Close Window
Aetna Aetna
Clinical Policy Bulletin:
Gait Analysis and Electrodynogram
Number: 0263


Policy

Aetna considers gait analysis (also known as motion analysis studies), dynamic electromyography or the use of an electrodynogram experimental and investigational for conditions that result in gait deviations and for all other indications because there is insufficient peer-reviewed medical literature demonstrating the clinical value of these technologies. 

See also CPB 0294 - Pedobarograpghy. 



Background

Several investigators have advocated the use of gait analysis for planning surgery and therapy treatments for children with cerebral palsy (CP).  Although their rationale appears sound, it has not been supported by clinical outcome studies demonstrating its efficacy beyond visual analysis of gait abnormalities routinely performed by clinicians.

An assessment conducted by the BlueCross BlueShield Association Technology Evaluation Center (2002) concluded that “[t]he evidence does not permit conclusions on whether the use of gait analysis for evaluation of children with cerebral palsy improves health outcomes or is as beneficial as established alternatives.”  The assessment noted that several studies have reported that treatment decisions were affected by the results of gait analysis; these studies, however, do not demonstrate whether clinical outcomes were improved by basing treatment decisions on gait analysis.  The assessment identified only 1 study that directly addressed the question of whether gait analysis improves patient outcomes, compared with standard clinical assessment.  This retrospective study (Lee et al, 1992) reported on the outcomes of 23 children with CP, 15 of whom were treated according to the recommendation of gait analysis data plus clinical assessment, and 8 of whom were treated according to clinical assessment alone.  Of the 7 children who were classified as not improved, 5 had been treated according to clinical assessment alone.  The TEC assessment noted several problems with this study, including small numbers of study subjects, lack of consideration of confounding factors, and vague definition of outcomes.  Most important, however, it was not reported whether children analyzed as treated according to gait analysis data underwent treatments discordant with the recommendations of clinical assessment alone or in agreement with clinical assessment alone (BCBSA, 2002).  The TEC assessment notes that, if it is the latter, then the study is severely flawed and can not be used to compare clinical assessment and gait analysis.  The TEC assessment concluded that, “[i]n the absence of any well-designed observational or randomized controlled trials, no conclusion can be drawn about whether gait analysis in the clinical evaluation and treatment of cerebral palsy has an effect upon health outcomes.”

Gait analysis studies that have been published since the TEC assessment was released suffer from similar limitations as previous studies, in that they have not included outcomes of internal comparison groups of children with CP who were managed based on clinical assessment alone.  Comparisons of outcomes between studies (i.e., comparisons of outcomes of studies where gait analysis was used with studies where gait analysis was not used) is problematic due to confounding factors that may account for differences in outcomes (such as differences in surgical technique and experience, characteristics of study subjects, methods of assessing outcomes, etc.).  

Several studies have reported that treatment decisions are affected by the results of gait analysis (Lofterod et al, 2007; Kay et al, 2000; Molenaers et al, 2006; Wren et al, 2005); these studies, however, do not demonstrate whether clinical outcomes are improved by basing treatment decisions on gait analysis.  Similarly, studies examining correlations (or the lack thereof) between clinical measurements and quantitative gait analysis (Desloovere et al, 2006; Kawamura et al, 2006) do not prove that the quantitative measurements provided by gait analysis alter the management of patients such that clinical outcomes are improved. 

One small retrospective study evaluated the impact of computerized gait analysis on clinical outcomes (Chang et al, 2006).  The study included 10 patients with CP and 10 age- and sex-matched controls.  Children in the control group chose not to follow the gait analysis recommendation and chose a non-surgical treatment approach, whereas children in the gait analysis group followed a physician's recommendation for gait analysis.  The results documented that 74 % of patients in the control group had no change or a negative outcome, 26 % in the control group had a positive outcome.  Of the gait analysis group 61 % had no change or a negative outcome, while 44 % of this group had a positive outcome.  While the results of this study suggest that gait analysis recommendations may improve clinical outcomes, the control group did not undergo surgery, whereas those in the gait analysis group did.  Therefore, it is not possible to determine the relative contributions of gait analysis and surgery.  

