Patient's assessment and prediction of recovery after stroke: a roadmap for clinicians.
Silvia Salvalaggio, Leonardo Boccuni, Andrea Turolla
PMID 37337288WHAT IT FOUND
A stepwise guide to predicting stroke recovery using tools like PREP2 for arm function and TWIST for walking.
These algorithms help set realistic goals but require clinical judgement, as long-term accuracy is only established for PREP2.
Key findings
01The PREP2 algorithm is the only validated predictive model for upper limb recovery that has established long-term prediction accuracy and impact on clinical care.
02The TWIST algorithm predicts time to walk independently using trunk control and hip extension strength assessed at one week, with high accuracy for specific patient profiles.
03Only 9% of physiotherapists and occupational therapists use prognostic tools in practice, despite 89% acknowledging their importance.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTsWhat it means for SLPs
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What it does not show
This is a narrative review, not a systematic review or meta-analysis, so it may not capture all available evidence. Long-term prediction accuracy and clinical impact have only been established for the PREP2 algorithm; other tools lack this level of validation. Statistical accuracy and power for many prognostic tools have not been fully validated, requiring reliance on clinical judgement. The review does not report new patient data or outcomes from a specific trial.
Declared interests
No specific conflicts of interest or funding declarations are provided in the text.
The easy way to misread this
Do not treat these prognostic tools as definitive predictions of individual patient outcomes. The paper states that statistical accuracy and power for most tools have not been fully validated, and clinical judgement must balance their use.