RESUMEN
BACKGROUND: Endovascular thrombectomy (EVT) is a time-sensitive treatment for acute ischemic stroke with large vessel occlusion. To optimize transfer efficiency, a web-based platform was introduced in the Tainan Stroke Network (TSN). We assessed its application and effectiveness in regional stroke care. METHOD: This new web-based platform containing a questionnaire-style interface was introduced on October 1, 2021. To assess the transfer efficiency and patient outcomes, acute stroke patients transferred from PSCs to CSC for EVT from April 01, 2020, to December 30, 2022, were enrolled. The patients were classified into the traditional transferal pathway (TTP) group and the new transferal pathway (NTP) group depending on mode of transfer. Patient characteristics, time segments after stroke onset and outcome were compared between groups. RESULT: A total of 104 patients were enrolled, with 77 in the TTP group and 27 in the NTP group. Compared to the TTP group, the NTP group had a significantly shorter onset-to-CSC door time (TTP vs. NTP: 267 vs. 198 min; p = 0.041) and a higher EVT rate (TTP vs. NTP: 18.2% vs. 48.1%, p = 0.002). Among EVT patients, those in the NTP group had a significantly shorter CSC door-to-puncture time (TTP vs. NTP: 131.5 vs. 110 min; p = 0.029). The NTP group had a higher rate of good functional outcomes at 3 months (TTP vs. NTP: 21% vs. 61.5%; p = 0.034). CONCLUSION: This new web-based EVT transfer system provides notable improvements in clinical outcomes, transfer efficiency, and EVT execution for potential EVT candidates without markedly changing the regional stroke care paradigm.
RESUMEN
Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.