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Early Detection of Unsuccessful Blood Flow Restoration After Stroke Treatment Using Cerebral Autoregulation Monitoring During Thrombectomy A Multicenter Observational Study

Led by Beijing Shijitan Hospital, Capital Medical University · Updated on 2026-05-06

129

Participants Needed

4

Research Sites

N/A

Total Duration

AI-Summary

What this Trial Is About

Researchers are investigating whether monitoring cerebral autoregulation during thrombectomy can serve as an early sign to predict futile recanalization in patients with acute ischemic stroke. This multicenter, prospective, observational diagnostic accuracy study focuses on patients undergoing thrombectomy for large-vessel occlusion stroke, aiming to improve early identification to guide personalized treatment strategies and potentially reduce adverse outcomes. Participants undergoing thrombectomy will have their cerebral autoregulation assessed intraoperatively using near-infrared spectroscopy NIRS combined with continuous blood pressure monitoring. This monitoring is part of routine care and helps analyze cerebral oxygen saturation and arterial blood pressure to evaluate autoregulation status. The study includes a modeling cohort and a validation cohort to develop and confirm a predictive model for futile recanalization. Participants will be evaluated face-to-face at approximately 24, 48 hours, and 7 days after enrollment or discharge, with a follow-up assessment conducted by telephone or in person at around 90 days. The primary outcomes measured are the sensitivity, specificity, and accuracy of cerebral autoregulation impairment in predicting futile recanalization, based on functional independence scores. Secondary outcomes include neurological improvement assessments at one day post-treatment, with ongoing safety and functional evaluations throughout the study period.

CONDITIONS

Brief Title

Ultra-early Identification of Futile Recanalization After Reperfusion Therapy in Acute Ischemic Stroke Based on Cerebral Autoregulation

Research Team

Z

Zidong Li

S

Shen Li

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