Li N, Shao JM, Jiang Y, Wang CH, Li SB, Wang DC, Di WY. Unraveling the Predictors of Enlarged Perivascular Spaces: A Comprehensive Logistic Regression Approach in Cerebral Small Vessel Disease.
Int J Gen Med 2024;
17:2513-2525. [PMID:
38846346 PMCID:
PMC11155382 DOI:
10.2147/ijgm.s464356]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
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.
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