Objective: Post-stroke epilepsy (PSE) is associated with increased morbidity and mortality. Stroke-associated acute symptomatic seizures are an important risk factor: 20.8–34.3% of these patients will go on to develop PSE. Identifying these “high risk” individuals may result in earlier PSE diagnosis, treatment, and avoidance of seizure-related morbidity. This study was to identify predictors of PSE development in patients with stroke-associated acute symptomatic seizures.Participants and Methods: This was a retrospective cohort study of 167 patients with stroke-associated acute symptomatic seizures admitted to the Neurology Department of a tertiary Hospital of China, from 1 May 2006 to 30 January 2020. Both those with primary ischemic stroke and intracerebral hemorrhage were included in the study. Patient demographics, medical history, stroke-associated, and seizure-related variables were evaluated with univariable analysis and multivariable Cox regression analysis. PSE was defined as unprovoked seizures occurring > 7 days post-stroke. Data points were extracted from medical records and supplemented by tele-interview.Results: Of the 167 patients with stroke-associated acute symptomatic seizures, 49 (29.3%) developed PSE. NIHSS score > 14 [hazard ratio (HR) 2.98, 95% CI 1.57–5.67], longer interval from stroke to acute symptomatic seizures (days 4–7 post-stroke) (HR 2.51, 95% CI 1.37–4.59) and multiple acute symptomatic seizures (HR 5.08, 95% CI 2.58–9.99) were independently associated with PSE development. This association remained in the sub-analysis within the ischemic stroke cohort. In the sub-analysis of the hemorrhagic stroke cohort, multilobar involvement (HR 4.80, 95% CI 1.49–15.39) was also independently associated with development of PSE. Further, we developed a nomogram to predict individual risk of developing PSE following stroke-associated acute symptomatic seizures. The nomogram showed a C-index of 0.73.Conclusion: More severe neurofunctional deficits (NIHSS score > 14), longer interval from stroke to acute symptomatic seizures (days 4–7 post-stroke), and multiple acute symptomatic seizures were independently associated with development of PSE in patients with stroke-associated acute symptomatic seizures. This knowledge may increase clinical vigilance for development of PSE, facilitating rapid diagnosis and treatment initiation, and subsequently reduce seizure-related morbidity.

BackgroundDilated perivascular spaces (dPVS) are considered to be a type of cerebral small vessel disease (CSVD) as well as an important part of the glymphatic system. Although obesity has been shown to play a significant role in the development of CSVD, there are no studies addressing the correlation between obesity and dPVS. We aimed to study the relationship between abdominal fat distribution and dPVS in neurologically healthy cohorts.MethodsA total of 989 subjects, who were examined during a health examination project, were included in this study. We measured both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) areas using abdominal computed tomography. The dPVS scores were also evaluated in the basal ganglia (BG) and the centrum semiovale (CSO).ResultsIn a multivariate ordinal regression analysis, the relationship between VAT area and CSO-dPVS scores remained significant (β [95% confidence interval {CI} = 0.00003395] [0.00001074–0.00005716], P = 0.004), especially in male cohorts (β [95% CI] = 0.00004325 [0.00001772–0.00006878], P = 0.001) after adjusting for age; sex; and glucose, creatinine, uric acid, high-density lipoprotein, and low-density lipoprotein levels, while no association was found between SAT area and dPVS scores. The effects of quartile VAT area on CSO-dPVS were also significant in male cohorts (odds ratio [95% CI] = 1.33 [1.139 – 1.557], P < 0.001).ConclusionWe demonstrated a positive association between VAT and CSO-dPVS scores in a healthy cohort, which was more prominent in males.

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Background Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78–0.88], 0.87 (95% CI: 0.82–0.92), 0.91 (95% CI: 0.88–0.95), and 0.83 (95% CI: 0.77–0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59–0.89), 0.82 (95% CI: 0.69–0.94), 0.73 (95% CI: 0.58–0.88), and 0.76 (95% CI: 0.61–0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

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