Predictors of Functional Outcome in a Cohort of Hispanic Patients Using Exoskeleton Rehabilitation for Cerebrovascular Accidents and Traumatic Brain Injury

Lisa R Treviño, Peter Roberge, Michael E Auer, Angela Morales, Annelyn Torres-Reveron, Lisa R Treviño, Peter Roberge, Michael E Auer, Angela Morales, Annelyn Torres-Reveron

Abstract

Traumatic brain injury (TBI) and cerebrovascular accidents (CVA) are two of the leading causes of disability in the United States. Robotic exoskeletons (RE) have been approved for rehabilitation by the Federal Drug Administration (FDA) for use after a CVA, and recently received approval for use in patients with TBI. The aim of the study was to determine which factors predict the improvement in functional independence measure (FIM) score after using RE rehabilitation in a population of patients with CVA or TBI. We carried out a retrospective chart-review analysis of the use of the RE (Ekso® GT) in the rehabilitation of patients with TBI and CVA using data from a single, private rehabilitation hospital for patients admitted and discharged between 01/01/2017 and 04/30/2020. From the medical records, we collected presentation date, Glasgow Coma Scale score (GCS) on the date of injury, rehabilitation start date, age, diabetes status on presentation (Yes or No), injury category (TBI or CVA), and both admission and discharge FIM scores. Matching algorithms resulted in one TBI patient matched to three CVA patients resulting in a sample size of 36. The diabetic and non-diabetic populations showed significant differences between age and days from injury to the start of rehabilitation. A multivariate linear regression assessed predictors for discharge motor FIM and found admission motor FIM score and total RE steps to be statistically significant predictors. For each point scored higher on the admission motor FIM the discharge FIM was increased by 1.19 FIM points, and for each 1,000 steps taken in the RE, the discharge motor FIM increased by three points. The type of acquired brain injury (CVA or TBI) was not found to affect functional outcome. The presented results show that key clinic-biologic factors including diabetic status, together with start to rehabilitation play key roles in discharge FIM scores for patients using RE. Clinical Trial Registration: ClinicalTrials.gov, NCT04465019.

Keywords: diabetes; gait; injury; minorities; trauma.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Treviño, Roberge, Auer, Morales and Torres-Reveron.

Figures

Figure 1
Figure 1
Diagram of the method used for comparing patients in the CVA and TBI groups with the final number of subjects included in each group.
Figure 2
Figure 2
Comparisons of motor FIM values. (A) Patients in the CVA group had similar admission and change in motor FIM compared to patients in the TBI group. (B) Diabetic status alone, did not influence the admission or the change in motor FIM score. However, diabetic status interacts with the time to start rehabilitation influencing motor FIM (see Table 3).

