Impact of gait analysis on pathology identification and surgical recommendations in children with spina bifida

Background: Gait analysis provides quantitative data that can be used to supplement standard clinical evaluation in identifying and understanding gait problems. It has been established that gait analysis changes treatment decision making for children with cerebral palsy, but this has not yet been studied in other diagnoses such as spina bifida. Research question: To determine the effects of gait analysis data on pathology identification and surgical recommendations in children with spina bifida. Methods: Two pediatric orthopaedic surgeons and two therapists with > 10 years of experience in gait analysis reviewed clinical, video, and gait analysis data from 43 ambulatory children with spina bifida (25 male; mean age 11.7 years, SD 3.8; 25 sacral, 18 lumbar). Primary gait pathologies were identified by each assessor both before and after consideration of the gait analysis data. Surgical recommendations were also recorded by the surgeons before and after consideration of the gait analysis data. Frequencies of pathology and surgery identification with and without gait analysis were compared using Fisher's exact test, and percent change in pathology and surgery identification was calculated. Results: Pathology identification often changed for common gait problems including crouch (28% of cases), tibial rotation (35%), pes valgus (18%), excessive hip flexion (70%), and abnormal femur rotation (75%). Recognition of excessive hip flexion and abnormal femur rotation increased significantly after consideration of gait analysis data (p < 0.05). Surgical recommendations also frequently changed for the most common surgeries including tibial derotation osteotomy (30%), antero-lateral release (22%), plantar fascia release (33%), knee capsulotomy (25%), 1st metatarsal osteotomy (60%), and femoral derotation osteotomy (89%). At the patient level, consideration of gait analysis data altered surgical recommendations for 44% of patients. Significance: Since gait analysis data often changes pathology identification and surgical recommendations, treatment decision making may be improved by including gait analysis in the patient care process.