国家神经系统疾病临床医学研究中心数据管理与共享系统

发表杂志：journal of diabetes

发表类型: Article

影响因子(IF): 3

PMID: 39264001 Pubmed原文链接 全文下载

Stroke is a major cerebrovascular disease that causing high morbidity, disability and mortality. Type 2 diabetes mellitus, as a major risk factor for stroke, is often associated with poor outcomes and high recurrence rates. Glycated albumin (GA) is a blood indicator to assess blood glucose levels 2-4 weeks before onset and is closely associated with the development of diabetes. The aim of this study was to validate the predictive role of glycated albumin in post-stroke prognosis, including mortality, functional outcome, and stroke recurrence at discharge, 3-month, 6-month, and 12-month in China National Stroke Registry Ⅲ. Particularly, other covariates were included in our study to further determine if addition of GA to a model improves the accuracy of functional prediction.

The Kolmogorov–Smirnov test was conducted to detect the normal distribution of data. Continuous variables conforming to a normal distribution were presented as the means ± standard deviations (SDs) and were compared using ANOVA. Skewed continuous variable that did not exhibit a normal distribution were presented as medians with interquartile ranges (IQRs) and were compared using the Kruskal–Wallis U test. Categorical variables were shown as numbers (proportions) and were compared using the χ2-test or Fisher's exact test. Patients participating in this study were divided into four groups according to the quartiles of GA assessed on admission. The association of GA levels with stroke recurrence and mortality was estimated by using a multivariate Cox regression model. Multivariable logistic regression was conducted to assess the functional dependency indicated by mRS score. Three steps were preformed to adjust the covariates: The first model was adjusted for age and sex. In the second model, body mass index (BMI), smoking status, alcohol consumption and medical histories were added. In the third model, systolic blood pressure (SBP), diastolic blood pressure (DBP), the NIHSS score at admission, the mRS score, the TOAST classification (including five subtypes of ischemic stroke: large-artery atherosclerosis, cardioembolism, small-vessel occlusion, other determined etiologies, and undetermined causes), medications received during hospitalization (including antiplatelet therapy, anticoagulation treatment, antihypertensive treatment, and lipid-lowering drugs), and laboratory tests were further added. In addition, restricted cubic spline analysis was used to address the association. Furthermore, Kaplan–Meier analysis was used to address survival plots of time to stroke recurrence and death during the 3-months, 6-months, and 1-year follow-up period, and each group was compared by the log-rank test. Discrimination tests (C-statistics, net reclassification improvement [NRI] and integrated discrimination improvement [IDI]) were used to evaluate whether GA improved risk prediction for stroke adverse outcomes in the third model. the interaction of age (<65 or ≥65 years) and sex was examined by subgroup analysis, adjusted in the third model. Furthermore, we performed sensitivity analyses to validate the robustness of the results. The competing risk model was applied to assess the associations between GA levels and stroke adverse outcomes, with non-stroke death being regarded as a competing risk event.