The neutrophil-to-lymphocyte ratio (NLR), an inflammatory marker, plays an important role in the inflammatory mechanisms of the pathophysiology and progression of acute ischemic stroke (AIS). The aim of this study was to identify the potential factors associated with functional prognosis in AIS. A total of 303 AIS patients were enrolled in this study; baseline information of each participant, including demographic characteristics, medical history, laboratory data, and 90-day functional outcome, was collected. Multivariate logistic regression analysis revealed that NLR, systolic blood pressure (SBP) and National Institutes of Health Stroke Scale (NIHSS) score were found to be independent factors for poor functional outcomes. Receiver operating characteristic (ROC) curve analysis was performed to estimate the predictive value of the NLR for 90-day functional outcome, with the best predictive cutoff value being 3.06. In the multivariate logistic regression analysis, three models were constructed: Model 1, adjusted for age, sex, SBP, and TOAST classification (AUC = 0.694); Model 2, further adjusted for the NIHSS score at admission (AUC = 0.826); and Model 3, additionally adjusted for the NLR (AUC = 0.829). The NLR at admission was an independent predictor of 90-day prognosis in patients with AIS. The risk factors related to poor 90-day functional outcomes were higher SBP, higher NLR, and a greater NIHSS score.

Background: Malnutrition is associated with poor outcomes after stroke. However, the association between malnutrition and post-stroke depression (PSD) remains unelucidated. We aimed to explore the association between geriatric nutritional risk index (GNRI) and depression after ischemic stroke. Methods: In total, 344 patients with ischemic stroke were included in this analysis. The GNRI was calculated from serum albumin level, weight, and height at admission. Malnutrition was defined using the GNRI cutoff points. A lower GNRI score indicates an elevated nutritional risk. The outcome was depression, measured 14 days after ischemic stroke. Logistic regression models were used to estimate the association between the GNRI and risk of PSD. Results: A total of 22.9% developed PSD 14 days after stroke. The mean GNRI was 99.3 ± 6.0, and 53.8% of the patients had malnutrition. After adjusting for covariates, baseline malnutrition was not associated with risk of PSD (OR, 0.670; 95%CI, 0.370–1.213; p = 0.186). The restricted cubic splines revealed a U-shaped association between the GNRI and PSD. Compared to moderate GNRI, higher GNRI (OR, 2.368; 95%CI, 0.983–5.701; p = 0.085) or lower GNRI (OR, 2.226; 95%CI, 0.890–5.563; p = 0.087) did not significantly increase the risk of PSD. Conclusion: A low GNRI was not associated with an increased risk of depression after ischemic stroke.

Objectives: Cognitive impairment may affect one-third of stroke survivors. Cardiovascular risk factors and stroke severity were known to be associated with cognitive function after stroke. However, it is unclear whether cardiovascular risk factors directly affect cognition after stroke, indirectly affect cognition by changing stroke severity, or both. Moreover, the effect of a combination of hypertension and diabetes mellitus was conflicting. We aimed to investigate the multiple direct and indirect associations and inspire potential intervention strategies.Materials and methods: From February 2020 to January 2021, 350 individuals received cognitive tests within 7 days after incident stroke. Cognitive tests were performed using the Chinese version of the Mini-Mental State Examination (MMSE). A moderated mediation model was constructed to test the indirect associations between cardiovascular and demographic risk factors and cognition mediated through stroke severity, the direct associations between risk factors and cognition, and the moderating effects of hypertension and diabetes.Results: Age (estimate, −0.112), atrial fibrillation (estimate, −4.092), and stroke severity (estimate, −1.994) were directly associated with lower cognitive function after stroke. Vascular disease (estimate, 1.951) and male sex (estimate, 2.502) were directly associated with better cognition after stroke. Higher education level was associated with better cognition directly (estimate, 1.341) and indirectly (estimate, 0.227) through stroke severity. The combination of hypertension decreased the magnitude of the negative association between atrial fibrillation and cognition (estimate, from −4.092 to −3.580).Conclusion: This is the first Chinese study exploring the moderated and mediating associations between cardiovascular risk factors, stroke severity, and cognitive function after stroke. Age, female sex, and atrial fibrillation were directly associated with lower cognition after stroke. The combination of hypertension might have a positive effect on cognition.