A study by Wren et al (2009) is a retrospective analysis of numbers of procedures and costs in children whose surgery was planned by gait analysis and those whose surgery was performed without gait analysis.  The investigators found no significant differences overall in total numbers of procedures and costs between the 2 groups.  Limitations of the study include its retrospective nature and lack of randomized assignment.  Since the study was retrospective, the authors were unable to assess other outcomes such as function, participation, and quality of life, which are important outcomes that need to be examined in future prospective studies.

Dobson and colleagues (2007) evaluated the validity of existing classifications of gait deviations in children with CP.  The authors noted that numerous efforts have been made to develop classification systems for gait in CP to assist in diagnosis, clinical decision-making and communication.  The authors examined the internal and external validity of gait classifications in 18 studies, including their sampling methods, content validity, construct validity, reliability and clinical utility.  The authors found that half of the studies used qualitative pattern recognition to construct the gait classification and the remainder used statistical techniques such as cluster analysis.  Few adequately defined their samples or sampling methods.  The authors found that most classifications were constructed using only sagittal plane gait data, and that many did not provide adequate guidelines or evidence of reliability and validity of the classification system.  No single classification addressed the full magnitude or range of gait deviations in children with CP.  The authors concluded that, although gait classification in CP can be useful in clinical and research settings, the methodological limitations of many classifications restrict their clinical and research applicability.

Lofterod and Terjesen (2008) evaluated the outcome of orthopedic surgery in ambulant children with CP, when the orthopedic surgeons followed the recommendations from pre-operative 3-dimensional gait analysis.  A total of 55 children (mean age of 10 years and 11 months) were clinically evaluated by orthopedic surgeons who proposed a surgical treatment plan.  After gait analysis and subsequent surgery, 3 groups were defined.  In group A, there was agreement between clinical proposals, gait analysis recommendations, and subsequent surgery in 128 specific surgical procedures.  In group B, 54 procedures were performed based on gait analysis, although these procedures had not been proposed at the clinical examination.  In group C, 55 surgical procedures that had been proposed after clinical evaluation were not performed because of the gait analysis recommendations.  The children underwent follow-up gait analysis 1 to 2 years after the initial analysis.  The kinematic results were satisfactory, with improvement in most of the gait parameters in children who had undergone surgery and no significant deterioration in those who were not operated.  In group A, there were significant improvements in maximum hip extension in stance, minimum knee flexion in stance, timing of maximum knee flexion in swing and knee range of motion (ROM), maximum ankle dorsiflexion in stance, and mean femur rotation in stance.  In group B, there were significant improvements in maximum hip extension in stance, minimum knee flexion in stance, and knee ROM.  The authors concluded that gait analysis was useful in confirming clinical indications for surgery, in defining indications for surgery that had not been clinically proposed, and for excluding or delaying surgery that was clinically proposed.  The findings of this study need to be validated by well-designed studies.

An additional important factor that limits the ability to interpret the evidence on gait analysis is the fact that the technical parameters, diagnostic variables, and outcome measures vary among studies.  Some of the differences included the number and placement of video cameras, reflective markers, and force plates; number and type of gait parameters that are measured; and variations in the use of EMG data.  Study participants are heterogenous with regard to the type of gait disorder and clinical history.  Studies of the impact of computerized gait analysis on patient management evaluated different types of surgical procedures, and varied in the number and type of muscles that were operated on.  In addition, few studies include follow-up data.

Narayanan (2007) reviewed the scientific literature to describe the role of gait analysis in the orthopedic management of ambulatory children with CP and examined the current best evidence to support these roles.  The author stated that although gait analysis has been shown to alter decision making, there is little evidence that the decisions based on gait analysis lead to better outcomes.  Consequently, clinical gait analysis remains controversial, with wide variation in the rates of utilization of gait analysis in the management of children with ambulatory CP.  The author stated that the time is ripe for clinical trials and cohort studies to provide the evidence to establish the appropriate utilization of this technology.

