Objective To characterise the time window in which endovascular thrombectomy (EVT) is associated with good outcome, and to test the differential relationship between functional outcome and onset-to-reperfusion time (ORT), depending on collateral status. Methods This was a retrospective analysis of clinical and imaging data of 554 consecutive patients, who had recanalisation success by EVT for anterior circulation large artery occlusion, from the prospectively maintained registries of 16 comprehensive stroke centres between September 2010 and December 2015. The patients were dichotomised into good and poor collateral groups, based on CT angiography. We tested whether the likelihood of good outcome (modified Rankin Scale, 0–2) by ORT was different between two groups. Results ORT was 298 min±113 min (range, 81–665 min), and 84.5% of patients had good collaterals. Age, diabetes mellitus, previous infarction, National Institutes of Health Stroke Scale, good collaterals (OR 40.766; 95% CI 10.668 to 155.78; p<0.001) and ORT (OR 0.926 every 30 min delay; 95% CI 0.862 to 0.995; p=0.037) were independently associated with good outcome. The drop in likelihood of good outcome associated with longer ORT was significantly faster in poor collateral group (OR 0.305 for every 30 min; 95% CI 0.113 to 0.822) than in good collateral group (OR 0.926 for every 30 min; 95% CI 0.875 to 0.980). Conclusions Earlier successful recanalisation was strongly associated with good outcome in poor collateral group; however, this association was weak during the tested time window in good collateral group. This suggests that the ORT window for good outcome can be adjusted according to collateral status.

BACKGROUND: Infarct volume inversely correlates with good recovery in stroke. The magnitude and predictors of infarct growth despite successful reperfusion via endovascular treatment are not known. PURPOSE: We aimed to summarize the extent of infarct growth in patients with acute stroke who achieved successful reperfusion (TICI 2b–3) after endovascular treatment. DATA SOURCES: We performed a systematic review and meta-analysis by searching MEDLINE and Google Scholar for articles published up to October 31, 2020. STUDY SELECTION: Studies of >10 patients reporting baseline and post-endovascular treatment infarct volumes on MR imaging were included. Only patients with TICI 2b–3 were included. We calculated infarct growth at a study level as the difference between baseline and follow-up MR imaging infarct volumes. DATA ANALYSIS: Our search yielded 345 studies, and we included 10 studies reporting on 973 patients having undergone endovascular treatment who achieved successful reperfusion. DATA SYNTHESIS: The mean baseline infarct volume was 19.5 mL, while the mean final infarct volume was 37.5 mL. A TICI 2b reperfusion grade was achieved in 24% of patients, and TICI 2c or 3 in 76%. The pooled mean infarct growth was 14.8 mL (95% CI, 7.9–21.7 mL). Meta-regression showed higher infarct growth in studies that reported higher baseline infarct volumes, higher rates of incomplete reperfusion (modified TICI 2b), and longer onset-to-reperfusion times. LIMITATIONS: Significant heterogeneity among studies was noted and might be driven by the difference in infarct growth between early- and late-treatment studies. CONCLUSIONS: These results suggest considerable infarct growth despite successful endovascular treatment reperfusion and call for a faster workflow and the need for specific therapies to limit infarct growth.