Background: Stroke-associated pneumonia (SAP) remains a neglected area despite its high morbidity and mortality. We aimed to establish an easy-to-use model for predicting SAP.Methods: Two hundred seventy-five acute ischemic stroke (AIS) patients were enrolled, and 73 (26.55%) patients were diagnosed with SAP. T-test, Chi-square test and Fisher’s exact test were used to investigate the associations of patient characteristics with pneumonia and its severity, and multivariable logistic regression models were used to construct a prediction scale.Results: Three variables with the most significant associations, including age, NGT placement, and right cerebral hemisphere lesions combined with gender, were used to construct a stroke-associated pneumonia prediction scale with high accuracy (AUC = 0.93). Youden index of our SAP prediction model was 0.77. The sensitivity and specificity of our SAP prediction model were 0.89 and 0.88, respectively.Conclusion: We identified the best predictive model for SAP in AIS patients. Our study aimed to be as clinically relevant as possible, focusing on features that are routinely available. The contribution of selected variables is visually displayed through SHapley Additive exPlanations (SHAP). Our model can help to distinguish AIS patients of high-risk, provide specific management, reduce healthcare costs and prevent life-threatening complications and even death.

Background: Stroke has been the leading cause of death and disability in the world. Early recognition and treatment of stroke could effectively limit brain damage and vastly improve outcomes. This study aims to develop a highly accurate prediction model of stroke with a list of lifestyle behaviors and clinical characteristics to distinguish high-risk groups in the community-dwelling population.Methods: Participants in this longitudinal cohort study came from the community-dwelling population in Suzhou between November 2018 and June 2019. A total of 4,503 residents participated in the study, while stroke happened to 22 participants in the 2-year follow-up period. Baseline information of each participant was acquired and enrolled in this study. T-test, Chi-square test, and Fisher’s exact test were used to examine the relationship of these indexes with stroke, and a prediction scale was constructed by multivariate logistic regression afterward. Receiver operating characteristic analysis was applied to testify to the prediction accuracy.Results: A highly accurate prediction model of stroke was constructed by age, gender, exercise, meat and vegetarian diet, BMI, waist circumference, systolic blood pressure, Chinese visceral adiposity index, and waist-height ratio. Two additional prediction models for overweight and non-overweight individuals were formulated based on crucial risk factors, respectively. The stroke risk prediction models for community-dwelling and overweight populations had accuracies of 0.79 and 0.82, severally. Gender and exercise were significant predictors (χ² > 4.57, p < 0.05) in the community-dwelling population model, while homocysteine (χ² = 4.95, p < 0.05) was significant in the overweight population model.Conclusion: The predictive models could predict 2-year stroke with high accuracy. The models provided an effective tool for identifying high-risk groups and supplied guidance for improving prevention and treatment strategies in community-dwelling population.

Post-stroke cognitive impairment (PSCI) is a significant neurological complication, affecting up to one-third of stroke survivors. Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, significantly increasing the risk of ischemic stroke. Increasing evidence suggests that AF plays a pivotal role in exacerbating cognitive decline in stroke patients. This review integrates current clinical, imaging, and mechanistic findings to elucidate how AF-related strokes exacerbate cognitive decline through multiple overlapping pathways, including thromboembolism, neuroinflammation, atherosclerosis, cerebral hypoperfusion, cerebral small vessel disease, and silent infarctions. These processes collectively impair cerebrovascular integrity, induce neuronal damage, and accelerate brain aging. The review further evaluates the role of clinical and neuroimaging biomarkers as predictive tools and their utility in guiding therapeutic strategies. By integrating insights of the latest researches, we aim to provide a comprehensive framework for alleviating cognitive decline in patients with AF-related stroke and highlight future research directions.