Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Journal：npj Digital Medicine

Publication type: Original Contributions

IF(IF): 15.1

PMID: 41361520 Link To Pubmed Full text Download

Radiomics-derived brain age is a biological age based on cortical and subcortical brain measures, which is a more sensitive marker for brain health than chronological age.1 While chronological age is one of the most important predictive factors of post-stroke outcomes, little is known of the impact of the radiomics-derived brain age. We aimed to investigate the relationship between the radiomics-derived brain age and the clinical outcomes in patients with acute ischemic stroke or transient ischemic attack.

Baseline characteristics were compared across groups. Continuous variables with skewed distributions were reported as medians with IQRs, and categorical variables as counts and percentages. The Wilcoxon rank-sum test was used to compare continuous variables between two groups, and the Kruskal-Wallis test was applied for comparisons among three groups. Categorical variables were compared using the χ² test. Differences in BAG across baseline categorical variables were assessed using student’s t-test. Variables demonstrating statistically significant differences in BAG were subsequently included in a multivariable linear regression model to identify independent determinants of BAG. The relationship between BAG and clinical outcomes was evaluated using multivariable Cox proportional hazards models. Additionally, Fine-Gray competing risk models were employed to account for death as a competing event. Adjusted models included BAG-related variables and clinical features of the index event, such as admission diagnosis, NIHSS score, TOAST classification, and infarct volume. Sensitivity analysis was conducted by categorizing BAG as either a binary or three-level variable. When BAG was analyzed as a categorical variable in relation to clinical outcomes, multivariable models were adjusted for baseline characteristics that significantly differed between groups, as well as clinical features of the index event, including admission diagnosis, NIHSS score, TOAST classification. Given the exploratory nature of this study investigating the association between BAG and stroke recurrence, we did not apply multiple testing corrections to avoid obscuring potentially significant clinical findings. Time to outcome events were graphically presented using Kaplan–Meier curves and compared by the log-rank test. The predictive performance of BAG for clinical outcomes was evaluated from three perspectives. First, its prognostic utility was compared with that of chronological age. Model discrimination was assessed with Harrell’s C statistic, and calibration was evaluated using the Brier score. Second, the incremental value of BAG was examined by incorporating it into established clinical prediction models, with its contribution quantified using NRI and IDI. Third, to further assess its contribution to risk prediction, BAG was integrated into multivariable Cox proportional hazards models that included age, sex, BMI, history of ischemic stroke, TIA, coronary heart disease, hypertension, diabetes mellitus, hypercholesterolemia, smoking status, index event, NIHSS score on admission, pre-stroke mRS score, and TOAST classification.