Randomized controlled clinical trials to compare outcomes of surgery planned with and without gait analysis are currently ongoing.  One of these studies, sponsored by the Federal Agency for Healthcare Research and Quality (AHRQ) and conducted at 2 children's hospitals in Los Angeles, has been completed, and its results (Wren, et al., 2012) are reported below.  The AHRQ explained why this study is needed: "Gait analysis testing has been used to assist orthopedic surgeons in developing treatment plans for children with gait abnormalities, particularly children with cerebral palsy.  Previous studies have shown that gait analysis testing significantly impacts surgical decision-making for these patients.  However, no controlled studies have been done to determine whether gait analysis and the subsequent changes in surgical decision-making affect clinical outcomes.  Consequently, the use of gait analysis in clinical practice remains controversial.  The purpose of this study is to conduct a randomized controlled trial to assess the effects of preoperative gait analysis on surgical outcomes in ambulatory children with cerebral palsy" (AHRQ, 2005).

Another randomized controlled clinical study of gait analysis, funded by the Canadian government, is being conducted at 2 children's hospitals in Toronto.  The study description explains the need for this trial: "Pre-operative planning is based on the physical examination and visual (observational) analysis of the child's gait.  In some centres, patients undergo additional gait analysis in a motion laboratory.  While gait laboratory analysis is accepted as an important research tool, there is controversy about its clinical utility in decision making for the surgical management of this population.  To date, no clinical trials have been undertaken to answer this question, and the appropriate clinical utilization of this technology is yet to be established.  The consequence of this uncertainty is that ambulatory children with cerebral palsy are either being deprived of a useful assessment tool in some centres, or alternatively they are being subjected to an unnecessary evaluation that is both expensive and time consuming in other centres" (Hospital for Sick Children, 2007).  Both of these randomized controlled clinical trials will examine the impact of gait analysis on a number of parameters, the most important of which relate to clinical outcomes (i.e., improvements in function and quality of life), as opposed to intermediate outcomes.

Maquet and colleagues (2010) evaluated gait characteristics during simple and dual task in patients with mild cognitive impairment (MCI) and compared them with those of healthy elderly subjects and mild Alzheimer's disease (AD) patients.  These researchers proposed a gait analysis to appreciate walking (simple task and dual task) in 14 MCI, 14 controls and 6 AD subjects who walked at their preferred speed.  A 20-second period of stabilized walking was used to calculated stride frequency, stride length, symmetry and regularity.  Speed walking was measured by electrical photocells.  Variables measured during simple and dual tasks showed an alteration of motor function as well in mild AD patients as in MCI patients.  The authors concluded that at the end of this preliminary study, they defined a specific gait pattern for each cognitive profile.  They stated that further researches appear necessary to enlarge the study cohort.  Furthermore, in a review on clinical gait analysis, Chapin (2010) stated that additional research documenting the value of incorporating clinical gait analysis in the treatment planning process may ultimately change the payment patterns of insurers.

Ornetti and colleagues (2010) stated that kinematic gait analysis consisting of measuring gait parameters (e.g., dynamic joint angles, gait speed, and stride length) is a potential outcome measure in osteoarthritis (OA).  These investigators evaluated the psychometric properties of gait analysis.  A systematic literature search was performed in PubMed and the Cochrane database until January 2008 by selecting manuscripts assessing any psychometric property of gait analysis in knee or hip OA.  These researchers assessed feasibility (access, cost, and time); reliability; discriminant capacity by differences between OA and non-OA patients; construct validity by correlation between gait analysis and OA symptoms: pain or functional disability (Lequesne/WOMAC); and responsiveness by improvement of gait analysis after treatment of OA using effect size.  Among the 252 articles identified, the final analysis included 30 reports (i.e., 781 knee OA patients and 343 hip OA patients).  Gait analysis presents various feasibility issues and there was limited evidence regarding reliability (3 studies; 67 patients).  Discriminant capacity showed significant reduction of gait speed, stride length and knee flexion in OA patients compared to healthy subjects.  Few data were available concerning construct validity (3 studies; 79 patients).  Responsiveness of gait speed was moderate to large with effect size ranging respectively from 0.33 to 0.89 for total knee replacement, and from 0.50 to 1.41 for total hip replacement.  The authors concluded that available data concerning validity and reliability of kinematic gait analysis are insufficient to date to consider kinematic parameters as valuable outcome measures in OA.  They stated that further studies evaluating a large number of patients are needed.