References

    1. Asselin P. K., Avedissian M., Knezevic S., Kornfeld S., Spungen A. M. (2016). Training persons with spinal cord injury to ambulate using a powered exoskeleton. J. Vis. Exp. 12:54071. 10.3791/54071
    1. Berger A., Horst F., Müller S., Steinberg F., Doppelmayr M. (2019). Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation: a brief review. Front. Hum. Neurosci. 13:172. 10.3389/fnhum.2019.00172
    1. Blackwell D. L., Villarroel M. A. (2018). Summary health statistics for U.S. adults: national health interview survey. Natl. Cent. Heal. Stat.
    1. Bode R. K., Heinemann A. W. (2002). Course of functional improvement after stroke, spinal cord injury, and traumatic brain injury. Arch. Phys. Med. Rehabil. 83, 100–106. 10.1053/apmr.2002.26073
    1. Bruni M. F., Melegari C., De Cola M. C., Bramanti A., Bramanti P., Calabr,ò R. S. (2018). What does best evidence tell us about robotic gait rehabilitation in stroke patients: a systematic review and meta-analysis. J. Clin. Neurosci. Off. J. Neurosurg. Soc. Australas. 48, 11–17. 10.1016/j.jocn.2017.10.048
    1. Capizzi A., Woo J., Verduzco-Gutierrez M. (2020). Traumatic brain injury: an overview of epidemiology, pathophysiology, and medical management. Med. Clin. North Am. 104, 213–238. 10.1016/j.mcna.2019.11.001
    1. Centers for Disease Control and Prevention (2015). Report to Congress on Traumatic Brain Injury in the United States: Epidemiology and Rehabilitation. Centers for Disease Control and Prevention.
    1. Centers for Disease Control Prevention (2019). Surveillance Report of Traumatic Brain Injury-Related Emergency Department Visits, Hospitalizations, and Deaths. Centers for Disease Control and Prevention. Available online at: (accessed March 15, 2021).
    1. Chen R., Ovbiagele B., Feng W. (2017). Diabetes and stroke: epidemiology, pathophysiology, pharmaceuticals, and outcomes. Am. J. Med. Sci. 351, 380–386. 10.1016/j.amjms.2016.01.011
    1. Dijkers M. P., Akers K. G., Dieffenbach S., Galen S. S. (2019). Systematic reviews of clinical benefits of exoskeleton use for gait and mobility in neurologic disorders: a tertiary study. Arch. Phys. Med. Rehabil. 102, 300–313. 10.1016/j.apmr.2019.01.025
    1. Dijkers M. P., deBear P. C., Erlandson R. F., Kristy K., Geer D. M., Nichols A. (1991). Patient and staff acceptance of robotic technology in occupational therapy: a pilot study. J. Rehabil. Res. Dev. 28, 33–44. 10.1682/JRRD.1991.04.0033
    1. DiRocco P. J. (1995). Fitness Programming and Physical Disability, 1st Edn. Champaing, IL: Human Kinetics.
    1. Esquenazi A., Lee S., Packel A. T., Braitman L. (2013). A randomized comparative study of manually assisted versus robotic-assisted body weight supported treadmill training in persons with a traumatic brain injury. PM R 5, 280–290. 10.1016/j.pmrj.2012.10.009
    1. Esquenazi A., Lee S., Wikoff A., Packel A., Toczylowski T., Feeley J. (2017). A comparison of locomotor therapy interventions: partial-body weight-supported treadmill, lokomat, and G-EO training in people with traumatic brain injury. PM R 9, 839–846. 10.1016/j.pmrj.2016.12.010
    1. Fasoli S. E., Adans-Dester C. P. (2019). A paradigm shift: rehabilitation robotics, cognitive skills training, and function after stroke. Front. Neurol. 10:1088. 10.3389/fneur.2019.01088
    1. Gempeler A., Orrego-González E., Hernandez-Casanas A., Castro A. M., Aristizabal-Mayor J. D., Mejia-Mantilla J. H. (2020). Incidence and effect of diabetes insipidus in the acute care of patients with severe traumatic brain injury. Neurocrit. Care 33, 718–724. 10.1007/s12028-020-00955-x
    1. Giles G. M. (2010). Cognitive versus functional approaches to rehabilitation after traumatic brain injury: commentary on a randomized controlled trial. Am. J. Occup. Ther. 64, 182–185. 10.5014/ajot.64.1.182
