AUTHOR=Wu Jinpeng , Xu Yifan , Zhang Chonghui , Mu Cuiping , Yu Le , Xu Haowen , Wang Chao , Cui Zhenwen 

TITLE=Development and validation of a predictive model for perioperative low-density lipoprotein as a risk factor for postoperative cerebral infarction in Moyamoya disease

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1602006

DOI=10.3389/fneur.2025.1602006

ISSN=1664-2295

ABSTRACT=BackgroundMoyamoya disease (MMD) is a rare progressive cerebrovascular disorder with a high risk of postoperative cerebral infarction. Low-density lipoprotein (LDL) is a key risk factor for atherosclerosis, but the association between perioperative dynamic changes in LDL levels and the risk of postoperative cerebral infarction in MMD patients has not been thoroughly studied.MethodsThis retrospective, single-center study included 266 MMD patients who underwent surgical treatment at The Affiliated Hospital of Qingdao University between 2015 and 2022. Preoperative, 24-h postoperative, and recovery-phase LDL levels (minimum, maximum, and mean) were recorded. Key variables were selected using LASSO regression, and a risk prediction model for cerebral infarction was constructed using multivariate logistic regression analysis.ResultsAmong the 266 patients, preoperative LDL (p = 0.049), postoperative LDL (p = 0.027), and mean LDL during the recovery period (p = 0.036) were significantly associated with the occurrence of postoperative cerebral infarction. The integrated model, combining LDL indicators and clinical variables, demonstrated excellent predictive ability (AUC = 0.82) and good calibration. Decision curve analysis (DCA) further validated the model’s application in clinical decision-making, indicating its effectiveness in identifying high-risk patients.ConclusionDynamic monitoring of LDL levels during the perioperative period is of great significance for predicting the risk of postoperative cerebral infarction in MMD patients. The constructed risk prediction model provides a scientific basis for early identification of high-risk patients and the development of individualized intervention strategies, with the potential to improve clinical management and patient outcomes.