Li Ning, Shao Jia-Min, Jiang Ye, Wang Chu-Han, Li Si-Bo, Wang De-Chao, Di Wei-Ying
Department of Neurology, Affiliated Hospital of Hebei University, Baoding, Hebei Province, People's Republic of China.
Int J Gen Med. 2024 Jun 1;17:2513-2525. doi: 10.2147/IJGM.S464356. eCollection 2024.
This study addresses the predictive modeling of Enlarged Perivascular Spaces (EPVS) in neuroradiology and neurology, focusing on their impact on Cerebral Small Vessel Disease (CSVD) and neurodegenerative disorders.
A retrospective analysis was conducted on 587 neurology inpatients, utilizing LASSO regression for variable selection and logistic regression for model development. The study included comprehensive demographic, medical history, and laboratory data analyses.
The model identified key predictors of EPVS, including Age, Hypertension, Stroke, Lipoprotein a, Platelet Large Cell Ratio, Uric Acid, and Albumin to Globulin Ratio. The predictive nomogram demonstrated strong efficacy in EPVS risk assessment, validated through ROC curve analysis, calibration plots, and Decision Curve Analysis.
The study presents a novel, robust EPVS predictive model, providing deeper insights into EPVS mechanisms and risk factors. It underscores the potential for early diagnosis and improved management strategies in neuro-radiology and neurology, highlighting the need for future research in diverse populations and longitudinal settings.
本研究致力于神经放射学和神经病学中血管周围间隙增宽（EPVS）的预测建模，重点关注其对脑小血管病（CSVD）和神经退行性疾病的影响。
对587例神经科住院患者进行回顾性分析，采用LASSO回归进行变量选择，逻辑回归进行模型构建。该研究包括全面的人口统计学、病史和实验室数据分析。
该模型确定了EPVS的关键预测因素，包括年龄、高血压、中风、脂蛋白a、血小板大细胞比率、尿酸和白蛋白球蛋白比率。预测列线图在EPVS风险评估中显示出强大的功效，通过ROC曲线分析、校准图和决策曲线分析得到验证。
该研究提出了一种新颖、稳健的EPVS预测模型，为EPVS机制和风险因素提供了更深入的见解。它强调了在神经放射学和神经病学中进行早期诊断和改进管理策略的潜力，突出了未来在不同人群和纵向研究中的研究需求。
