PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke

Abstract

ObjectiveRecovery of motor function is important for regaining independence after stroke, but difficult to predict for individual patients. Our aim was to develop an efficient, accurate, and accessible algorithm for use in clinical settings. Clinical, neurophysiological, and neuroimaging biomarkers of corticospinal integrity obtained within days of stroke were combined to predict likely upper limb motor outcomes 3 months after stroke.MethodsData from 207 patients recruited within 3 days of stroke [103 females (50%), median age 72 (range 18–98) years] were included in a Classification and Regression Tree analysis to predict upper limb function 3 months poststroke.ResultsThe analysis produced an algorithm that sequentially combined a measure of upper limb impairment; age; the presence or absence of upper limb motor evoked potentials elicited with transcranial magnetic stimulation; and stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS score. The algorithm makes correct predictions for 75% of patients. A key biomarker obtained with transcranial magnetic stimulation is required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy.InterpretationThe new algorithm is more accurate, efficient, and accessible than its predecessors, which may support its use in clinical practice. While further work is needed to potentially incorporate sensory and cognitive factors, the algorithm can be used within days of stroke to provide accurate predictions of upper limb functional outcomes at 3 months after stroke. www.presto.auckland.ac.nz

Brain oscillatory activity as a biomarker of motor recovery in chronic stroke

“…Reappearance of the delicate excitatoryinhibitory balance in the thalamocortical circuits after stroke in the course of a learning process directly targeting this oscillatory mechanisms, clearly supports the neurophsyiosological logic of BMI strategies (Birbaumer & Cohen, 2007;Birbaumer, Elbert, Canavan, & Rockstroh, 1990). Stinear et al (2017) proved the performance of their sequential algorithm PREP2. It is based on clinical, neurophysiological and neuroimaging markers.…”

“…Stinear et al () proved the performance of their sequential algorithm PREP2. It is based on clinical, neurophysiological and neuroimaging markers.…”

Multimodal DTI-TMS assessment of the motor system in patients with chronic ischemic stroke

“…In patients whose RMT for an investigated muscle (separately for APB and EDC) was higher than 99% of the maximal stimulator output, the criterion was changed to eliciting MEP with 100% intensity with preactivation. MEP was considered as present (MEP+) when it was possible to obtain MEP higher than 50 µV in any of the tested muscle with a consistent latency in 4 out of 8 consecutive 6 trials during rest or attempted/imagined contraction of the affected and/or contralesional hand, which is similar to the approach described in the PREP2 algorithm 7 .…”

“…30 To our knowledge, it is the first time Fréchet distance is applied for analysis of ipsi-and contralesional FA CST profiles. 7 There is currently no consensus which CC areas are the most discriminative for stroke patients with motor deficit. [31][32][33] Thus, we decided to perform voxel-based comparison of FA maps between the best (Group1) and the worst recovered patients (Group3).…”

“…4,6 Despite being one of the most commonly used CST integrity parameters 3 , the method of detecting MEP presence is still not a unified one. 4,5,[7][8][9][10][11] It is still not clear, whether it is necessary to study more than one muscle and which hand muscles are the most informative. [12][13][14][15] Another possible important selection biomarker, for rehabilitation intervention choice, is the state of the contralesional motor areas and the excitability balance between the hemispheres.…”