ArticleJournal of neurology, neurosurgery, and psychiatry2021
Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: the next step.
Article in Journal of neurology, neurosurgery, and psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Motor rehabilitation after stroke: European Stroke Organisation (ESO) consensus-based definition and guiding framework.European stroke journal · 2023Guideline
- Association between lesion location and sensorimotor rhythms in stroke - a systematic review with narrative synthesis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023Pooled it
- A Sensor-Based Feedback Device Stimulating Daily Life Upper Extremity Activity in Stroke Patients: A Feasibility Study.Sensors (Basel, Switzerland) · 2023Trial
- Performing a shortened version of the Action Research Arm Test in immersive virtual reality to assess post-stroke upper limb activity.Journal of neuroengineering and rehabilitation · 2022Trial
- Associations Between Early Functional Brain Network Topology and 6-Month Upper Limb Motor Outcomes in Stroke Patients.Translational stroke research · 2026Article
- Can machine learning improve on the early prediction of upper limb recovery after stroke?Journal of neuroengineering and rehabilitation · 2025Article
- Article
- How do clinicians use, experience, and value applications of outcome information in daily care? A mixed-methods study.Journal of rehabilitation medicine · 2025Article
- External Validation of a Dynamic Prediction Model for Upper Limb Function After Stroke.Archives of rehabilitation research and clinical translation · 2024Article
- Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model.Journal of neuroengineering and rehabilitation · 2023Article
- Rasch validation of a new scale to measure dependency in arm use in daily life: the Upper Limb Lucerne ICF-based Multidisciplinary Observation Scale.Frontiers in neurology · 2023Article
- Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform.Frontiers in neurology · 2023Article
- Early prediction of upper limb functioning after stroke using clinical bedside assessments: a prospective longitudinal study.Scientific reports · 2022Article
- Development of a patient decision aid for discharge planning of hospitalized patients with stroke.BMC neurology · 2022Article
- Measurement Feedback System for Intensive Neurorehabilitation after Severe Acquired Brain Injury.Journal of medical systems · 2022Article
- Relationship between intensity and recovery in post-stroke rehabilitation: a retrospective analysis.Journal of neurology, neurosurgery, and psychiatry · 2022Article
- Investigating secondary white matter degeneration following ischemic stroke by modelling affected fiber tracts.NeuroImage. Clinical · 2022Article
- A prediction model for the decline in renal function in people with type 2 diabetes mellitus: study protocol.Diagnostic and prognostic research · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPredicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients.
methodsModel input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure.
resultsA model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments.
conclusionOur innovative dynamic model can predict real-time, patient-specific upper limb capacity recovery profiles up to 6 months poststroke. The model can use all available serially assessed data in a flexible way, creating a prediction at any desired moment poststroke, stand-alone or linked with an electronic health record system.
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