    1. Globe Newswire (2020). Ekso Bionics® Receives FDA Clearance to Market its EksoNRTM Robotic Exoskeleton for Use with Acquired Brain Injury Patients. Available online at: (accessed March 15, 2021).
    1. Hesse S., Werner C. (2009). Connecting research to the needs of patients and clinicians. Brain Res. Bull. 78, 26–34. 10.1016/j.brainresbull.2008.06.004
    1. Karunakaran K. K., Nisenson D. M., Nolan K. J. (2020). Alterations in cortical activity due to robotic gait training in traumatic brain injury. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020, 3224–3227. 10.1109/EMBC44109.2020.9175764
    1. Khoury J. C., Kleindorfer D., Alwell K., Moomaw C. J., Woo D., Adeoye O., et al. . (2013). Diabetes mellitus: a risk factor for ischemic stroke in a large biracial population. Stroke 44, 1500–1504. 10.1161/STROKEAHA.113.001318
    1. Kumar R. G., Ketchum J. M., Corrigan J. D., Hammond F. M., Sevigny M., Dams-O'Connor K. (2020). The longitudinal effects of comorbid health burden on functional outcomes for adults with moderate to severe traumatic brain injury. J. Head Trauma Rehabil. 35, E372–E381. 10.1097/HTR.0000000000000572
    1. Kwakkel G., Kollen B., Lindeman E. (2004). Understanding the pattern of functional recovery after stroke: facts and theories. Restor. Neurol. Neurosci. 22, 281–299. Available online at:
    1. Lane N. E. (2006). Epidemiology, etiology, and diagnosis of osteoporosis. Am. J. Obstet. Gynecol. 194, S3–S11. 10.1016/j.ajog.2005.08.047
    1. Langhorne P., Bernhardt J., Kwakkel G. (2011). Stroke rehabilitation. Lancet 377, 1693–1702. 10.1016/S0140-6736(11)60325-5
    1. Lapitskaya N., Nielsen J. F., Fuglsang-Frederiksen A. (2011). Robotic gait training in patients with impaired consciousness due to severe traumatic brain injury. Brain Inj. 25, 1070–1079. 10.3109/02699052.2011.607782
    1. Lee M., Saver J. L., Hong K.-S., Song S., Chang K.-H., Ovbiagele B. (2012). Effect of pre-diabetes on future risk of stroke: meta-analysis. BMJ 344:e3564. 10.1136/bmj.e3564
    1. Leeper T. J. (2018). Margins: Marginal Effects for Model Objects. Available online at: (accessed March 15, 2021).
    1. Linacre J. M., Heinemann A. W., Wright B. D., Granger C. V., Hamilton B. B. (1994). The structure and stability of the functional independence measure. Arch. Phys. Med. Rehabil. 75, 127–132. 10.1016/0003-9993(94)90384-0
    1. Logan L. M., Semrau J. A., Debert C. T., Kenzie J. M., Scott S. H., Dukelow S. P. (2018). Using robotics to quantify impairments in sensorimotor ability, visuospatial attention, working memory, and executive function after traumatic brain injury. J. Head Trauma Rehabil. 33, E61–E73. 10.1097/HTR.0000000000000349
    1. Louie D. R., Mortenson W. B., Durocher M., Teasell R., Yao J., Eng J. J. (2020). Exoskeleton for post-stroke recovery of ambulation (ExStRA): study protocol for a mixed-methods study investigating the efficacy and acceptance of an exoskeleton-based physical therapy program during stroke inpatient rehabilitation. BMC Neurol. 20, 1–9. 10.1186/s12883-020-1617-7
    1. Maggio M. G., Torrisi M., Buda A., De Luca R., Piazzitta D., Cannavò A., et al. . (2020). Effects of robotic neurorehabilitation through lokomat plus virtual reality on cognitive function in patients with traumatic brain injury: a retrospective case-control study. Int. J. Neurosci. 130, 117–123. 10.1080/00207454.2019.1664519
    1. Mehrholz J., Thomas S., Werner C., Kugler J., Pohl M., Elsner B. (2017). Electromechanical-assisted training for walking after stroke. Cochrane Database Syst. Rev. 5:CD006185. 10.1002/14651858.CD006185.pub4
    1. Millard A. V., Graham M. A., Mier N., Moralez J., Perez-Patron M., Wickwire B., et al. . (2017). Diabetes screening and prevention in a high-risk, medically isolated border community. Front. Public Heal. 5:135. 10.3389/fpubh.2017.00135
