweating, and their ability to

only record signals from superficial muscles, whose function frequently does not relate to the

intended prosthetic function. More sophisticated prosthetic devices require a stable and reliable

interface between the user and robotic hand to improve upper limb prosthetic function.

Implantable Myoelectric Sensors (IMES) are small electrodes intended to detect and wirelessly

transmit EMG signals to an electro-mechanical prosthetic hand via an electro-magnetic coil built

into the prosthetic socket. This system is designed to simultaneously capture EMG signals from

multiple residual limb muscles, allowing the natural control of multiple degrees of freedom

simultaneously. In a case report, these investigators reported the status of the first Food and

Drug Administration (FDA)-approved clinical trial of the IMES System. This study is currently in

progress, limiting reporting to only preliminary results. The first subject has reported the ability to

accomplish a greater variety and complexity of tasks in his everyday life compared to what could

be achieved with his previous myoelectric prosthesis. The authors concluded that the interim



results of this study indicated the feasibility of utilizing IMES technology to reliably sense and

wirelessly transmit EMG signals from residual muscles to intuitively control a 3 degree-of-

freedom prosthetic arm.

Bergmeister et al (2016) noted that myoelectric prostheses lack a strong human-machine

interface, leading to high abandonment rates in upper limb amputees. Implantable wireless

EMG systems improve control by recording signals directly from muscle, compared with surface

EMG. These devices do not exist for high amputation levels. These researchers presented an

implantable wireless EMG system for these scenarios tested in Merino sheep for 4 months. In a

pilot trial, the electrodes were implanted in the hind limbs of 24 Sprague-Dawley rats. After 8 or

12 weeks, impedance and histocompatibility were assessed. In the main trial, the system was

tested in 4 Merino sheep for 4 months. Impedance of the electrodes was analyzed in 2 animals;

EMG data were analyzed in 2 freely moving animals repeatedly during forward and backward

gait. Device implantation was successful in all 28 animals. Histologic evaluation showed a tight

encapsulation after 8 weeks of 78.2 ± 26.5 µm subcutaneously and 92.9 ± 31.3 µm on the

muscular side. Electromyographic recordings showed a distinct activation pattern of the triceps,

brachialis, and latissimus dorsi muscles, with a low signal-to-noise ratio, representing specific

patterns of agonist and antagonist activation. Average electrode impedance decreased over the

whole frequency range, indicating an improved electrode-tissue interface during the implantation.

All measurements taken over the 4 months of observation used identical settings and showed

similar recordings despite changing environmental factors. The authors concluded that the

findings of this study showed the implantation of this EMG device as a promising alternative to

surface EMG, providing a potentially powerful wireless interface for high-level amputees.

Partial-Hand Myoelectric Prostheses

Earley et al (2016) stated that although partial-hand amputees largely retain the ability to use

their wrist, it is difficult to preserve wrist motion while using a myoelectric partial-hand prosthesis

without severely impacting control performance. Electromyogram (EMG) pattern recognition is a

well-studied control method; however, EMG from wrist motion can obscure myoelectric finger

control signals. Thus, to accommodate wrist motion and to provide high classification accuracy

and minimize system latency, these researchers developed a training protocol and a classifier

that switches between long and short EMG analysis window lengths. A total of 17 non-amputee

and 2 partial-hand amputee subjects participated in a study to determine the effects of including

EMG from different arm and hand locations during static and/or dynamic wrist motion in the

classifier training data. They evaluated several real-time classification techniques to determine

which control scheme yielded the highest performance in virtual real-time tasks using a 3-way

ANOVA. These investigators found significant interaction between analysis window length and

the number of grasps available. Including static and dynamic wrist motion and intrinsic hand



muscle EMG with extrinsic muscle EMG significantly reduced pattern recognition classification

error by 35 %. Classification delay or majority voting techniques significantly improved real-time

task completion rates (17 %), selection (23 %), and completion (11 %) times, and selection

attempts (15 %) for non-amputee subjects, and the dual window classifier significantly reduced

the time (8 %) and average number of attempts required to complete grasp selections (14 %)

made in various wrist positions. Amputee subjects demonstrated improved task timeout rates,

and made fewer grasp selection attempts, with classification delay or majority voting techniques.