Background and Objectives: A machine learning technique for identifying hidden coronary artery disease (CAD) might be useful. We developed and validated machine learning models to predict patients with hidden CAD and assess long-term outcomes in patients with acute ischemic stroke. Methods: Multidetector coronary computed tomography was performed for patients without known history of CAD. Primary outcomes were defined as having any degree of CAD and having obstructive CAD (≥50% stenosis). Demographic variables, risk factors, laboratory results, Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, NIH Stroke Scale score, blood pressure, and carotid artery stenosis were used to develop and validate machine learning models to predict CAD. Area under the receiver operating characteristic curves (AUC) was calculated for performance analysis, and Kaplan–Meier and Cox survival analyses of long-term outcomes were performed. Major adverse cardiovascular events (MACE) were defined as ischemic stroke, myocardial infarction, unstable angina, urgent coronary revascularization, and cardiovascular mortality. Results: Overall, 1,710 patients were included for the training dataset and 348 patients for the validation dataset. An Extreme Gradient Boosting model was developed to predict any degree of CAD, which showed an AUC of 0.763 (95% CI 0.711–0.814) on validation. A logistic regression model was used to predict obstructive CAD and had an AUC of 0.714 (95% CI 0.692–0.799). During the first 5 years of follow-up, MACE occurred more frequently when predicted of any CAD (P = 0.022) or obstructive CAD (P < 0.001). Cox proportional analysis showed that the hazard ratio of MACE was 1.5 (95% CI 1.1–2.2; P = 0.016) when predicted of any CAD, whereas it was 1.9 (95% CI 1.3–2.6; P < 0.001) for obstructive CAD. Discussion: We demonstrated that machine learning may help identify hidden CAD in patients with acute ischemic stroke. Long-term outcomes were also associated with prediction results. Classification of Evidence: This study provides Class II evidence that in patients with acute ischemic stroke with CAD risk factors but no known history of CAD, a machine learning model predicts CAD on multidetector coronary computed tomography with an AUC of 0.763 (95% CI 0.711-0.814).

This retrospective study investigated whether the volume or density of the thrombus is predictive of recanalization in stent retriever (SR) treatment. Consecutive patients treated with SR thrombectomy as the first endovascular modality were enrolled. The thrombus volume and density were measured on thin-section noncontrast computed tomography using 3-dimensional software. The patients were grouped by recanalization status and the number of SR passes. Among 165 patients, recanalization was achieved with the first pass in 68 (50.0%), 2–3 passes in 43 (31.6%), and ≥4 passes in 25 (18.4%) patients. The thrombus volume was smaller in patients with (107.5 mm³) than without (173.7 mm³, p = 0.025) recanalization, and tended to be larger with increasing number of passes (p for trend = 0.001). The thrombus volume was an independent predictor of first-pass recanalization (odds ratio 0.93 per 10 mm³, 95% confidence interval 0.89–0.97). However, the thrombus density was not associated with recanalization success. Recanalization within 3 passes was associated with a favorable outcome. In conclusion, the thrombus volume was significantly related to recanalization in SR thrombectomy. Measuring the thrombus volume was particularly predictive of first-pass recanalization, which was associated with a higher likelihood of a favorable outcome.

Objective: To evaluate whether patients with epilepsy were more susceptible to coronavirus disease 2019 (COVID-19) infection and at greater risk of severe complications when infected with COVID-19 when compared with patients without epilepsy. Methods: We included participants who underwent at least one SARS-CoV-2 real-time reverse transcription polymerase chain reaction test between Jan 1 and June 4, 2020 from the Korean nationwide COVID-19 dataset. Epilepsy was defined according to the presence of diagnostic code in health claims data prior to the COVID-19 diagnosis. To investigate the association between epilepsy and the susceptibility or severe complications of COVID-19, a 1:6 ratio propensity score matching (PSM) and logistic regression analysis were performed. Severe complications with COVID-19 infection were defined as a composite of the incidence of mechanical ventilation, intensive care unit admission, and death within two months after COVID-19 diagnosis. Results: Among 212,678 study participants who underwent COVID-19 test, 3,919 (1.8%) had a history of epilepsy. After PSM, there was no significant difference in COVID-19 PCR positivity according to epilepsy history [odds ratio (OR): 0.86, 95% confidence interval (CI) 0.67–1.11]. Of the 7,713 individuals with confirmed COVID-19 infection, 72 (0.9%) had a history of epilepsy. Among the COVID-19 patients, the severe complications occurred in 444 (5.8%) individuals. After PSM, the presence of epilepsy was associated with occurrence of severe complications after COVID-19 infection [OR: 2.05, 95% CI: 1.04–4.04]. Mortality following COVID-19 infection did not differ according to the presence of epilepsy history [OR: 1.55, 95% CI: 0.65–3.70]. Conclusions: The presence of epilepsy was not associated with increased susceptibility to COVID-19 infection or mortality related to the infection. However, there was an increased risk of severe complications with COVID-19 in patients with epilepsy; therefore, careful management and monitoring may be necessary.