    1. National Spinal Cord Injury Statistical Center (2019). Facts and Figures at Glance. Birmingham, AL: National Spinal Cord Injury Statistical Center.
    1. Nelson L. D., Temkin N. R., Dikmen S., Barber J., Giacino J. T., Yuh E., et al. . (2019). Recovery after mild traumatic brain injury in patients presenting to us level i trauma centers: a transforming research and clinical knowledge in traumatic brain injury (TRACK-TBI) Study. JAMA Neurol. 76, 1049–1059. 10.1001/jamaneurol.2019.1313
    1. Nolan K. J., Karunakaran K. K., Chervin K., Monfett M. R., Bapineedu R. K., Jasey N. N., et al. . (2020). Robotic exoskeleton gait training during acute stroke inpatient rehabilitation. Front. Neurorobot. 14:581815. 10.3389/fnbot.2020.581815
    1. Nolan K. J., Karunakaran K. K., Ehrenberg N., Kesten A. G. (2018). Robotic exoskeleton gait training for inpatient rehabilitation in a young adult with traumatic brain injury. Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018, 2809–2812. 10.1109/EMBC.2018.8512745
    1. O'Donnell M. J., Xavier D., Liu L., Zhang H., Chin S. L., Rao-Melacini P., et al. . (2010). Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet 376, 112–123. 10.1016/S0140-6736(10)60834-3
    1. Palermo A. E., Maher J. L., Baunsgaard C. B., Nash M. S. (2017). Clinician-focused overview of bionic exoskeleton use after spinal cord injury. Top. Spinal Cord Inj. Rehabil. 23, 234–244. 10.1310/sci2303-234
    1. Parrington L., Jehu D. A., Fino P. C., Stuart S., Wilhelm J., Pettigrew N., et al. . (2020). The Sensor Technology and Rehabilitative Timing (START) protocol: a randomized controlled trial for the rehabilitation of mild traumatic brain injury. Phys. Ther. 100, 687–697. 10.1093/ptj/pzaa007
    1. Permenter C. M., Fernández-de Thomas R. J., Sherman A.l. (2020). Postconcussive Syndrome. Treasure Island, FL: StatPearls Publishing.
    1. R Core Team (2020). R: A Language and Environment for Statistical Computing. Available online at: (accessed March 15, 2021).
    1. Tatara Y., Shimada R., Kibayashi K. (2020). Effects of preexisting diabetes mellitus on the severity of traumatic brain injury. J. Neurotrauma 38, 886–902. 10.1089/neu.2020.7118
    1. Theadom A., McDonald S., Starkey N., Barker-Collo S., Jones K. M., Ameratunga S., et al. . (2019). Social cognition four years after mild-TBI: an age-matched prospective longitudinal cohort study. Neuropsychology 33, 560–567. 10.1037/neu0000516
    1. Thorpe E. R., Garrett K. B., Smith A. M., Reneker J. C., Phillips R. S. (2018). Outcome measure scores predict discharge destination in patients with acute and subacute stroke: a systematic review and series of meta-analyses. J. Neurol. Phys. Ther. 42, 2–11. 10.1097/NPT.0000000000000211
    1. Tomida K., Sonoda S., Hirano S., Suzuki A., Tanino G., Kawakami K., et al. . (2019). Randomized controlled trial of gait training using Gait Exercise Assist Robot (GEAR) in stroke patients with hemiplegia. J. Stroke Cerebrovasc. Dis. Off. J. Natl. Stroke Assoc. 28, 2421–2428. 10.1016/j.jstrokecerebrovasdis.2019.06.030
    1. Tziomalos K. Type 2 diabetes is associated with a worse functional outcome of ischemic stroke. World J Diabetes. (2014) 5:939. 10.4239/wjd.v5.i6.939
    1. Uniform Data system for Medical Rehabilitation (2020). Available online at: (accessed March 15, 2021).
    1. UT Health–School of Public Health Brownsville (2018). RHP 5 Community Needs Assessment. Available online at: (accessed March 15, 2021).
    1. Wang Y. H., Yang Y. R., Pan P. J., Wang R. Y. (2014). Modeling factors predictive of functional improvement following acute stroke. J. Chin. Med. Assoc. 77, 469–476. 10.1016/j.jcma.2014.03.006
    1. Williams G., Lai D., Schache A., Morris M. E. (2015). Classification of gait disorders following traumatic brain injury. J. Head Trauma Rehabil. 30, E13–E23. 10.1097/HTR.0000000000000038

Source: PubMed

3
Abonner