Thus, the authors concluded that the proposed techniques showed promise for improving control

of partial-hand prostheses and more effectively restoring function to individuals using these

devices.

Adewuy et al (2016) noted that pattern recognition-based myoelectric control of upper-limb

prostheses has the potential to restore control of multiple degrees of freedom. Though this

control method has been extensively studied in individuals with higher-level amputations, few

studies have investigated its effectiveness for individuals with partial-hand amputations. Most

partial-hand amputees retain a functional wrist and the ability of pattern recognition-based

methods to correctly classify hand motions from different wrist positions is not well studied. In

this study, focusing on partial-hand amputees, these researchers evaluated (i) the performance

of non-linear and linear pattern recognition algorithms, and (ii) the performance of optimal

EMG feature subsets for classification of 4 hand motion classes in different wrist positions

for 16 non-amputees and 4 amputees. The results showed that linear discriminant analysis

and linear and non-linear artificial neural networks performed significantly better than the

quadratic discriminant analysis for both non-amputees and partial-hand amputees. For

amputees, including information from multiple wrist positions significantly decreased error (p < 0.001)

but no further significant decrease in error occurred when more than 4, 2, or 3 positions

were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic

muscle EMG (p = 0.08), respectively. Finally, the authors found that a feature set determined by

selecting optimal features from each channel outperformed the commonly used time domain

(TD) (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be

used as a screening filter to select the features from each channel that provide the best

classification of hand postures across different wrist positions. They stated that these findings

suggested that some of the widely used TD features were better suited for use with intrinsic

muscle EMG data than extrinsic muscle data for good control across multiple wrist positions.

Moreover, they noted that further analysis of data from amputees completing tasks with the wrist

in different positions in a virtual environment or with a physical prosthesis is needed.



Adewuy et al (2017) stated that that the use of pattern recognition-based methods to control

myoelectric upper-limb prostheses has been well studied in individuals with high-level

amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who

often possess a functional wrist. These investigators evaluated strategies that allow partial-hand

amputees to control a prosthetic hand while allowing retain wrist function. EMG data were

recorded from the extrinsic and intrinsic hand muscles of 6 non-amputees and 2 partial-hand

amputees while they performed 4 hand motions in 13 different wrist positions. The performance

of 4 classification schemes using EMG data alone and EMG data combined with wrist positional

information was evaluated. Using recorded wrist positional data, the relationship between EMG

features and wrist position was modeled and used to develop a wrist position-independent

classification scheme. A multi-layer perceptron artificial neural network classifier was better able

to discriminate 4 hand motion classes in 13 wrist positions than a linear discriminant analysis

classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear

perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to

EMG data significantly improved performance (p < 0.001). Training the classifier with the

combination of extrinsic and intrinsic muscle EMG data performed significantly better than using

intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic

muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001).

The same trends were observed for amputees, except training with intrinsic muscle

EMG data, on average, performed worse than the extrinsic muscle EMG data. These

researchers proposed a wrist position-independent controller that simulated data from multiple

wrist positions and was able to significantly improve performance by 48 to 74 % (p < 0.05) for

non-amputees and by 45 to 66 % for partial-hand amputees, compared to a classifier trained

only with data from a neutral wrist position and tested with data from multiple positions. The

authors concluded that sensor fusion (using EMG and wrist position information), non-linear

artificial neural networks, combining EMG data across multiple muscle sources, and simulating

data from different wrist positions were effective strategies for mitigating the wrist position effect

and improving classification performance.

