Sorbonne Université, Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225.
AP-HP, Urgences Cérébro-Vasculaires.
Curr Opin Neurol. 2020 Aug;33(4):482-487. doi: 10.1097/WCO.0000000000000843.
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.
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.
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.
本文回顾了 2017-2019 年期间预测运动性卒中恢复的实用和前沿方法。重点关注临床评分和生物标志物（包括经颅磁刺激（TMS）和 MRI）以及更具创新性的替代方法的预测价值。
临床评分结合 TMS 诱导的运动诱发电位（MEP）和 MRI 评估的皮质脊髓束（CST）完整性，可预测运动恢复的准确性约为 75%。因此，仍然需要研究新的生物标志物，以提高这些模型的准确性。
到目前为止，对于应该在临床常规中使用哪些预测模型还没有共识。决策树，如 PREP2 算法，可能是将预测模型从基础转化为临床应用的最简单方法。然而，仍需要外部验证来实施当前的模型。