Calhoun et al (2011) compared kinematic and kinetic gait patterns in children with autism versus age-matched controls.  A total of 12 children with autism and 22 age-matched controls participated in the study.  An 8-camera motion capture system and 4 force plates were used to compute joint angles and joint kinetics during walking.  Parametric analyses and principal component analyses were applied to kinematic and kinetic waveform variables from the autism and control groups.  Group differences in parameterization values and principal component scores were tested using 1-way ANOVAs and Kruskal-Wallis tests.  Significant differences between the autism and control group were found for cadence, and peak hip and ankle kinematics and kinetics.  Significant differences were found for 3 of the principal component scores: (i) sagittal ankle moment principal component one, (ii) sagittal ankle angle principal component one, and (iii) sagittal hip moment principal component two.  Results suggest that children with autism demonstrate reduced plantar-flexor moments and increased dorsiflexion angles, which may be associated with hypotonia.  Decreased hip extensor moments were found for the autism group compared to the control group, however, the clinical significance of this result is unclear.  This study has identified several gait variables that were significantly different between autism and control group walkers.  This is the first study to provide a comprehensive analysis of gait patterns in children with autism.  The role of gait analysis, if any, in the managment of children with autism has yet to be established.

The American Association of Electrodiagnostic Medicine/American Academy of Physical Medicine and Rehabilitation's technology review on "Dynamic electromyography in gait and motion analysis" (1999) concluded that "its utility in pre-operative planning has not been proven in well-designed, large, multi-center studies.  There remains many legitimate differences of opinion as to the relative benefits of surface versus fine-wire techniques that future studies will need to resolve.  Also in doubt is the best way of determining onset of electrical activity and other technical variables.  Dynamic EMG, as part of comprehensive motion analysis, has found applications in the optimization of athletic performance.  The subjects in these studies are not patients in the classic sense and did not necessarily carry any type of medical diagnosis".

A randomized controlled clinical trial (Wren et al, 2013) found no significant difference in primary outcome measures and most secondary outcome measures with use of gait analysis in cerebral palsy surgery.  This study examined the impact of gait analysis on surgical outcomes in 156 ambulatory children with CP through a randomized controlled trial.  Patients underwent gait analysis and were randomized to 2 groups: (i) Gait Report group (n = 83), where the referring surgeon received the patient’s gait analysis report, and (ii) Control group (n = 73), where the surgeon did not receive the gait report.  Outcomes were assessed pre- and 1.3 + 0.5 years post-operatively.  An intent-to-treat analysis compared outcomes between the 2 groups.  The primary outcome measures were the walking scale of the Gillette Functional Assessment Questionnaire (FAQ), the Gait Deviation Index (GDI), and the oxygen cost of walking (O2 cost).  Secondary outcome measures included the gross motor function measure (GMFM-66) and health-related quality of life questionnaires (Child Health Questionnaire (CHQ), Pediatric Outcomes Data Collection Instrument (PODCI), and Pediatric Evaluation and Disability Inventory (PEDI).  The outcomes that differed significantly between groups were change in health component of the CHQ, which was rated as much better for 56 % (46/82) of children in the Gait Report group compared with 38 % (28/73) in the Control group (p = 0.04), and the upper extremity physical function component of the PODCI.  There were no significant differences in outcomes between the Gait Report group and the Control group in the primary outcome measures: the FAQ, the GDI, and O2 cost.  There were also no significant differences in most secondary outcomes, including the GMFM-66, all four components of the PEDI, and components of the CHQ other than change in health (i.e., global health, physical functioning, role/social limitations - physical, pain/discomfort, self esteem, general health perception, parental impact - emotional, and parental impact - time), and components of the PODCI other than upper extremity physical function (i.e., sports/physical function, transfer/basic mobility, pain/comfort, and global functioning).  The authors posited that one potential reason for the lack of difference between the Gait Report group and the Control group for most measures was because surgeons who received gait reports followed the report recommendations less than half (42 %) of the time.