The authors stated that these results were limited in that the training and testing data sets were

from the same day and experimental session. Although pattern recognition control deteriorated

when classifiers were trained and tested with data collected from different days or sessions, a

recent study has shown that between-day performance improved and approached within-day

performance when subjects performed contractions over 11 consecutive days. These results

implied that subjects were better able to make more consistent contractions when training over

multiple days. It was thus possible that the mapping between EMG features and wrist position

would be stable if subjects were trained over multiple days. They stated that further multi-day

experiments are needed to determine if the neural network maintains its performance across

sessions. One important consideration regarding the neural network regression model was that



these researchers assumed each feature was independent and thus the change in feature as a

function of wrist position was predicted separately for each feature. Consequently these

researchers lost any some mutual information across the features. Even with this loss of

information, the performance using the model-generated data particularly with intrinsic and

extrinsic muscles performed just as well as the real data set, implying that the issue was not

critical. Perhaps this was because there were enough data from enough features to overcome

this. It was possible however, that preserving the relation and co-variability between features

would better allow the model-generated data to more accurately predict the feature changes and

improve performance. Another potential drawback was that the analyses were performed off-line

and with only 4 hand motion classes (2 grasps, hand open and no movement). The authors

expected classification error to increase when more hand grasps were available to the classifier

though future work is needed to evaluate the extent to which wrist position information improves

error and to determine if the performance of the simulated dataset generalize to more grasps.

The relationship between off-line error and real-time performance is unclear. Some previous

research had demonstrated a minimal correlation between off-line performance and usability with

a virtual task; however other studies have shown significant correlation between off-line

classification error and real-time control. These researchers stated that although the findings of

this study were promising, further real-time experiments in a virtual environment or with a

physical prosthesis are needed.

Targeted Muscle Re-Innervation

Kuiken and co-workers (2017) stated that myoelectric devices are controlled by EMG signals

generated by contraction of residual muscles, which thus serve as biological amplifiers of neural

control signals. Although nerves severed by amputation continue to carry motor control

information intended for the missing limb, loss of muscle effectors due to amputation prevents

access to this important control information. Targeted muscle re-innervation (TMR) was

developed as a novel strategy to improve control of myoelectric upper limb prostheses. Severed

motor nerves are surgically transferred to the motor points of denervated target muscles, which,

after re-innervation, contract in response to neural control signals for the missing limb; TMR

creates additional control sites, eliminating the need to switch the prosthesis between different

control modes. In addition, contraction of target muscles, and operation of the prosthesis, occurs

in response to attempts to move the missing limb, making control easier and more intuitive. The

authors concluded that TMR has been performed extensively in individuals with high-level upper

limb amputations and has been shown to improve functional prosthesis control. The benefits of

TMR are being studied in individuals with trans-radial amputations and lower limb amputations;

TMR is also being investigated in an ongoing clinical trial as a method to prevent or treat painful

amputation neuromas.



Vadala and colleagues (2017) stated that TMR is a novel surgical technique developed to

improve the control of myoelectric upper limb prostheses. Nerves transected by the amputation,

which retain their original motor pathways even after being severed, are re-directed to residual

denervated muscles that serve as target for consequent re-innervation. Once the process is

complete, re-innervated muscles will contract upon voluntary activation of transferred nerves

while attempting to move missing regions of the amputated limb, generating EMG signals that

can be recorded and used to control a prosthetic device. This allows creating new control sites

that can overcome major drawbacks of conventional myoelectric prostheses by offering a more

natural and intuitive control of prosthetic arms. These researchers noted that TMR has been

widely performed in individuals who underwent shoulder disarticulation amputation and trans-

humeral amputation since proximal amputations do not leave enough functional muscles

exploitable to control independent degree of freedoms of multi-articulated prostheses. The

authors concluded that TMR application is currently under investigation in patients suffering

further distal amputations, as well as for treating and preventing painful post-amputation

neuromas.

Bowen and associates (2017) noted that there are approximately 185,000 amputations each

year and nearly 2 million amputees currently living in the United States. About 25 % of these

amputees will experience chronic pain issues secondary to localized neuroma pain and/or

phantom limb pain. The significant discomfort caused by neuroma and phantom limb pain

interferes with prosthesis wear, subjecting amputees to the additional physical and psychological

morbidity associated with chronic immobility. Although numerous neuroma treatments are

described, none of these methods is consistently effective in eliminating symptoms. Targeted

muscle re-innervation is a surgical technique involving the transfer of residual peripheral nerves

to redundant target muscle motor nerves, restoring physiological continuity and encouraging

organized nerve regeneration to decrease and potentially prevent the chaotic and mis-directed

nerve growth, which can contribute to pain experienced within the residual limb. These

researchers stated that TMR represents one of the more promising treatments for neuroma pain.

