Abstract

Mechanical thrombectomy (MT) is usually recommended for acute ischemic stroke (AIS) due to large vessel occlusion (LVO) within the time window (6 hours after the disease onset). However, poor prognosis in acute great vascular occlusive stroke after MT, which is not an uncommon occurrence, can be attributed to an absence of appropriate postoperative monitoring. Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) offer the advantages of fast, convenient, and bedside examinations compared with conventional imaging techniques. We aimed to analyze the predictive performance of clinical factors, Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) for the prognosis of patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) at 90 days after discharge. Patients achieved revascularization through MT performed within 6 hours after the onset of AIS due to LVO were included. We use the data to build four predictive models of prognosis and compared the predictive performance measured by the area under the curve, sensitivity, and specificity. A total of 74 patients were included in the study. Among them, 47 patients had a poor prognosis (63.5%) on discharge, and 45 patients had a poor prognosis (60.8%) at 90 days after discharge. Independent predictors of poor prognosis at 90 days after discharge were identified as follows: age, NIHSS score on admission, PI on the affected/healthy side, and RAP. Among the four models built, AUC was the highest (reaching 0.831) when age was combined with NIHSS score on admission, TCD parameters (VD on the affected side, PI on the affected/healthy side), and QEEG parameter (RAP) for prognostic prediction. However, AUC of the four predictive models did not differ significantly (P>0.05). Age, NIHSS score on admission, TCD parameters, and QEEG parameter were independent predictors of the prognosis at 90 days after discharge in patients receiving MT for AIS due to LVO in the anterior circulation. The model combining the above four parameters may be helpful for prognostic prediction in such patients.

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