In a cohort study, Chow and colleagues (2012) examined the velocity-dependent change in medial gastrocnemius (MG) activity during the stance phase of gait in patients with moderate-to-severe resting hypertonia after stroke or traumatic brain injury (TBI).  Convenience sample of patients with chronic TBI and stroke (n = 11 each), and age- and sex-matched healthy controls (n = 22).  Main outcome measures included frequency and gain (steepness) of positive (greater than 0) and significant positive (greater than 0 and goodness of fit p ≤ 0.05) electromyogram-lengthening velocity (EMG-LV) linear regression slope in MG during the stance phase of gait.  Positive and significant positive slopes were found significantly more often on the more affected (MA) than less affected (LA) side in patients with TBI but not stroke.  Both the frequencies of positive and significant positive slopes on the MA side in patients with TBI were also significantly higher than in controls.  However, neither the gain of positive nor significant positive EMG-LV slope was different between the MA and LA sides or in comparison with controls.  Positive slope parameters were not related to Ashworth score on the MA side.  The authors concluded that the frequency and gain of positive EMG-lengthening slope did not effectively differentiate patients from controls, nor were they related to the resting muscle hypertonia.  Motor output during MG lengthening in the stance phase of gait is apparently not exaggerated or related to resting hypertonia in patients with chronic TBI and stroke.  Thus, changes in gait during stance cannot be ascribed to increased stretch reflex activity in MG muscle after acquired brain injury.

An evidence review of management of children with cerebral palsy (Narayanan, 2012) stated that there is good evidence that gait analysis does alter surgical decision-making at least some of the time. However, "there remain concerns about the reliability (reproducibility) of these decisions or whether implementing these recommendations would result in different, let alone better outcomes" (Narayanan, 2012). The review reported on one study of gait analysis that found that, when the same gait analysis data were examined by gait analysis experts from 6 different institutions, there was only slight to moderate agreement in the list of problems generated by the experts (citing Skaggs, et al., 2000). Agreement about specific surgical recommendations was similarly poor. The review explained that, although gait analysis data are themselves objective, there is subjectivity in interpretation even among experts, with diagnoses and treatment recommendations varying significantly by surgeon or institution. The evidence review stated that, in another study (citing Noonan, et al., 2003), there was variability in the kinematic data generated in 4 different motion laboratories that tested the same 11 patients. Although the clinical significance of some of this variability has been challenged, the treatment recommendations generated from these data were different across the 4 centers for 9 of the 11 patients. The author of the review stated: "Variability in the interpretation of gait data reflects the prevailing uncertainty (or controversies) about the causes and/or significance of specific findings and will only be resolved with ongoing clinical research and experience using gait analysis. Similarly, variability in treatment recommendations based on the same gait data also reflects differences of opinion about best strategies to deal with specific problems, which in turn can only be definitively resolved with comparative clinical trials or observational studies" (Narayanan, 2012). The review author concluded that "as long as such significant variability exists, the recommendation that gait analysis is essential for all preoperative decision-making before multilevel orthopaedic surgery in clinical (as opposed to research) practice is currently not supported by the literature" (Narayanan, 2012).

An UpToDate review on “Gait disorders of elderly patients” (Ronthal, 2013) states that “It becomes evident that the control of walking is a highly complex and integrated activity.  With multiple control points, multiple areas of vulnerability to disruption of normal gait are present.  Knowledge of the basic physiology is a good starting point in gait disorder analysis.  More sophisticated analysis of gait by posturography and studies of gait variability are largely research tools, but can help with rehabilitation”.

 
CPT Codes / HCPCS Codes / ICD-9 Codes
CPT codes not covered for indications listed in the CPB:
96000
96001
96002
96003
96004
ICD-9 codes not covered for indications listed in the CPB (not all-inclusive):
343.0 - 343.9 Infantile cerebral palsy
344.00 - 344.9 Other specified paralytic syndromes
781.2 Abnormality of gait
781.3 Lack of coordination


