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Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology

 Sunghoon I. Lee  ;  Andrew Campion  ;  Alex Huang  ;  Eunjeong Park  ;  Jordan H. Garst  ;  Nima Jahanforouz  ;  Marie Espinal  ;  Tiffany Siero  ;  Sophie Pollack  ;  Marwa Afridi  ;  Meelod Daneshvar  ;  Saif Ghias  ;  Majid Sarrafzadeh  ;  Daniel C. Lu 
 Journal of Neuroengineering and Rehabilitation, Vol.14(1) : 77, 2017 
Journal Title
 Journal of Neuroengineering and Rehabilitation 
Issue Date
Adult ; Aged ; Biomechanical Phenomena ; Cohort Studies ; Decompression, Surgical ; Disability Evaluation ; Female ; Gait ; Humans ; Lumbar Vertebrae/surgery* ; Male ; Middle Aged ; Pain Measurement ; Pain, Postoperative/epidemiology ; Pilot Projects ; Postoperative Period ; Predictive Value of Tests ; Reproducibility of Results ; Shoes* ; Spinal Stenosis/surgery* ; Treatment Outcome ; Walking
Lumbar spinal stenosis ; Oswestry disability index ; Prediction ; Predominant pain ; Smart shoes ; Walking test
BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes. METHODS: This study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables. RESULTS: The gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10-7) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10-4). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated. CONCLUSIONS: The reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies.
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Park, Eunjeong(박은정)
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