Lindgren Erik, Angeleri Luca, Bretzner Martin, Bonkhoff Anna K, Jern Christina, Lindgren Arne G, Maguire Jane, Regenhardt Robert W, Rost Natalia S, Schirmer Markus D
J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg.
medRxiv. 2025 May 3:2025.05.02.25326881. doi: 10.1101/2025.05.02.25326881.
To compare quantitative MRI markers of brain health in their ability to predict functional outcome after acute ischemic stroke (AIS).
We included AIS survivors from the international MRI-GENIE study (multicenter; 2003-2011) with acute T2-FLAIR imaging. Automated pipelines estimated white matter hyperintensity volume (WMHv), brain volume, and intracranial volume (ICV). Assessed brain health markers included: brain parenchymal fraction (brain volume relative to ICV); radiomics derived brain age; brain reserve (normal appearing brain volume relative to ICV), and effective Reserve (eR, latent variable based on age, WMH load and brain volume). We added the markers to a clinical reference model, comparing model performances between separate multivariable regression models in their prediction of unfavorable outcome (90-day modified Rankin Scale score 3-5), using Bayesian Information Criterion (BIC).
We analyzed 2,223 patients (median age 67 years, 45% female, 24% unfavorable outcome). All models using brain health markers outperformed the clinical reference model (ΔBIC > 10). The eR model showed the lowest BIC value (BIC=2171.8), providing strong statistical evidence to outperform the brain age model (BIC=2179.5, ΔBIC > 6), and very strong (ΔBIC > 10) statistical evidence to outperform all other models.
Quantitative MRI markers of brain health, especially eR, enhance personalized outcome prognostication after AIS.
比较脑健康的定量MRI标志物预测急性缺血性卒中（AIS）后功能结局的能力。
我们纳入了国际MRI-GENIE研究（多中心；2003 - 2011年）中有急性T2-FLAIR成像的AIS幸存者。自动化流程估计了白质高信号体积（WMHv）、脑体积和颅内体积（ICV）。评估的脑健康标志物包括：脑实质分数（脑体积相对于ICV）；基于影像组学的脑龄；脑储备（外观正常的脑体积相对于ICV），以及有效储备（eR，基于年龄、WMH负荷和脑体积的潜在变量）。我们将这些标志物添加到临床参考模型中，使用贝叶斯信息准则（BIC）比较单独多变量回归模型在预测不良结局（90天改良Rankin量表评分3 - 5）时的模型性能。
我们分析了2223例患者（中位年龄67岁，45%为女性，24%有不良结局）。所有使用脑健康标志物的模型均优于临床参考模型（ΔBIC > 10）。eR模型显示出最低的BIC值（BIC = 2171.8），提供了有力的统计学证据表明其优于脑龄模型（BIC = 2179.5，ΔBIC > 6），以及非常有力的（ΔBIC > 10）统计学证据表明其优于所有其他模型。
脑健康的定量MRI标志物，尤其是eR，可增强AIS后个性化结局的预后评估。