The above policy is based on the following references:
  1. Stefko RM, de Swart RJ, Dodgin DA, et al. Kinematic and kinetic analysis of distal derotational osteotomy of the leg in children with cerebral palsy. J Pediatr Orthop. 1998;18(1):81-87.
  2. Dietz V. Neurophysiology of gait disorders: Present and future applications. Electroencephalogr Clin Neurophysiol. 1997;103(3):333-355.
  3. Dabney KW, Lipton GE, Miller F. Cerebral palsy. Curr Opin Pediatr. 1997;9(1):81-88.
  4. Scott AC, Chambers C, Cain TE. Adductor transfers in cerebral palsy: Long-term results studied by gait analysis. J Pediatr Orthop. 1996;16(6):741-746.
  5. DeLuca PA. The musculoskeletal management of children with cerebral palsy. Pediatr Clin North Am. 1996;43(5):1135-1150.
  6. Rechtien JJ, Gelblum JB, Haig AJ, et al. Technology assessment: Dynamic electromyography in gait and motion analysis. Muscle Nerve. 1996;19(3):396-402.
  7. Gage JR. Editorial. The role of gait analysis in the treatment of cerebral palsy. J Pediatr Orthop. 1994;14:701-702.
  8. Watts HG. Editorial. Gait laboratory analysis for preoperative decision making in spastic cerebral palsy: Is it all it's cracked up to be? J Pediatr Orthop. 1994;14:703-704.
  9. Kirkpatrick M, Wytch R, Cole G, et al. Is the objective assessment of cerebral palsy gait reproducible? J Pediatr Orthop. 1994;14:705-708.
  10. Park TS, Owen JH. Surgical management of spastic diplegia in cerebral palsy. N Engl J Med. 1992;326(11):745-749.
  11. Lee EH, Goh JCH, Bose K. Value of gait analysis in the assessment of surgery in cerebral palsy. Arch Phys Med Rehabil. 1992;73:642-646.
  12. Shapiro A, Susak Z, Malkin C, et al. Preoperative and postoperative gait evaluation in cerebral palsy. Arch Phys Med Rehabil. 1990;71:236-240.
  13. Tomie J, Hailey D. Computerized gait analysis in the rehabilitation of children with cerebral palsy and spina bifida. Health Technology Assessment. HTA 5. Edmonton, AB: Alberta Heritage Foundation for Medical Research; October 1997.
  14. Cottalorda J. Gait analysis: Matching the method to the goal. Rev Rhum Engl Ed. 1999;66(7-9):367-369.
  15. Coutts F. Gait analysis in the therapeutic environment. Man Ther. 1999;4(1):2-10.
  16. Morton R. New surgical interventions for cerebral palsy and the place of gait analysis. Dev Med Child Neurol. 1999;41(6):424-428.
  17. Banta J. Gait analysis: Past, present, and future. Dev Med Child Neurol. 1999;41(6):363.
  18. Cooper RA, Quatrano LA, Stanhope SJ, et al. Gait analysis in rehabilitation medicine: A brief report. Am J Phys Med Rehabil. 1999;78(3):278-280.
  19. Andriacchi TP. Practical and theoretical considerations in the application in the development of clinical gait analysis. Biomed Mater Eng. 1998;8(3-4):137-143.
  20. D'Amico JC. The F-Scan system with EDG module for gait analysis in the pediatric patient. J Am Podiatr Med Assoc. 1998;88(4):166-175.
  21. Chau T. A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods. Gait Posture. 2001;13(1):49-66.
  22. Chau T. A review of analytical techniques for gait data. Part 2: Neural network and wavelet methods. Gait Posture. 2001;13(2):102-120.
  23. BlueCross BlueShield Association (BCBSA), Technology Evaluation Center (TEC). Gait analysis for pediatric cerebral palsy. TEC Assessment Program. Chicago, IL: BCBSA; April 2002;16(19). Available at: http://www.bcbs.com/tec/. Accessed August 18, 2003.
  24. Lee EH, Nather A, Goh J, et al. Gait analysis in cerebral palsy. Ann Acad Med. 1985;14(1):37-43.
  25. DeLuca PA. Gait analysis in the treatment of the ambulatory child with cerebral palsy. Clin Orthopaed. 1991;264:65-75.
  26. Nene AV, Evangs GA, Patrick JH. Simultaneous multiple operations for spastic diplegia. J Bone Joint Surg. 1993;75-B(3):488-494.
  27. Kerrigan DC, Glenn MB. An illustration of clinical gait laboratory use to improve rehabilitation management. Am J Phys Med Rehabil. 1994;73(6):421-427.
