Can Evoked Potential Changes during the Superficial Temporal Artery-Middle Cerebral Artery Bypass Surgery Predict Postoperative Improvement of Cerebral Perfusion and Functional Status?

BACKGROUND: We investigated evoked potential (EP) changes during superficial temporal artery to middle cerebral artery (STA-MCA) bypass surgery and their correlations with imaging and clinical findings postoperatively. METHODS: This retrospective study included patients who underwent STA-MCA bypass surgery due to ischemic stroke with large artery occlusion (MB group). Patients who underwent unruptured MCA aneurysm clipping were enrolled in the control group (MC group). Median and tibial somatosensory evoked potentials (SSEP), and motor evoked potentials recorded from the abductor pollicis brevis (APB-MEP) and abductor hallucis (AH-MEP) were measured intraoperatively. Modified Rankin scale (mRS) and perfusion-weighted imaging (PWI) related variables, i.e., mean transit time (MTT) and time to peak (TTP), were assessed. RESULTS: Δmedian SSEP, ΔAPB-MEP, and ΔAH-MEP were significantly higher in the MB group than in the MC group (p = 0.027, p = 0.006, and p = 0.015, respectively). APB-MEP and AH-MEP amplitudes were significantly increased at the final measurement (p = 0.010 and p < 0.001, respectively). The ΔTTP asymmetry index was moderately correlated with ΔAPB-MEP (r = 0.573, p = 0.005) and ΔAH-MEP (r = 0.617, p = 0.002). ΔAPB-MEP was moderately correlated with ΔMTT (r = 0.429, p = 0.047) and ΔmRS at 1 month (r = 0.514, p = 0.015). CONCLUSIONS: MEP improvement during STA-MCA bypass surgery was partially correlated with PWI and mRS and could reflect the recovery in cerebral perfusion.

Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea.

BACKGROUND: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. METHODS: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. RESULTS: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. CONCLUSIONS: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

Usefulness of Intraoperative Neurophysiological Monitoring During the Clipping of Unruptured Intracranial Aneurysm: Diagnostic Efficacy and Detailed Protocol.

Background: Intraoperative neurophysiological monitoring (IONM) has been widely applied in brain vascular surgeries to reduce postoperative neurologic deficit (PND). This study aimed to investigate the effect of IONM during clipping of unruptured intracranial aneurysms (UIAs). Methods: Between January 2013 and August 2020, we enrolled 193 patients with 202 UIAs in the N group (clipping without IONM) and 319 patients with 343 UIAs in the M group (clipping with IONM). Patients in the M group were intraoperatively monitored for motor evoked potentials (MEPs) and somatosensory evoked potentials (SSEPs). Irreversible evoked potential (EP) change was defined as EP deterioration that did not recover until surgery completion. Sustained PND was defined as neurological symptoms lasting for more than one postoperative month. Results: Ten (3.1%) and 13 (6.7%) in the M and N groups, respectively, presented with PND. Compared with the N group, the M group had significantly lower occurrence rates of sustained PND [odds ratio (OR) = 0.36, 95% confidence interval (CI) = 0.13-0.98, p = 0.04], ischemic complications (OR = 0.39, 95% CI = 0.15-0.98, p = 0.04), and radiologic complications (OR = 0.40, 95% CI = 0.19-0.82, p = 0.01). Temporary clipping was an independent risk factor for ischemic complications (ICs) in the total patient group (OR = 6.18, 95% CI = 1.75-21.83, p = 0.005), but not in the M group (OR = 5.53, 95% CI = 0.76-41.92, p = 0.09). Regarding PND prediction, considering any EP changes (MEP and/or SSEP) showed the best diagnostic efficiency with a sensitivity of 0.900, specificity of 0.940, positive predictive value of 0.321, negative predictive value (NPV) of 0.997, and a negative likelihood ratio (LR) of 0.11. Conclusion: IONM application during UIA clipping can reduce PND and radiological complications. The diagnostic effectiveness of IONM, specifically the NPV and LR negative values, was optimal upon consideration of changes in any EP modality.