Prior research into "secondary" TMR performed in a delayed manner after amputation has

shown great improvement in treating amputee pain issues because of peripheral nerve

dysfunction. "Primary" TMR performed at the time of amputation suggested that it may prevent

neuroma formation while avoiding the risks associated with a delayed procedure. In addition,

TMR allows the target muscles to act as bio-amplifiers to direct bioprosthetic control and

function. The authors concluded that TMR has the potential to treat pain from neuromas while

enabling amputee patients to return to their ADL and improve prosthetic use and tolerance.

They stated that recent research in the areas of secondary (i.e., delayed) and primary TMR aims

to optimize efficacy and efficiency and demonstrated great potential for establishing a new

standard of care for amputees.



Moreover, these investigators stated that if successful, primary TMR will reduce the total number

of surgeries, thus eliminating recovery time and other risks associated with additional operations.

It is their hope that prevention of neuroma and phantom limb pain (NPLP) symptoms will lead to

earlier, more consistent, and comfortable prosthesis use and improved health outcomes overall.

The results of primary TMR will continue to be examined through close patient follow-up to

determine its long-term effects on NPLP prevention.

CPT Codes / HCPCS Codes / ICD-10 Codes

Information in the [brackets] below has been added for clarification purposes. Codes requiring a 7th character are represented by "+":

Code Code D escription

CPT codes not covered for indications listed in the CPB:

- no specific code:

Other CPT codes related to the CPB:

24900 -24935,

25900 -25931,

26910 - 29652

Surgical amputation, upper extremity

HCPCS codes covered if selection criteria are met:

L6000 Partial hand, thumb remaining

L6010 Partial hand, little and/or ring finger remaining

L6020 Partial hand, no finger remaining

L6050 Wrist disarticulation, molded socket, flexible elbow hinges, triceps pad

L6055 Wrist disarticulation, molded socket with expandable interface, flexible elbow hinges,

triceps pad

L6100 Below elbow, molded socket, flexible elbow hinge, triceps pad

L6110 Below elbow, molded socket, (muenster or northwestern suspension types)

L6120 Below elbow, molded double wall split socket, step-up hinges, half cuff

L6130 Below elbow, molded double wall split socket, stump activated locking hinge, half

cuff

L6200 Elbow disarticulation, molded socket, outside locking hinge, forearm

L6205 Elbow disarticulation, molded socket with expandable interface, outside locking

hinges, forearm

L6250 Above elbow, molded double wall socket, internal locking elbow, forearm

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Code Code Description

L6300 Shoulder disarticulation, molded socket, shoulder bulkhead, humeral section,

internal locking elbow, forearm

L6310 Shoulder disarticulation, passive restoration (complete prosthesis)

L6320 Shoulder disarticulation, passive restoration (shoulder cap only)

L6629 Upper extremity addition, quick disconnect lamination collar with coupling piece,

Otto Bock or equal

L6632 Upper extremity addition, latex suspension sleeve, each

L6680 Upper extremity addition, test socket, wrist disarticulation or below elbow

L6687 Upper extremity addition, frame type socket, below elbow or wrist disarticulation

L6703 Terminal device, passive hand/mitt, any material, any size

L6704 Terminal device, sport/recreational/work attachment, any material, any size

L6706 Terminal device, hook, mechanical, voluntary opening, any material, any size, lined

or unlined

L6707 Terminal device, hook, mechanical, voluntary closing, any material, any size, lined or

unlined

L6708 Terminal device, hand, mechanical, voluntary opening, any material, any size

L6709 Terminal device, hand, mechanical, voluntary closing, any material, any size