  28. DeLuca PA, Davis RB, Ounpuu S, et al. Alterations in surgical decision making in patients with cerebral palsy based on three-dimensional gait analysis. J Pediatr Orthopaed. 1997;17:608-614.
  29. Fabry G, Liu XC, Molenaers G. Gait pattern in patients with spastic diplegic cerebral palsy who underwent staged operations. J Pediatr Orthopaed. 1999;8(Part B):33-38.
  30. Perry J. The use of gait analysis for surgical recommendations in traumatic brain injury. J Head Rehabil. 1999;14(2):116-135.
  31. Gage JR, Novacheck TF. An update on the treatment of gait problems in cerebral palsy. J Pediatr Orthopaed. 2001;10(Part B):265-274
  32. Fuller DA, Keenan MAE, Esquenazi A, et al. The impact of instrumented gait analysis on surgical planning: Treatment of spastic equinovarus deformity of the foot and ankle. Foot Ankle Int. 2002;22(8):738-743.
  33. Saraph V, Zwick E, Zwick G, et al. Multilevel surgery in spastic diplegia: Evaluation by physical examination and gait analysis in 25 children. J Pediatr Orthopaed. 2002;22:150-157.
  34. Schwartz MH, Viehweger E, Stout J, et al. Comprehensive treatment of ambulatory children with cerebral palsy. An outcome assessment. J Pediatr Orthop. 2004;24(1):45-53.
  35. Simon SR. Quantification of human motion: Gait analysis-benefits and limitations to its application to clinical problems. J Biomech. 2004;37(12):1869-1880.
  36. Ounpuu S, DeLuca PA, Bell KJ, Davis RB. Using surface electrodes for the evaluation of the rectus femoris, vastus medialis and vastus lateralis muscles in children with cerebral palsy. Gait Posture. 1997;5(3):211-216.
  37. DeLuca PA, Ounpuu S, Davis RB, Walsh JH. Effect of hamstring and psoas lengthening on pelvic tilt in patients with spastic diplegic cerebral palsy. J Pediatr Orthopaed. 1998;18(6):712-718.
  38. Filho MC, Yoshida R, Carvalho WD, et al. Are the recommendations from three-dimensional gait analysis associated with better postoperative outcomes in patients with cerebral palsy? Gait Posture. 2008;28(2):316-322.
  39. Dobson F, Morris ME, Baker R, Graham HK. Gait classification in children with cerebral palsy: A systematic review. Gait Posture. 2007;25(1):140-152.
  40. Narayanan UG. The role of gait analysis in the orthopaedic management of ambulatory cerebral palsy. Curr Opin Pediatr. 2007;19(1):38-43.
  41. The Hospital for Sick Children. Outcomes of orthopaedic surgery using gait laboratory versus observational gait analysis in children with cerebral palsy. ClinicalTrials.gov Identifier:  NCT00419432. Bethesda, MD: National Institutes of Health (NIH), National Library of Medicine (NLM); January 5, 2007.
  42. Agency for Healthcare Research and Quality (AHRQ). Effects of walking analysis on surgical outcomes. ClinicalTrials.gov Identifier: NCT00114075. Bethesda, MD: National Institutes of Health (NIH), National Library of Medicine (NLM); June 23, 2005.
  43. Lofterød B, Terjesen T, Skaaret I, et al. Preoperative gait analysis has a substantial effect on orthopedic decision making in children with cerebral palsy: Comparison between clinical evaluation and gait analysis in 60 patients. Acta Orthop. 2007;78(1):74-80.
  44. Chang FM, Seidl AJ, Muthusamy K, et al. Effectiveness of instrumented gait analysis in children with cerebral palsy--comparison of outcomes. J Pediatr Orthop. 2006;26(5):612-616. 
  45. Desloovere K, Molenaers G, Feys H, et al. Do dynamic and static clinical measurements correlate with gait analysis parameters in children with cerebral palsy? Gait Posture. 2006;24(3):302-313. 
  46. Kawamura CM, de Morais Filho MC, et al. Comparison between visual and three-dimensional gait analysis in patients with spastic diplegic cerebral palsy. Gait Posture. 2007;25(1):18-24. 
  47. Molenaers G, Desloovere K, Fabry G, De Cock P. The effects of quantitative gait assessment and botulinum toxin a on musculoskeletal surgery in children with cerebral palsy. J Bone Joint Surg Am. 2006;88(1):161-170.
