Predicting activities of daily living at discharge in stroke patients using rehabilitation robot training-induced functional connectivity

I don't consider any prediction of recovery useful at all. You're supposed to deliver EXACT RECOVERY PROTOCOLS AS SURVIVORS NEED! This is fucking useless for survivors! You're fired!

'Assessments' don't get you recovered, only EXACT PROTOCOLS DO! SURVIVORS WANT RECOVERY! GET THERE!

I'd fire everyone involved with this crapola! You're 'assessing' based on the failure of the status quo! Change the status quo, you blithering idiots!

 Predicting activities of daily living at discharge in stroke patients using rehabilitation robot training-induced functional connectivity

ABSTRACT

Background

Predicting activities of daily living (ADL) in stroke patients optimizes discharge planning, which relies on accurate functional assessment. Recent studies have shown that functional connectivity (FC) of brain networks induced by upper extremity rehabilitation robotic training (UE-RAT) effectively reflects functional status, but its prognostic value for ADL remains unclear.

Objective

Utilize functional near-infrared spectroscopy (fNIRS) to measure FC during UE-RAT and develop machine learning models to evaluate the predictive value of task-FC for ADL.

Methods

This study recruited 86 patients with subacute stroke. Activation and FC features of key brain regions, such as the superior frontal cortex (SFC) and primary motor cortex (M1), were measured in the resting state and during UE-RAT using fNIRS. Concurrently, 38 clinical features were collected. With modified Barthel Index (mBI) ≥75 at discharge as the prediction target, machine learning algorithms such as artificial neural network (ANN) were used to construct resting-state fNIRS model, task-state fNIRS model, clinical model, and combined model, and analyze the importance of the predictor variables based on the Shapley additive interpretation (SHAP).

Results

The combined model constructed by combining clinical and task-state fNIRS features had the best predictive performance (AUC_mean: 0.955, 95% CI: 0.948–0.962). Higher connectivity between the ipsilateral premotor cortex (iPMC) and primary motor cortex (iM1) during the task state, along with higher mBI scores and lower mRS scores, predict significant improvement in functional independence for patients.

Conclusions

UE-RAT induced FC can be a valid biomarker for mBI prediction and can improve the accuracy of rehabilitation prediction.

Prognostic accuracy of the Stroke Rehabilitation Assessment of Movement (STREAM) scores on admission for walking independence in stroke patients at discharge and one-month follow-up

 

I don't consider any prediction of recovery useful at all. You're supposed to deliver EXACT RECOVERY PROTOCOLS AS SURVIVORS NEED! This is fucking useless for survivors! You're fired!

Prognostic accuracy of the Stroke Rehabilitation Assessment of Movement (STREAM) scores on admission for walking independence in stroke patients at discharge and one-month follow-up

•  Thamonwan Kirdthongkham,
• Maria Justine,
• Akkradate Siriphorn

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• Published: March 7, 2025
• https://doi.org/10.1371/journal.pone.0319682

Abstract

Gait prediction is critical in optimizing rehabilitation strategies for stroke survivors. This study evaluates the prognostic utility of the Stroke Rehabilitation Assessment of Movement (STREAM) scores, recorded at admission, for predicting walking ability at discharge and one-month follow-up. We assessed 47 stroke patients using STREAM at admission; walking independence was defined using two criteria: a Functional Ambulation Category (FAC) score >  3 and a 10-Meter Walk Test (10-MWT) speed ≥  0.4 m/s. The predictive validity of STREAM scores was analyzed using the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, and cut-off values were computed. The analysis revealed that a STREAM score above 38 at admission significantly predicted independent gait by discharge, evidenced by a high AUC of 0.897. At the one-month follow-up, a cut-off score of 29 continued to predict walking independence, with an AUC of 0.987. The subscores further enhanced predictive accuracy and highlighted the effectiveness of the STREAM assessment as a robust predictor of independent walking in stroke patients. These findings suggest the practicality of using STREAM scores to predict walking independence, which can guide the planning of more effective rehabilitation interventions. Trial registration TCTR20240323004 at www.thaiclinicaltrials.org.

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Citation: Kirdthongkham T, Justine M, Siriphorn A (2025) Prognostic accuracy of the Stroke Rehabilitation Assessment of Movement (STREAM) scores on admission for walking independence in stroke patients at discharge and one-month follow-up. PLoS ONE 20(3): e0319682. https://doi.org/10.1371/journal.pone.0319682

Editor: Jyotindra Narayan, Imperial College London, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Received: October 10, 2024; Accepted: February 6, 2025; Published: March 7, 2025

Copyright: © 2025 Kirdthongkham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.