L6711 Terminal device, hook, mechanical, voluntary opening, any material, any size, lined

or unlined, pediatric

L6712 Terminal device, hook, mechanical, voluntary closing, any material, any size, lined or

unlined, pediatric

L6713 Terminal device, hand, mechanical, voluntary opening, any material, any size,

pediatric

L6714 Terminal device, hand, mechanical, voluntary closing, any material, any size,

pediatric

L6715 Terminal device, multiple articulating digit, includes motor(s), initial issue or

replacement

L6721 Terminal device, hook or hand, heavy duty, mechanical, voluntary opening, any

material, any size, lined or unlined

L6722 Terminal device, hook or hand, heavy duty, mechanical, voluntary closing, any

material, any size, lined or unlined

L6810 Addition to terminal device, precision pinch device




L6880 Electric hand, switch or myoelectric controlled, independently articulating digits, any

grasp pattern or combination of grasp patterns, includes motor(s)

L6882 Microprocessor control feature, addition to upper limb prosthetic terminal device

L6890 Addition to upper extremity prosthesis, glove for terminal device, any material,

prefabricated, includes fitting and adjustment

L6925 Wrist disarticulation, external power, self-suspended inner socket, removable

forearm shell, Otto Bock or equal electrodes, cables, two batteries and one charger,

myoelectronic control of terminal device

L6935 Below elbow, external power, self-suspended inner socket, removable forearm shell,

Otto Bock or equal electrodes, cables, two batteries and one charger, myoelectronic

control of terminal device

L6945 Elbow disarticulation, external power, molded inner socket, removable humeral shell,

outside locking hinges, forearm, Otto Bock or equal electrodes, cables, two batteries

and one charger, myoelectronic control of terminal device

L6955 Above elbow, external power, molded inner socket, removable humeral shell,

internal locking elbow, forearm, Otto Bock or equal electrodes, cables, two batteries

and one charger, myoelectronic control of terminal device

L6965 Shoulder disarticulation, external power, molded inner socket, removable shoulder

shell, shoulder bulkhead, humeral section, mechanical elbow, forearm, Otto Bock or

equal electrodes, cables, two batteries and one charger, myoelectronic control of

terminal device

L6975 Interscapular-thoracic, external power, molded inner socket, removable shoulder

shell, shoulder bulkhead, humeral section, mechanical elbow, forearm, Otto Bock or

equal electrodes, cables, two batteries and one charger, myoelectronic control of

terminal device

L7007 - L7008 Electric hand, switch or myoelectric controlled, adult or pediatric

L7009, L7045 Electric hook, switch or myoelectric controlled, adult or pediatric

L7190 - L7191 Electronic elbow, variety village or equal, myoelectronically controlled, adolescent or

child

L7259 Electronic wrist rotator, any type

L7368 Lithium ion battery charger

L7400 Addition to upper extremity prosthesis, below elbow/wrist disarticulation, ultralight

material (titanium, carbon fiber or equal)




L7403 Addition to upper extremity prosthesis, below elbow/wrist disarticulation, acrylic

material

L8465 Prosthetic shrinker, upper limb, each

HCPCS codes not covered for indications listed in the CPB:

Implantable myoelectric sensors for upper limb prostheses and hand prostheses, water prosthesis:

No specific code

L6026 Transcarpal/metacarpal or partial hand disarticulation prosthesis, external power,

self-suspended, inner socket with removable forearm section, electrodes and

cables, two batteries, charger, myoelectric control of terminal device, excludes

terminal device(s)

Other HCPCS codes related to the CPB:

L7360 Six volt battery, each

L7362 Battery charger, six volt, each

L7364 Twelve volt battery, each

L7366 Battery charger, twelve volt, each

L7367 Lithium ion battery, rechargeable, replacement

L7368 Lithium ion battery charger, replacement only

ICD-10 codes covered if selection criteria are met:

Q71.00 - Q71.53

Q71.811 - Q71.93

Reduction defects of upper limb

S48.011+ -

S48.929+

Traumatic amputation of shoulder and upper arm

S58.011+ -

S58.929+

Traumatic amputation of elbow and forearm

S68.411+ -

S68.429+,

S68.711+ -

S68.729+

Traumatic amputation of hand




S48.911+,

S48.921+,

S58.911+, S58.921+

[S48.912+,

S48.922+,

S58.912+,

S58.922+ also

required]

Traumatic amputation of shoulder and upper arm and forearm, level unspecified

(complete) (partial), bilateral

Z89.011 - Z89.239 Acquired absence of upper limb

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

G00 - G99 Diseases of nervous system [neuromuscular disease that interferes with prosthesis

function]

T87.31 Neuroma of amputation stump, right upper extremity

T87.32 Neuroma of amputation stump, left upper extremity

1. Nader M. The artificial substitution of missing hands with myoelectrical prostheses.

Clin Orthop. 1990;(258):9-17.

