Myoelectric Upper Limb Prostheses

Number: 0399

Policy

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 operation 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 could interfere with maintaining function of the prosthesis (eg, neuromuscular disease).

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 been established.

Aetna considers transcranial direct current stimulation for enhancing performance of myoelectric prostheses experimental and investigational because of insufficient evidence.

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.

For myoelectric prostheses of the lower extremity see CPB 0578 - Lower Limb Prostheses.

Background

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 conventional hooks..

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 a candidate.

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]):
  1. 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
  2. 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
  1. the performance of non-linear and linear pattern recognition algorithms, and
  2. 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.

Table: CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

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

CPT codes not covered for indications listed in the CPB:

Transcranial direct current stimulation (tDCS) - 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:

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
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:

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)

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

The above policy is based on the following references:

  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 of 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.
  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.
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