Gait pattern analysis and clinical subgroup identification: a retrospective observational study.

To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.In this retrospective study, temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1328 subjects). We assessed differences according to the condition and used a community detection algorithm to identify subgroups within each condition. Additionally, we developed a prediction model for subgroup classification according to gait speed and maximal hip extension in the stance phase.The main findings can be summarized as follows. First, there was an asymmetric decrease of the knee/ankle flexion angles in hemiplegia and a marked reduction of the hip/knee range of motion with increased moment in Parkinsonian disorders. Second, three abnormal gait patterns, including fast gait speed with adequate maximal hip extension, fast gait speed with inadequate maximal hip extension, and slow gait speed, were found throughout the conditions examined. Third, our simple prediction model based on gait speed and maximal hip extension angle was characterized by a high degree of accuracy in predicting subgroups within a condition.Our findings suggest the existence of specific gait patterns within and across conditions. Our novel subgrouping algorithm can be employed in routine clinical settings to classify abnormal gait patterns in various neurological disorders and guide the therapeutic approach and monitoring.