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A Clinical Model predicting the 90-Day Prognosis after Mechanical Thrombectomy in Patients with Acute Ischemic
Yu Huang1,2, Chuyue Wu1,2,3, Cuiping Du1,2
1Department of Neurology, Chongqing University Three Gorges Hospital, Wanzhou, Chongqing, 400016, China.
Background And Purpose:
Mechanical Thrombectomy (MT) is the recommended treatment for patients with an acute ischemic stroke (AIS) due to large vessel occlusion (LVO) within 6 h after onset. However, the poor prognosis for patients with an acute great vascular occlusive stroke after an MT, which is a common occurrence, can be attributed to an absence of appropriate postoperative monitoring. Transcranial Doppler (TCD) ultrasound and Quantitative Electroencephalography (QEEG) offer the advantages of rapid, convenient, and bedside examinations compared to conventional imaging techniques.
Objective:
In the current study, we analyzed the predictive performance of clinical factors, TCD ultrasound, and QEEG for the prognosis of patients with an AIS due to LVO 90 days after hospital discharge.
Methods:
Patients who achieved revascularization following an MT that was performed within 6 h after the onset of AIS due to LVO were included. We used the data to build four predictive models of prognosis and compared the predictive performance measured by the area under the curve, sensitivity, and specificity.
Results:
Seventy-four patients were included in the study, among whom 47 had a poor prognosis (63.5%) at the time of hospital discharge, and 45 had a poor prognosis (60.8%) 90 d after hospital discharge. Independent predictors of poor prognosis 90 d after hospital discharge included the following: age, National Institute of Health stroke scale (NIHSS) score at the time of hospital admission, pulsatility index (PI) on the affected/healthy side, and relative alpha power (RAP). The area under the receiver operating characteristic curve (AUC) was highest (0.831) among the 4 models when age was combined with NIHSS score at the time of hospital admission, TCD parameters (diastolic velocity [VD] on the affected side and PI on the affected/healthy side), and a QEEG parameter (e.g., RAP) for prognostic prediction. However, the AUC for the 4 predictive models did not differ significantly (p > 0.05).
Conclusion:
Age, the NIHSS score at the time of hospital admission, TCD parameters, and a QEEG parameter were independent predictors of prognosis 90 d after discharge in patients undergoing 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.
