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Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians. [ABSTRACT FROM AUTHOR]

Background/Objectives: There is a relative lack of specific research on registered nurse (RN) staffing in long-term care hospitals in the Republic of Korea. This study investigated the association between RN staffing levels and inpatient outcomes in long-term care hospitals in the Republic of Korea. Methods: Nationwide data of long-term care hospitals from the Health Insurance Review and Assessment Services website were used to analyze the association between registered nurse staffing levels and 7 inpatient outcome indicators. Results: The results indicated that in long-term care hospitals with higher RN staffing levels, there was an improvement in moderate-to-severe pain, activities of daily living enhancement, lower prevalence of indwelling catheters, reduction in the incidence of pressure ulcers, improvement in existing pressure ulcers, and increased rate of return to the community. Similar results were observed in analyses conducted according to disease classification groups. Conclusions: These findings suggest that increasing RN staffing levels can enhance patient safety and improve treatment outcomes. Furthermore, this study provided important foundational data for developing policies to optimize RN staffing in long-term care hospitals in the Republic of Korea. [ABSTRACT FROM AUTHOR]

ImportanceCerebrovascular disease (CVD) and Alzheimer disease are significant causes of cognitive impairment in the elderly. However, few studies have evaluated the relationship between CVD and β-amyloid burden in living humans or their synergistic effects on cognition. Thus, there is a need for better understanding of mild cognitive impairment (MCI) before clinical deterioration begins.ObjectiveTo determine the synergistic effects of β-amyloid burden and CVD on cognition in patients with subcortical vascular MCI (svMCI).Design, setting, and participantsA cross-sectional study was conducted using a hospital-based sample at a tertiary referral center. We prospectively recruited 95 patients with svMCI; 67 of these individuals participated in the study. Forty-five patients with amnestic MCI (aMCI) were group matched with those with svMCI by the Clinical Dementia Rating Scale Sum of Boxes.Main outcomes and measuresWe measured β-amyloid burden using positron emission tomography with carbon 11-labeled Pittsburgh Compound B (PiB). Cerebrovascular disease was quantified as white matter hyperintensity volume detected by magnetic resonance imaging fluid-attenuated inversion recovery. Detailed neuropsychological tests were performed to determine the level of patients' cognitive impairment.ResultsOn evaluation, 22 of the svMCI group (33%) and 28 of the aMCI group (62%) were found to be PiB positive. The mean PiB retention ratio was lower in patients with svMCI than in those with aMCI. In svMCI, the PiB retention ratio was associated with cognitive impairments in multiple domains, including language, visuospatial, memory, and frontal executive functions, but was associated only with memory dysfunction in aMCI. A significant interaction between PiB retention ratio and white matter hyperintensity volume was found to affect visuospatial function in patients with svMCI.Conclusions and relevanceMost patients with svMCI do not exhibit substantial amyloid burden, and CVD does not increase β-amyloid burden as measured by amyloid imaging. However, in patients with svMCI, amyloid burden and white matter hyperintensity act synergistically to impair visuospatial function. Therefore, our findings highlight the need for accurate biomarkers, including neuroimaging tools, for early diagnosis and the need to relate these biomarkers to cognitive measurements for effective use in the clinical setting.

BackgroundCurrently, intensive lipid lowering is recommended in patients with atherosclerotic ischemic stroke or transient ischemic attack. However, the role of statin in cardioembolic stroke is unclear. We investigated the association of statin with pretreatment collateral status in cardioembolic stroke.MethodsA collaborative study from two stroke centers in distinct geographic regions included consecutive patients with acute middle cerebral artery (MCA) infarction due to atrial fibrillation (AF) who underwent cerebral angiography. The relationship between pretreatment collateral grade and the use/dose of statin at stroke onset was assessed. The angiographic collateral grade was evaluated according to the ASITN/SIR Collateral Flow Grading System.ResultsNinety-eight patients (76 statin-naïve, 22 statin users) were included. Compared with statin-naïve patients, statin users were older and more frequently had hypertension, hyperlipidemia and coronary heart disease. Excellent collaterals (grade 3-4) were more frequently observed in statin users (11 patients, 50%) than in statin-naïve patients (21 patients, 27.6%; p = 0.049). The use of atorvastatin 10 mg equivalent or higher doses of statin was associated with excellent collaterals (p for trend = 0.025). In multiple regression analysis, prestroke statin use was independently associated with excellent collaterals (odds ratio, 7.841; 95% confidence interval, CI, 1.96-31.363; p = 0.004).ConclusionsPremorbid use of statin in AF patients is associated with excellent collateral flow. Although most statin trials excluded patients with cardioembolic stroke, our data suggests the possibility that statin may be beneficial in AF-related stroke