Activated platelets modulate immune responses. Platelet activation coincides with poststroke immunosuppression, so we hypothesized that platelet inhibition would mitigate immunosuppression and decrease the risk of infectious complications after stroke. In this study, we aimed to evaluate the contribution of platelet inhibition by antiplatelet agents to poststroke infection.

We performed a prospective cohort study of 975 patients with acute ischemic stroke to compare the differences in the risk of infection within 7 days after admission between aspirin alone, clopidogrel alone and aspirin plus clopidogrel. Multivariable Cox proportional hazards regression model was used to assess the association between antiplatelet therapy and poststroke infection.

Among 975 included patients, 578 received aspirin, 98 received clopidogrel, and 299 received both. A total of 113 patients experienced poststroke infection within 7 days after admission. The combination of aspirin and clopidogrel decreased the risk of poststroke infection compared with aspirin alone (hazard ratio [HR], 0.41; 95% confidence interval [CI], 0.22-0.77; P = 0.006), as compared with clopidogrel alone (HR, 0.46; 95% CI, 0.22-1.00; P = 0.050). We found no difference in infection risk between clopidogrel and aspirin. When assessing site-specific infections, a significant difference was observed only in the risk of pneumonia between dual antiplatelet therapy and aspirin alone.

Dual antiplatelet therapy with aspirin and clopidogrel is associated with decreased infection after stroke compared with aspirin or clopidogrel monotherapy. The findings support the net protective effect of aspirin and clopidogrel against poststroke infection.

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.

Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke.

Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model's outcomes and defined the influential predictive factors.

Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM).

The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation.

This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.

Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.

To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.

The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed.

Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839-0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively.

Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Stroke is a significant global health burden and ranks as the second leading cause of death worldwide.

This study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score.

The study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study's inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction.

The RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors.

The RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

(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.