Walking assessment after lumbar puncture in normal-pressure hydrocephalus: a delayed improvement over 3 days.

OBJECTIVE The determination of gait improvement after lumbar puncture (LP) in idiopathic normal-pressure hydrocephalus (iNPH) is crucial, but the best time for such an assessment is unclear. The authors determined the time course of improvement in walking after LP for single-task and dual-task walking in iNPH. METHODS In patients with iNPH, sequential recordings of gait velocity were obtained prior to LP (time point [TP]0), 1-8 hours after LP (TP1), 24 hours after LP (TP2), 48 hours after LP (TP3), and 72 hours after LP (TP4). Gait analysis was performed using a pressure-sensitive carpet (GAITRite) under 4 conditions: walking at preferred velocity (STPS), walking at maximal velocity (STMS), walking while performing serial 7 subtractions (dual-task walking with serial 7 [DTS7]), and walking while performing verbal fluency tasks (dual-task walking with verbal fluency [DTVF]). RESULTS Twenty-four patients with a mean age of 76.1 ± 7.8 years were included in this study. Objective responder status moderately coincided with the self-estimation of the patients with subjective high false-positive results (83%). The extent of improvement was greater for single-task walking than for dual-task walking (p < 0.05). Significant increases in walking speed were found at TP2 for STPS (p = 0.042) and DTVF (p = 0.046) and at TP3 for STPS (p = 0.035), DTS7 (p = 0.042), and DTVF (p = 0.044). Enlargement of the ventricles (Evans Index) positively correlated with early improvement. Gait improvement at TP3 correlated with the shunt response in 18 patients. CONCLUSIONS Quantitative gait assessment in iNPH is important due to the poor self-evaluation of the patients. The maximal increase in gait velocity can be observed 24-48 hours after the LP. This time point is also best to predict the response to shunting. For dual-task paradigms, maximal improvement appears to occur later (48 to 72 hours). Assessment of gait should be performed at Day 2 or 3 after LP.

Automated classification of neurological disorders of gait using spatio-temporal gait parameters.

OBJECTIVE: Automated pattern recognition systems have been used for accurate identification of neurological conditions as well as the evaluation of the treatment outcomes. This study aims to determine the accuracy of diagnoses of (oto-)neurological gait disorders using different types of automated pattern recognition techniques. METHODS: Clinically confirmed cases of phobic postural vertigo (N = 30), cerebellar ataxia (N = 30), progressive supranuclear palsy (N = 30), bilateral vestibulopathy (N = 30), as well as healthy subjects (N = 30) were recruited for the study. 8 measurements with 136 variables using a GAITRite(®) sensor carpet were obtained from each subject. Subjects were randomly divided into two groups (training cases and validation cases). Sensitivity and specificity of k-nearest neighbor (KNN), naive-bayes classifier (NB), artificial neural network (ANN), and support vector machine (SVM) in classifying the validation cases were calculated. RESULTS: ANN and SVM had the highest overall sensitivity with 90.6% and 92.0% respectively, followed by NB (76.0%) and KNN (73.3%). SVM and ANN showed high false negative rates for bilateral vestibulopathy cases (20.0% and 26.0%); while KNN and NB had high false negative rates for progressive supranuclear palsy cases (76.7% and 40.0%). CONCLUSIONS: Automated pattern recognition systems are able to identify pathological gait patterns and establish clinical diagnosis with good accuracy. SVM and ANN in particular differentiate gait patterns of several distinct oto-neurological disorders of gait with high sensitivity and specificity compared to KNN and NB. Both SVM and ANN appear to be a reliable diagnostic and management tool for disorders of gait.