The management of acute ischemic stroke primarily revolves around the timely restoration of blood flow (recanalization/reperfusion) in the occluded vessel and maintenance of cerebral perfusion through collaterals before reperfusion. Mechanical thrombectomy is the most effective treatment for acute ischemic stroke due to large vessel occlusions in appropriately selected patients. Judicious management of blood pressure before, during, and after mechanical thrombectomy is critical to ensure good outcomes by preventing progression of cerebral ischemia as well hemorrhagic conversion, in addition to optimizing systemic perfusion. While direct evidence to support specific hemodynamic targets around mechanical thrombectomy is limited, there is increasing interest in this area. Newer approaches to blood pressure management utilizing individualized cerebral autoregulation-based targets are being explored. Early efforts at utilizing machine learning to predict blood pressure treatment thresholds and therapies also seem promising; this focused review aims to provide an update on recent evidence around periprocedural blood pressure management after acute ischemic stroke, highlighting its implications for clinical practice while identifying gaps in current literature.

急性缺血性中风的管理主要围绕闭塞血管中血流的及时恢复（再通/再灌注）和再灌注前通过侧枝维持脑灌注。Mechanical thrombectomy is the most effective treatment for acute ischemic stroke due to large vessel occlusions in appropriately selected patients. 在机械血栓切除术之前、期间和之后明智地管理血压对于确保良好的结果至关重要除了优化全身灌注外，还可以防止脑缺血和出血转化的进展。虽然支持机械血栓切除术特定血流动力学目标的直接证据有限，但人们对该领域的兴趣越来越大。正在探索利用基于个体化脑自动调节目标的更新的血压管理方法。利用机器学习来预测血压治疗阈值和疗法的早期努力似乎也很有希望；这篇重点综述旨在提供有关急性缺血性卒中后围手术期血压管理的最新证据，强调其对临床实践的影响，同时找出当前文献中的空白。