Development and External Validation of a Motor Intention–Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke.
To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE. A prospective longitudinal multicenter cohort s...
| Publicado en: | Archives of Physical Medicine & Rehabilitation Vol. 106; no. 2; pp. 206 - 216 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
W B Saunders
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182480501&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182480501 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039993 1XL jtl: Archives of Physical Medicine & Rehabilitation issn: 00039993 maglogo: N pubinfo: dt: Feb2025 vid: 106 iid: 2 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 182480501 182480501 182480501 10.1016/j.apmr.2024.08.015 182480501 ppf: 206 ppct: 10 formats: tig: atl: Development and External Validation of a Motor Intention–Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke. aug: au: Hu, Chengpeng Ti, Chun Hang Eden Shi, Xiangqian Yuan, Kai Leung, Thomas W.H. Tong, Raymond Kai-Yu affil: Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China sug: subj: Instrument Construction Instrument Validation Psychometrics Evaluation Prediction Models Upper Extremity Physiology Motor Skills Evaluation Stroke Rehabilitation Hand Physiology Robotics Clinical Assessment Tools Human Prospective Studies Validation Studies Multicenter Studies Regression Descriptive Statistics ROC Curve ab: To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE. A prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects' motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated. Nine rehabilitation centers. Chronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample. All participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk). Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy. The best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases. This study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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