The Diagnostic Scope of Sensor-Based Gait Analysis in Atypical Parkinsonism: Further Observations - Gaßner, Heiko; Raccagni, Cecilia; Eskofier, Bjoern M.; Klucken, Jochen; Wenning, Gregor K. (Frontiers in Neurology, 2019)
Background: Differentiating idiopathic Parkinson´s disease (IPD) from atypical parkinsonian disorders (APD) is challenging, especially in early disease stages. Postural instability and gait difficulty (PIGD) are core features of IPD and APD. Clinical evidence implies that patients with APD have larger PIGD impairment than IPD patients. Sensor-based gait analysis as instrumented bedside test revealed more gait deficits in APD compared to IPD. However, the diagnostic value of instrumented bedside tests compared to clinical assessments in differentiating APD from IPD patients have not been evaluated so far. Objective: The objectives were a) to evaluate whether sensor-based gait parameters provide additional information to validated clinical scores in differentiating APD from matched IPD patients, and b) to investigate if objective, instrumented gait assessments have comparable discriminative power to clinical scores. Methods: In a previous study we have recorded instrumented gait parameters in patients with APD (Multiple System Atrophy and Progressive Supranuclear Palsy). Here, we compared gait parameters to those of retrospectively pairwise disease duration-, age-, and gender-matched IPD patients in order to address this new research questions. To this aim, the PIGD score was calculated as sum of the MDS-UPDRS-3-items “gait”, “postural stability”, “arising from chair”, and “posture”. Gait characteristics were evaluated in standardized gait tests using an instrumented, sensor-based gait analysis system mounted on the lateral heel part of both shoes. Patients performed overground gait tests in self-selected walking speed. Machine learning algorithms were used to extract spatio-temporal gait parameters from raw sensor signals. Receiver Operating Characteristic analysis was performed in order to detect the discriminative power of the instrumented versus the clinical bedside tests in differentiating IPD from APD. Results: A high Area Under the Curve (AUC) was shown by PIGD score (0.919), and UPDRS-3 (0.848). Particularly, the objective parameters stance time variability (0.841), swing time variability (0.834), stride time variability (0.821), and stride length variability (0.804) reached high AUC’s as well. Conclusions: PIGD symptoms showed high discriminative power in differentiating IPD from APD supporting gait disorders as substantial diagnostic target. Sensor-based gait variability parameters provide metric, objective added value and serve as complementary outcomes supporting clinical diagnostics and long-term home-monitoring concepts.