Preoperative and Postoperative, Three-dimensional Gait Analysis in Surgically Treated Patients With High-grade Spondylolisthesis

Abstract

Background: High-grade spondylolisthesis (HGS) (Myerding grade III-V) in adolescents can lead to a marked alteration of gait pattern and maybe the presenting symptom in these patients. This characteristic gait pattern in patients with HGS has been referred to as the “pelvic waddle.” Modern 3-dimensional (3D) gait analysis serves an important tool to objectively analyze the different components of this characteristic gait preoperatively and postoperatively and is an objective measure of postoperative improvement. This study demonstrates the use of 3D gait analysis preoperatively and postoperatively in a cohort of 4 consecutive patients with HGS treated surgically at a single tertiary referral center and utilize this to objectively evaluate outcome of surgical treatment in these patients. This has not been reported previously in a cohort of patients. Methods: This is a prospective analysis of patients with HGS who underwent surgical intervention for spondylolisthesis at a single institution. Patient demographics, clinical, and radiologic assessment were recorded, and all patients underwent 3D gait analysis before and after surgical intervention. Kinetic, kinematic, and spatial parameters were recorded preoperatively and postoperatively for all patients. This allowed the outcome of change in gait deviation index, before and after surgical treatment, to be evaluated. Results: We were able to review complete records of 4 adolescent patients who underwent surgical treatment for HGS. Mean age at surgery was 13.5 years with a minimum follow-up of 2.5 years postoperatively (average 40 mo). Preoperative gait analysis revealed marked posterior pelvic tilt in 2 patients, reduced hip and knee extension in all 4 patients and external foot progression in 3 of the 4 patients. Along with an observed improvement in gait, there was an objective improvement in gait parameters postoperatively in all 4 patients. Gait deviation index score improved significantly from 78.9 to 101.3 (mean). Conclusions: Preoperative gait abnormalities exist in HGS and can be objectively analyzed with gait analysis. Surgical intervention may successfully resolve these gait abnormalities and gait analysis is a useful tool to assess the outcome of surgery and quantify an otherwise intangible benefit of surgical intervention. Level of Evidence: Level IV—case series.

Application of the Gait Deviation Index to Study Gait Impairment in Adult Population With Spinal Cord Injury: Comparison With the Walking Index for Spinal Cord Injury Levels

“…The GDI has been used as an outcome measure to study gait in several conditions such as: CP ( Schwartz and Rozumalski, 2008 ; Molloy et al, 2010 ; Cimolin et al, 2011 ; Sagawa et al, 2013 ; Massaad et al, 2014 ; Wilson et al, 2015 ; Malt et al, 2016 ; Ito et al, 2019 ; Rasmussen et al, 2019 ), post-stroke hemiparetic gait ( Correa et al, 2017 ; Guzik and Drużbicki, 2020 ), Duchenne muscular dystrophy ( Sienko Thomas et al, 2010 ), Parkinson’s disease ( Galli et al, 2012 ; Speciali et al, 2013 ), arthritis ( Broström et al, 2013 ; Esbjörnsson et al, 2014 ; Rosenlund et al, 2016 ; Kobsar et al, 2019 ; Bazarnik-Mucha et al, 2020 ), lower limb amputations ( Eshraghi et al, 2014 ; Kark et al, 2016 ), degenerative spinal pathologies ( Mar et al, 2019 ; Trivedi et al, 2021 ; Zhou et al, 2021 ), diverse genetic ( Ito et al, 2020 ; Mindler et al, 2020 ) and congenital disorders ( Eriksson et al, 2015 ; Garman et al, 2019 ), and even the effect of the COVID-19 on physical function ( Ito et al, 2021 ), among others. A recently published article by Hwang et al (2021) used the GDI as a way to quantify and characterize gait patterns in ambulatory children and adolescents with transverse myelitis, whose gait showed moderate kinematic deviations from normal gait pattern.…”

Derivation of the Gait Deviation Index for Spinal Cord Injury

“…Ever since, that 15-feature basis originally developed from data of children with CP has been widely used to calculate the GDI across different conditions, including post-stroke hemiparetic gait ( Correa et al, 2017 ; Guzik and Drużbicki, 2020 ), Duchenne muscular dystrophy ( Sienko Thomas et al, 2010 ), Parkinson’s disease ( Galli et al, 2012 ; Speciali et al, 2013 ), arthritis ( Broström et al, 2013 ; Esbjörnsson et al, 2014 ; Kobsar et al, 2019 ; Bazarnik-Mucha et al, 2020 ), lower limb amputations ( Eshraghi et al, 2014 ; Kark et al, 2016 ), degenerative spinal pathologies ( Mar et al, 2019 ; Trivedi et al, 2021 ; Zhou et al, 2021 ), genetic disorders ( Ito et al, 2020 ; Mindler et al, 2020 ), congenital disorders ( Eriksson et al, 2015 ; Garman et al, 2019 ), the effect of the Covid-19 on physical function ( Ito et al, 2021 ), and mostly in CP ( Schwartz and Rozumalski, 2008 ; Molloy et al, 2010 ; Cimolin et al, 2011 ; Sagawa et al, 2013 ; Massaad et al, 2014 ; Wilson et al, 2015 ; Malt et al, 2016 ; Ito et al, 2019 ; Rasmussen et al, 2019 ).…”

DataSheet1.xlsx

“…Ever since, that 15-feature basis originally developed from data of children with CP has been widely used to calculate the GDI across different conditions, including post-stroke hemiparetic gait (Correa et al, 2017;Guzik and Drużbicki, 2020), Duchenne muscular dystrophy (Sienko Thomas et al, 2010), Parkinson's disease (Galli et al, 2012;Speciali et al, 2013), arthritis (Broström et al, 2013;Esbjörnsson et al, 2014;Kobsar et al, 2019;Bazarnik-Mucha et al, 2020), lower limb amputations (Eshraghi et al, 2014;Kark et al, 2016), degenerative spinal pathologies (Mar et al, 2019;Trivedi et al, 2021;Zhou et al, 2021), genetic disorders (Ito et al, 2020;Mindler et al, 2020), congenital disorders (Eriksson et al, 2015;Garman et al, 2019), the effect of the Covid-19 on physical function (Ito et al, 2021), and mostly in CP (Schwartz and Rozumalski, 2008;Molloy et al, 2010;Cimolin et al, 2011;Sagawa et al, 2013;Massaad et al, 2014;Wilson et al, 2015;Malt et al, 2016;Ito et al, 2019;Rasmussen et al, 2019).…”