Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward?

Ono K, Takahashi R, Morita K, Ara Y, Abe S, Ito S, Uno S, Abe M, Shirasaka T. Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward? Jpn J Compr Rehabil Sci 2024; 15: 1-7. Objective: This study aimed to develop a prediction model for walking independence in patients with stroke in the recovery phase at the time of hospital discharge using Prediction One, an artificial intelligence (AI)-based predictive analysis tool, and to examine its utility. Methods: Prediction One was used to develop a prediction model for walking independence for 280 patients with stroke admitted to a rehabilitation ward-based on physical and mental function information at admission. In 134 patients with stroke hospitalized during different periods, accuracy was confirmed by calculating the correct response rate, sensitivity, specificity, and positive and negative predictive values based on the results of AI-based predictions and actual results. Results: The prediction accuracy (area under the curve, AUC) of the proposed model was 91.7%. The correct response rate was 79.9%, sensitivity was 95.7%, specificity was 62.5%, positive predictive value was 73.6%, and negative predictive value was 93.5%. Conclusion: The accuracy of the prediction model developed in this study is not inferior to that of previous studies, and the simplicity of the model makes it highly practical.

Leg Cycling Leads to Improvement of Spasticity by Enhancement of Presynaptic Inhibition in Patients with Cerebral Palsy.

OBJECTIVE: The purpose of this study was to investigate if leg cycling could reduce lower extremity spasticity in patients with cerebral palsy (CP). In addition, we investigated whether the intervention could cause changes in the modulation of presynaptic inhibition. METHODS: This study was a quasi-experimental study, with pretest-posttest for 1 group. Participants in this experiment were eight adult patients with CP with lower extremity spasticity. Spasticity parameters assessed were the amplitude of soleus maximum Hoffmann's reflex (Hmax) and maximum angular velocity (MAV) of knee flexion measured using the pendulum test. D1 inhibition, which seems to be related to the presynaptic inhibition, was recorded by measuring soleus Hoffmann's reflex (H-reflex) with conditioned electric stimuli to the common peroneal nerve. RESULTS: D1 inhibition was significantly enhanced immediately by the cycling intervention. The amplitude of the soleus Hmax was significantly depressed, and there was significant difference in Hmax/maximum M-wave. The MAV was increased due to inhibition of the stretch reflex. CONCLUSION: Leg cycling suppressed stretch reflex and H-reflex, and caused plasticity of inhibitory circuits in patients with CP with lower extremity spasticity. These findings strongly suggest that lower extremity spasticity can be improved by cycling movements.