Histological structure of thrombi is a strong determinant of the outcome of vascular recanalization therapy, the only treatment option for acute ischemic stroke (AIS) patients. A total of 21 AIS patients from this study after undergoing non-enhanced CT scan and multimodal MRI were treated with mechanical stent-based and manual aspiration thrombectomy, and thromboembolic retrieved from a cerebral artery. Complementary histopathological and imaging analyses were performed to understand their composition with a specific focus on fibrin, von Willebrand factor, and neutrophil extracellular traps (NETs). Though distinct RBC-rich and platelet-rich areas were found, AIS patient thrombi were overwhelmingly platelet-rich, with 90% of thrombi containing <40% total RBC-rich contents (1.5 to 37%). Structurally, RBC-rich areas were simple, consisting of tightly packed RBCs in thin fibrin meshwork with sparsely populated nucleated cells and lacked any substantial von Willebrand factor (VWF). Platelet-rich areas were structurally more complex with thick fibrin meshwork associated with VWF. Plenty of leukocytes populated the platelet-rich areas, particularly in the periphery and border areas between platelet-rich and RBC-rich areas. Platelet-rich areas showed abundant activated neutrophils (myeloperoxidase⁺ and neutrophil-elastase⁺) containing citrullinated histone-decorated DNA. Citrullinated histone-decorated DNA also accumulated extracellularly, pointing to NETosis by the activated neutrophils. Notably, NETs-containing areas showed strong reactivity to VWF, platelets, and high-mobility group box 1 (HMGB1), signifying a close interplay between these components.

This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from January 2014 to July 2022. Various predictive factors were considered, such as stroke severity at presentation, patient demographics, laboratory results, admission location, and other clinical features. Random forest, logistic regression, XGboost, support vector machines, and decision trees were trained and evaluated. SHAP analysis was conducted to identify key predictors. The RF model demonstrated superior performance in predicting prognosis, while LR was more effective in predicting in-hospital mortality. The National Institute of Health Stroke Score (NIHSS) and admission location were key predictors. Despite its limitations, this research underscores the importance of advancing stroke registries and emphasizes the necessity for comprehensive external validation of predictive models. Furthermore, it demonstrates the importance of initial stroke severity in influencing patient outcomes and highlights the significance of admission to stroke units in reducing poor outcomes. This may help shape interventions to enhance stroke center capacities and influence strategic policies. This study contributes towards developing more precise predictive models for hemorrhagic stroke outcomes, potentially impacting clinical practice and optimizing resource allocation significantly.

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.

Studies assessing the burden of stroke in Qataris are limited. We aim to study stroke in the Qatari population. A retrospective review was undertaken of all Qatari adults presenting with stroke to Hamad Medical Corporation over a 5-year period. Descriptive statistics were used to summarize demographic and all other clinical characteristics of the patients. The primary outcome was the incidence of stroke in the Qatari patients. Comparison was made between the sexes. 862 patients were included, with 58.9% being male. The average incidence of stroke over the 5-year period was 92.04 per 100,000 adult Qatari population. The mean age of the cohort was 64.3±14.4 years, (range 19–105 years). The mean age of first ever cerebrovascular event was 63.2±14.5 years. The diagnosis was ischemic stroke in (73.7%), transient ischemic attack in (13.8%), intracerebral hemorrhage (ICH) in (11.6%), subarachnoid hemorrhage in (0.7%) and (0.2%) cerebral venous sinus thrombosis. Small vessel disease was the most common cause of ischemic stroke accounting for (46.5%), followed by large artery atherosclerosis (24.5%). Hypertension (82.7%) and diabetes (71.6%) were particularly prevalent in this cohort. Females were older (65.8±14.1 vs 63.4±14.5 years), had more hypertension and diabetes and more disability or death at 90 days (p<0.05) compared to Qatari males. Stroke occurs at a significantly lower age in Qataris compared to the western population. This study has uncovered sex differences that need to be studied further.

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.