Prediction of motor recovery after stroke: being pragmatic or innovative?
Charlotte Rosso, Jean‐Charles Lamy
Abstract
PURPOSE OF REVIEW: This review considers both pragmatic and cutting-edge approaches for predicting motor stroke recovery over the period 2017-2019. It focuses on the predictive value of clinical scores and biomarkers including Transcranial Magnetic Stimulation (TMS) and MRI as well as more innovative alternatives. RECENT FINDINGS: Clinical scores combined with corticospinal tract (CST) integrity as assessed by both TMS-induced motor-evoked potential (MEP) and MRI predict motor recovery with an accuracy of about 75%. Therefore, research on novel biomarkers is still needed to improve the accuracy of these models. SUMMARY: Up to date, there is no consensus about which predictive models should be used in clinical routine. Decision trees, such as the PREP2 algorithm are probably the easiest approach to operationalize the translation of predictive models from bench to bedside. However, external validation is still needed to implement current models.