2. Silcox DH, Rooks MD, Vogel RR, et al. Myoelectric prostheses. A long-term follow-up

and a study of the use o f alternative prostheses. J Bone Joint Surg Am.

1993;75(12):1781-1789.

3. Weaver SA, Lange LR, Vogts VM. Comparison of myoelectric and conventional

prostheses for adolescent amputees. Am J Occup Ther. 1988;42(2):87-91.

4. Scott RN, Parker PA. Myoelectric prostheses: State of the art. J Med Eng Technol.

1988;12(4):143-151.

5. Kritter AE. Myoelectric prostheses. J Bone Joint Surg Am. 1985;67(4):654-657.

6. Stein RB, Walley M. Functional comparison of upper extremity amputees using

myoelectric and conventional prostheses. Arch Phys Med Rehabil. 1983;64(6):243-248.

7. Leonard JA, Meier RH. Upper and lower extremity prosthetics. In: Rehabilitation

Medicine: Principles and Practice. 2nd ed. JA DeLisa, ed. Philadelphia, PA: J.B. Lippincott

Co.; 1993:507, 514-515.

8. Otto Bock, Inc. Myoelectrical prostheses. Minneapolis, MN: Otto Bock; 1999. Available

at: http://www.ottobockus.com/. Accessed June 11, 2001.



http://www.ottobockus.com/

9. Motion Control, Inc. The Utah Arm. Salt Lake City, UT: Motion Control; 1999. Available

at: http://www.utaharm.com/. Accessed June 11, 2001.

10. Routhier F, Vincent C, Morissette MJ, et al. Clinical results of an investigation of

paediatric upper limb myoelectric prosthesis fitting at the Quebec Rehabilitation

Institute. Prosthet Orthot Int. 2001;25(2):119-131.

11. Esquenazi A. Amputation rehabilitation and prosthetic restoration. From surgery to

community reintegration. Disabil Rehabil. 2004;26(14-15):831-836.

12. Hsu MJ, Nielsen DH, Lin-Chan SJ, Shurr D. The effects of prosthetic foot design on

physiologic measurements, self-selected walking velocity, and physical activity in

people with transtibial amputation. Arch Phys Med Rehabil. 2006;87(1):123-129.

13. Butter M, Rensma A, van Boxsel J, et al. Robotics for Healthcare. Final Report. Version 5.

Prepared by TNO for the European Commission, DG Information Society. Delft, The

Netherlands: Netherlands Organization for Applied Scientific Research (TNO); October

3, 2008.

14. Egermann M, Kasten P, Thomsen M. Myoelectric hand prostheses in very young

children. Int Orthop. 2009;33(4):1101-1105.

15. Kelly BM, Pangilnan PH Jr, Rodriguez GM, et al. Upper limb prosthetics. eMedicine

Physical Medicine and Rehabilitation. New York, NY: Medscape; January 14, 2009.

16. Castellini C, Fiorilla AE, Sandini G. Multi-subject/daily-life activity EMG-based control of

mechanical hands. J Neuroeng Rehabil. 2009;6:41.

17. Otr OV, Reinders-Messelink HA, Bongers RM, et al. The i-LIMB hand and the DMC plus

hand compared: A case report. Prosthet Orthot Int. 2010;34(2):216-220.

18. Putzi R. Myoelectric partial-hand prosthesis. J Prosthet Orthot. 1992;4(2):103-108.

19. Lake C. Experience with electric prostheses for the partial hand presentation: An eight-

year retrospective. J Prosthet Orthot. 2009;21(2):125-130.

