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Abstract This study aimed to investigate the association between non-traditional lipid profiles and the risk of 1-year vascular events in patients who were already using statins before stroke and had admission LDL-C

Abstract It is uncertain whether the prognostic power of white matter hyperintensity (WMH) on post-stroke outcomes is modulated as a function of initial neurological severity, a critical determinant of outcome after stroke. This multi-center MRI study tested if higher WMH quintiles were associated with 3-month poor functional outcome (modified Rankin Scale ≥ 3) for mild versus moderate-to-severe ischemic stroke. Mild and moderate-to-severe stroke were defined as admission National Institute of Health Stroke Scale scores of 1–4 and ≥ 5, respectively. Mean age of the enrolled patients (n = 8918) was 67.2 ± 12.6 years and 60.1% male. The association between WMH quintiles and poor functional outcome was modified by stroke severity (p-for-interaction = 0.008). In mild stroke (n = 4994), WMH quintiles associated with the 3-month outcome in a dose-dependent manner for the 2nd to 5th quintile versus the 1st quintile, with adjusted-odds-ratios (aOR [95% confidence interval]) being 1.29 [0.96–1.73], 1.37 [1.02–1.82], 1.60 [1.19–2.13], and 1.89 [1.41–2.53], respectively. In moderate-to-severe stroke (n = 3924), however, there seemed to be a threshold effect: only the highest versus the lowest WMH quintile was significantly associated with poor functional outcome (aOR 1.69 [1.29–2.21]). WMH burden aggravates 3-month functional outcome after mild stroke, but has a lesser modulatory effect for moderate-to-severe stroke, likely due to saturation effects.

Abstract We hypothesized that the association between BP and endovascular treatment (EVT) outcomes would differ by baseline perfusion and recanalization status. We identified 388 ICA or M1 occlusion patients who underwent EVT ≤ 24 h from onset with successful recanalization (TICI ≥ 2b). BP was measured at 5-min intervals from arrival and during the procedure. Systolic BPs (SBP) were summarized as dropmax (the maximal decrease over two consecutive measurements), incmax (the maximal increase), mean, coefficient of variation (cv), and standard deviation. Adequate baseline perfusion was defined as hypoperfusion intensity ratio (HIR) ≤ 0.5; infarct proportion as the volume ratio of final infarcts within the Tmax > 6 s region. In the adequate perfusion group, infarct proportion was closely associated with SBPdropmax (β ± SE (P-value); 1.22 ± 0.48, (

Abstract This study aimed to elucidate whether low-density lipoprotein cholesterol (LDL-C) levels differentially affect functional outcomes after intravenous thrombolysis (IVT) depending on prestroke statin use. Patients with acute ischemic stroke treated with IVT were categorized into low, intermediate, and high LDL-C groups based on LDL-C levels at admission ( 130 mg/dl, respectively). Multivariable logistic regression analyses were performed to explore the relationships between LDL-C and clinical outcomes (good outcomes at 3 months, modified Rankin Scale scores 0–2). The interaction between LDL-C levels and prestroke statin use regarding functional outcomes was investigated. Among the 4711 patients (age, 67 ± 12 years; males, 62.1%) who met the eligibility criteria, compared with the high LDL-C group, the low and intermediate LDL-C groups were not associated with good outcomes at 3 months according to the multivariable analysis. A potential interaction between the LDL-C group and prestroke statin use on good outcomes at 3 months was observed (P interaction = 0.07). Among patients with prestroke statin use, low (aOR 1.84 [1.04–3.26]) and intermediate (aOR 2.31 [1.20–4.47]) LDL-C groups were independently associated with a greater likelihood of having a 3-month good outcome. Our study showed that LDL-C was not associated with a 3-month good outcome, but prestroke statin use could modify the influence of LDL-C levels on functional outcomes after IVT.

Abstract Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models (k-prototype and semi-supervised clustering, SSC) and a conventional risk score (Stroke Prognostic Instrument-II, SPI-II) to obtain a distinct distribution of 1-year vascular events. Ultimately, 7,650 patients were included, and the 1-year primary outcome event occurred in 13.1%. The DLC-Kuiper UB model had a significantly higher C-index (0.674), log-rank score (153.1), and Brier score (0.08) than the other cluster models (SSC and DLC-MMD) and the SPI-II score. There were significant differences in primary outcome events among the 3 clusters (41.7%, 13.4%, and 6.5% in clusters 0, 1, and 2, respectively) when the DLC-Kuiper UB model was used. A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic stroke.

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.

Abstract Serotonin (5-HT) plays an important role in cerebrovascular homeostasis and psychiatric disorders, including suicidality. Methylation of the serotonin transporter gene (SLC6A4) is associated with 5-HT expression. However, the prognostic roles of SLC6A4 methylation and suicidal ideation (SI) in long-term outcomes of stroke have not been evaluated. We investigated the independent and interactive effects of SLC6A4 methylation and SI immediately after stroke on long-term outcomes. Blood SLC6A4 methylation status and SI based on the suicide item of the Montgomery–Åsberg Depression Rating Scale were assessed in 278 patients at 2 weeks after stroke. After the index stroke, cerebro-cardiovascular events by SLC6A4 methylation status and SI were investigated over an 8–14-year follow-up period and using Cox regression models adjusted for a range of covariates. SLC6A4 hypermethylation and SI within 2 weeks of stroke both predicted worse long-term outcomes, independent of covariates. A significant interaction effect of SI and the methylation status of CpG 4 on long-term stroke outcomes was also identified. The association between SLC6A4 methylation and long-term adverse outcomes may be strengthened in the presence of SI within 2 weeks after stroke. Evaluation of methylation and SI status during the acute phase can be helpful when assessing stroke patients.

Abstract We investigated a multicenter registry to identify estimated event rates according to CHA2DS2-VASc scores in patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). The additional effectiveness of antiplatelets (APs) plus oral anticoagulants (OACs) compared with OACs alone considering the CHA2DS2-VASc scores was also explored. This study retrospectively analyzed a multicenter stroke registry between Jan 2011 and Nov 2017, identifying patients with acute ischemic stroke with AF. The primary outcome event was a composite of recurrent stroke, myocardial infarction, and all-cause mortality within 1 year. A total of 7395 patients (age, 73 ± 10 years; men, 54.2%) were analyzed. The primary outcome events at one year ranged from 5.99% (95% CI 3.21–8.77) for a CHA2DS2-VASc score of 0 points to 30.45% (95% CI 24.93–35.97) for 7 or more points. After adjustments for covariates, 1-point increases in the CHA2DS2-VASc score consistently increased the risk of primary outcome events (aHR 1.10 [1.06–1.15]) at 1-year. Among OAC-treated patients at discharge (n = 5500), those treated with OAC + AP (vs. OAC alone) were more likely to experience vascular events, though among patients with a CHA2DS2-VASc score of 5 or higher, the risk of primary outcome in the OAC + AP group was comparable to that in the OAC alone group (Pint = 0.01). Our study found that there were significant associations of increasing CHA2DS2-VASc scores with the increasing risk of vascular events at 1-year in AIS with AF. Further study would be warranted.