  48. Wren TA, Woolf K, Kay RM. How closely do surgeons follow gait analysis recommendations and why? J Pediatr Orthop B. 2005;14(3):202-205.
  49. Cook RE, Schneider I, Hazlewood ME, et al. Gait analysis alters decision-making in cerebral palsy. J Pediatr Orthop. 2003;23(3):292-295.
  50. Kay RM, Dennis S, Rethlefsen S, et al. The effect of preoperative gait analysis on orthopaedic decision making. Clin Orthop Relat Res. 2000;(372):217-222.
  51. Lofterød B, Terjesen T. Results of treatment when orthopaedic surgeons follow gait-analysis recommendations in children with CP. Dev Med Child Neurol. 2008;50(7):503-509.
  52. Wren TA, Kalisvaart MM, Ghatan CE, et al. Effects of preoperative gait analysis on costs and amount of surgery. J Pediatr Orthop. 2009;29(6):558-563.
  53. Buderath P, Gärtner K, Frings M, et al. Postural and gait performance in children with attention deficit/hyperactivity disorder. Gait Posture. 2009;29(2):249-254.
  54. Damiano DL, Arnold AS, Steele KM, Delp SL. Can strength training predictably improve gait kinematics? A pilot study on the effects of hip and knee extensor strengthening on lower-extremity alignment in cerebral palsy. Phys Ther. 2010;90(2):269-279.
  55. Maquet D, Lekeu F, Warzee E, et al. Gait analysis in elderly adult patients with mild cognitive impairment and patients with mild Alzheimer's disease: Simple versus dual task: a preliminary report. Clin Physiol Funct Imaging. 2010;30(1):51-56.
  56. Chapin KB. A focus on clinical gait analysis. Rehab Manag. 2010;23(1):10-12.
  57. Ornetti P, Maillefert JF, Laroche D, et al. Gait analysis as a quantifiable outcome measure in hip or knee osteoarthritis: A systematic review. Joint Bone Spine. 2010;77(5):421-425.
  58. Calhoun M, Longworth M, Chester VL. Gait patterns in children with autism. Clin Biomech (Bristol, Avon). 2011;26(2):200-206.
  59. American Association of Electrodiagnostic Medicine/American Academy of Physical Medicine and Rehabilitation. Technology review: Dynamic electromyography in gait and motion analysis. Muscle Nerve 1999;22 (Supplement 8):S233-S238. Available at: http://www.aanem.org/getmedia/68743ce6-4f67-4384-9ee5-9a39c4424418/tech_rvw_dynamic_emg.PDF.aspx. Accessed February 16, 2012.
  60. Skaggs DL, Rethlefsen SA, Kay RM, et al. Variability in gait analysis interpretation. J Pediatr Orthop. 2000;20:759–764.
  61. Noonan KJ, Halliday S, Browne R, et al. Interobserver variability of gait analysis in patients with cerebral palsy. J Pediatr Orthop. 2003;23:279–287; discussion 288–291.
  62. Chow JW, Yablon SA, Stokic DS. Et al. Electromyogram-lengthening velocity relation in plantar flexors during stance phase of gait in patients with hypertonia after acquired brain injury. Arch Phys Med Rehabil. 2012;93(12):2287-2294.
  63. Wren TA, Otsuka NY, Bowen RE, et al. Influence of gait analysis on decision-making for lower extremity orthopaedic surgery: Baseline data from a randomized controlled trial. Gait Posture. 2011;34(3):364-369.
  64. Wren TA, Elihu KJ, Mansour S, et al. Differences in implementation of gait analysis recommendations based on affiliation with a gait laboratory. Gait Posture. 2013;37(2):206-209.
  65. Wren TA, Otsuka NY, Bowen RE, et al. Outcomes of lower extremity orthopedic surgery in ambulatory children with cerebral palsy with and without gait analysis: Results of a randomized controlled trial. Gait Posture. 2013;38(2):236-241.
  66. Narayanan UG. Management of children with ambulatory cerebral palsy: An evidence-based review. J Pediatr Orthop. 2012;32 Suppl 2:S172-S181.
  67. Ronthal M. Gait disorders of elderly patients. Last reviewed December 2013. UpToDate Inc., Waltham, MA.


email this page   


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.
Aetna
Back to top