20. Work Loss Data Institute. Shoulder (acute & chronic). Encinitas, CA: Work Loss Data

Institute; 2011.

21. Ostlie K, Lesjo IM, Franklin RJ, et al. Prosthesis use in adult acquired major upper-limb

amputees: Patterns of wear, prosthetic skills and the actual use of prostheses in

activities of daily life. Disabil Rehabil Assist Technol. 2012;7(6):479-493.

22. Dutta A, Paulus W, Nitsche MA. Facilitating myoelectric-control with transcranial direct

current stimulation: A preliminary study in healthy humans. J Neuroeng Rehabil.

2014;11:13.

23. Pan L, Zhang D, Sheng X, Zhu X. Improving myoelectric control for amputees through

transcranial direct current stimulation. IEEE Trans Biomed Eng. 2015;62(8):1927-1936.

24. Carey SL, Lura DJ, Highsmith MJ, et al. Differences in m yoelectric and body-powered

upper-limb prostheses: Systematic literature review. J Rehabil Res Dev. 2015;52(3):247-

262.



http://www.utaharm.com/

25. Pasquina PF, Evangelista M, Carvalho AJ, et al. First-in-man demonstration of a fully

implanted myoelectric sensors system to control an advanced electromechanical

prosthetic hand. J Neurosci Methods. 2015;244:85-93.

26. Bergmeister KD1, Hader M, Lewis S, et al. Prosthesis control with an implantable

multichannel wireless electromyography system for high-level amputees: A large-

animal study. Plast Reconstr Surg. 2016;137(1):153-162.

27. Earley EJ, Hargrove LJ, Kuiken TA. Dual window pattern recognition classifier for

improved partial-hand prosthesis control. Front Neurosci. 2016;10:58.

28. Adewuyi AA, Hargrove LJ, Kuiken TA. Evaluating EMG feature and classifier selection for

application to partial-hand prosthesis control. Front Neurorobot. 2016;10:15.

29. Adewuyi AA, Hargrove LJ, Kuiken TA. Resolving the effect of wrist position on

myoelectric pattern recognition control. J Neuroeng Rehabil. 2017;14(1):39.

30. Kuiken TA, Barlow AK, Hargrove L, Dumanian GA. Targeted muscle reinnervation for

the upper and lower extremity. Tech Orthop.2017;32(2):109-116.

31. Vadala G, Di Pino G, Ambrosio L, et al. Targeted muscle reinnervation for improved

control of myoelectric upper limb prostheses. J Biol Regul Homeost Agents. 2017;31(4

suppl 1):183-189.

32. Bowen JB, Wee CE, Kalik J, Valerio IL. Targeted muscle reinnervation to improve pain,

prosthetic tolerance, and bioprosthetic outcomes in the amputee. Adv Wound Care

(New Rochelle). 2017;6(8):261-267.

33. Centers for Medicare & Medicaid Services (CMS). Leg, Arm, Back, and Neck Braces,

Trusses, and Artificial Legs, Arms, and Eyes. Medicare Benefit Policy Manual, Chapter

15, Section 130. Baltimore, MD: CMS; revised October 1,2003.



Copyright Aetna Inc. All rights reserved. Clinical Policy Bulletins are developed by Aetna to assist in administering plan benefits and

constitute neither offers of coverage nor medical advice. This Clinical Policy Bulletin contains only a partial, general description of plan or

program benefits and does not constitute a contract. Aetna does not provide health care services and, therefore, cannot guarantee any

results or outcomes. Participating providers are independent contractors in private practice and are neither employees nor agents of Aetna

or its affiliates. Treating providers are solely responsible for medical advice and treatment of members. This Clinical Policy Bulletin may be

updated and therefore is subject to change.

Copyright © 2001-2019 Aetna Inc.



AETNA BETTER HEALTH® OF PENNSYLVANIA

Amendment to Aetna Clinical PolicyBulletin Number: 0399

Upper Limb Prosthesis

There are no amendments for Medicaid.

www.aetnabetterhealth.com/pennsylvania revised 10/10/2019

Proprietary

http://www.aetnabetterhealth.com/pennsylvania

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