Trends in stroke-related journals: Examination of publication patterns using topic modeling.

OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. MATERIALS AND METHODS: Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. RESULTS: Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. CONCLUSIONS: The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study.

Background: While numerous prognostic factors have been reported for large vessel occlusion (LVO)-acute ischemic stroke (AIS) patients, the same cannot be said for distal medium vessel occlusions (DMVOs). We used machine learning (ML) algorithms to develop a model predicting the short-term outcome of AIS patients with DMVOs using demographic, clinical, and laboratory variables and baseline computed tomography (CT) perfusion (CTP) postprocessing quantitative parameters. Methods: In this retrospective cohort study, consecutive patients with AIS admitted to two comprehensive stroke centers between January 1, 2017, and September 1, 2022, were screened. Demographic, clinical, and radiological data were extracted from electronic medical records. The clinical outcome was divided into two categories, with a cut-off defined by the median National Institutes of Health Stroke Scale (NIHSS) shift score. Data preprocessing involved addressing missing values through imputation, scaling with a robust scaler, normalization using min-max normalization, and encoding of categorical variables. The data were split into training and test sets (70:30), and recursive feature elimination (RFE) was employed for feature selection. For ML analyses, XGBoost, LightGBM, CatBoost, multi-layer perceptron, random forest, and logistic regression algorithms were utilized. Performance evaluation involved the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), the area under these curves, accuracy, precision, recall, and Matthews correlation coefficient (MCC). The relative weights of predictor variables were examined using Shapley additive explanations (SHAP). Results: Sixty-nine patients were included and divided into two groups: 35 patients with favorable outcomes and 34 patients with unfavorable outcomes. Utilizing ten selected features, the XGBoost algorithm achieved the best performance in predicting unfavorable outcomes, with an area under the ROC curve (AUROC) of 0.894 and an area under the PRC curve (AUPRC) of 0.756. The SHAP analysis ranked the following features in order of importance for the XGBoost model: mismatch volume, time-to-maximum of the tissue residue function (Tmax) >6 s, diffusion-weighted imaging (DWI) volume, neutrophil-to-platelet ratio (NPR), mean corpuscular volume (MCV), Tmax >10 s, hemoglobin, potassium, hypoperfusion index (HI), and Tmax >8 s. Conclusions: Our ML models, trained on baseline quantitative laboratory and CT parameters, accurately predicted the short-term outcome in patients with DMVOs. These findings may aid clinicians in predicting prognosis and may be helpful for future research.

Assessing the Efficacy of Mechanical Thrombectomy in Patients with an NIHSS < 6 Presenting with Proximal Middle Cerebral Artery Vessel Occlusion as Compared to Best Medical Management.

BACKGROUND AND PURPOSE: Minor acute ischemic stroke (AIS) patients-defined by an NIHSS score < 6-presenting with proximal middle cerebral artery large vessel occlusions (MCA-LVO) is a subgroup for which treatment is still debated. Although these patients present with minor symptoms initially, studies have shown that several patients afflicted with MCA-LVO in this subgroup experience cognitive and functional decline. Although mechanical thrombectomy (MT) is the standard of care for patients with an NIHSS score of 6 or higher, treatment in the minor stroke subgroup is still being explored. The purpose of this preliminary study is to report our center's experience in evaluating the potential benefit of mechanical thrombectomy (MT) in minor stroke patients when compared to medical management (MM). METHODS: We performed a retrospective study with two comprehensive stroke centers within our hospital enterprise of consecutive patients presenting with minor AIS secondary to MCA-LVO (defined as M1 or proximal M2 segments of MCA). We subsequently evaluated patients who received MT versus those who received MM. RESULTS: Between January 2017 and July 2021, we identified 46 AIS patients (11 treated with MT and 35 treated with MM) who presented with an NIHSS score < 6 secondary to MCA-LVO (47.8% 22/46 female, mean age 62.3 years, range 49-75 years). MT was associated with a significantly lower mRS at 90 days (median: 1.0 [IQR 0.0-2.0] versus 3.0 [IQR 1.0-4.0], p = <0.001), a favorable NIHSS shift (-4.0 [IQR -10.0--2.0] versus 0.0 [IQR -2.0-1.0], p = 0.002), favorable NIHSS shift dichotomization (5/11, 45.5% versus 3/35, 8.6%, p = 0.003) and favorable mRS dichotomization (7/11, 63.6% versus 14/35, 40.0%, p = 0.024). CONCLUSIONS: In our center's preliminary experience, for AIS patients presenting with an NIHSS score < 6 secondary to MCA-LVO, MT may be associated with improved clinical outcomes when compared to MM only.

Arterial Spin Labeling Imaging Characteristics of Anti-leucine-rich Glioma-Inactivated 1 Encephalitis: A Qualitative and Quantitative Analysis.

Background and Significance: Autoimmune encephalitis (AE) is a rare group of diseases that can present with stroke-like symptoms. Anti-leucine-rich glioma inactivated 1 (LGI1) encephalitis is an AE subtype that is infrequently associated with neoplasms and highly responsive to prompt immunotherapy treatment. Therefore, accurate diagnosis of LGI1 AE is essential in timely patient management. Neuroimaging plays a critical role in evaluating stroke and stroke mimics such as AE. Arterial Spin Labeling (ASL) is an MRI perfusion modality that measures cerebral blood flow (CBF) and is increasingly used in everyday clinical practice for stroke and stroke mimic assessment as a non-contrast sequence. Our goal in this preliminary study is to demonstrate the added value of ASL in detecting LGI1 AE for prompt diagnosis and treatment. Methods: In this retrospective single center study, we identified six patients with seropositive LGI1 AE who underwent baseline MRI with single delay 3D pseudocontinuous ASL (pCASL), including five males and one female between ages 28 and 76 years, with mean age of 55 years. Two neuroradiologists qualitatively interpreted the ASL images by visual inspection of CBF using a two-point scale (increased, decreased) when compared to both the ipsilateral and contralateral unaffected temporal and non-temporal cortex. The primary measures on baseline ASL evaluation were a) presence of ASL signal abnormality, b) if present, signal characterization based on the two-point scale, c) territorial vascular distribution, d) localization, and e) laterality. Quantitative assessment was also performed on postprocessed pCASL cerebral blood flow (CBF) maps. The obtained CBF values were then compared between the affected temporal cortex and each of the unaffected ipsilateral parietal, contralateral temporal, and contralateral parietal cortices. Results: On consensus qualitative assessment, all six patients demonstrated ASL hyperperfusion and corresponding FLAIR hyperintensity in the hippocampus and/or amygdala in a non-territorial distribution (6/6, 100%). The ASL hyperperfusion was found in the right hippocampus or amygdala in 5/6 (83%) of cases. Four of the six patients underwent initial follow-up imaging where all four showed resolution of the initial ASL hyperperfusion. In the same study on structural imaging, all four patients were also diagnosed with mesial temporal sclerosis (MTS). Quantitative assessment was separately performed and demonstrated markedly increased CBF values in the affected temporal cortex (mean, 111.2 ml/min/100 g) compared to the unaffected ipsilateral parietal cortex (mean, 49 ml/min/100 g), contralateral temporal cortex (mean, 58.2 ml/min/100 g), and contralateral parietal cortex (mean, 52.2 ml/min/100 g). Discussion: In this preliminary study of six patients, we demonstrate an ASL hyperperfusion pattern, with a possible predilection for the right mesial temporal lobe on both qualitative and quantitative assessments in patients with seropositive LGI1. Larger scale studies are necessary to further characterize the strength of these associations.