Neutrophil-to-lymphocyte ratio associated with symptomatic saccular unruptured intracranial aneurysm.

BACKGROUND AND PURPOSE: Whether symptomatic unruptured intracranial aneurysms (UIAs) lead to change in circulating inflammation remains unclear. This study aims to evaluate the role of hematological inflammatory indicators in predicting symptomatic UIA. METHODS: Adult patients diagnosed with saccular intracranial aneurysm from March 2019 to September 2023 were recruited retrospectively. Clinical and laboratory data, including the white blood cells (WBC), neutral counts (NEUT), lymphocyte counts (LYM), and monocyte counts (MONO) of each patient, were collected. The neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were calculated as NLR = NEUT/LYM, LMR = LYM/MONO, SII = PLT*NEUT/LYM. The hematological inflammatory indicators were compared in symptomatic saccular and asymptomatic UIA patients. Multivariable logistic regression analyses were performed to explore the factors predicting symptomatic UIA. RESULTS: One hundred and fifty UIA patients with a mean age of 58.5 ± 12.4 were included, of which 68% were females. The NLR and LMR were significantly associated with symptomatic UIA, and the association remained in small UIAs (< 7 mm). The multiple logistic regression analysis showed that NLR was independently associated with symptomatic UIA. On ROC curve analysis, the optimal cutoff value of NLR to differentiate symptomatic from asymptomatic was 2.38. In addition, LMR was significantly associated with symptomatic UIA smaller than 7 mm. CONCLUSION: There was a significant correlation between NLR and symptomatic UIA. The NLR was independently associated with symptomatic UIA.

Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty.

Background: Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics. Methods: As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications. Results: It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score. Conclusion: This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

Qualitative and quantitative plaque enhancement on high-resolution vessel wall imaging predicts symptomatic intracranial atherosclerotic stenosis.

BACKGROUND AND PURPOSE: Intracranial atherosclerotic stenosis (ICAS) is a major cause of ischemic stroke (IS), and high-resolution vessel wall imaging (HR-VWI) can be used to assess the plaque characteristics of ICAS. This study aimed to qualitatively and quantitatively assess plaque enhancement of ICAS and to investigate the relationship between plaque enhancement, plaque morphological features, and IS. METHODS: Data from adult patients with ICAS from April 2018 to July 2022 were retrospectively collected, and all patients underwent HR-VWI examination. Plaque enhancement was qualitatively and quantitatively assessed, and the plaque-to-pituitary stalk contrast ratio (CR) indicated the degree of plaque enhancement. Plaque characteristics, such as plaque burden and area, were quantitatively measured using HR-VWI. Furthermore, receiver-operating characteristic (ROC) analysis was performed to assess the ability of CR to discriminate plaque enhancement. The patients were divided into a symptomatic ICAS group and an asymptomatic ICAS group according to the clinical and imaging characteristics. Univariate and multivariate analyses were performed to investigate which factors were significantly associated with plaque enhancement and symptomatic ICAS. The plaque burden and CR were compared using linear regression. RESULTS: A total of 91 patients with ICAS were enrolled in this study. ICAS plaque burden was significantly associated with plaque enhancement (p = .037), and plaque burden was linearly positively correlated with CR (R = 0.357, p = .001). ROC analysis showed that the cutoff value of CR for plaque enhancement was 0.56 (specificity of 81.8%). Both plaque enhancement and plaque burden were significantly associated with symptomatic ICAS, and only plaque enhancement was an independent risk factor after multivariate analysis. CONCLUSION: Plaque burden was an independent risk factor for plaque enhancement and showed a linear positive correlation with CR. The cutoff value of CR for plaque enhancement was 0.56, and CR ≥ 0.56 was significantly associated with symptomatic ICAS, which was independently associated with plaque enhancement.