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Development and validation of a predictive model for perioperative low-density lipoprotein as a risk factor for
Jinpeng Wu1, Yifan Xu1, Chonghui Zhang1
1Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Background:
Moyamoya 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.
Methods:
This 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.
Results:
Among 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.
Conclusion:
Dynamic 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.
Insights
Dynamic changes in low-density lipoprotein (LDL) levels during surgery predict stroke risk in Moyamoya disease (MMD) patients. Monitoring LDL offers a new way to identify high-risk individuals for better MMD treatment.
Area of Science:
- Neurology
- Cardiology
- Vascular Surgery
Background:
- Moyamoya disease (MMD) is a rare cerebrovascular disorder associated with a high risk of postoperative stroke.
- Low-density lipoprotein (LDL) is a known risk factor for atherosclerosis.
- The link between perioperative LDL fluctuations and stroke risk in MMD patients remains understudied.
Purpose of the Study:
- To investigate the association between dynamic changes in LDL levels during the perioperative period and the risk of postoperative cerebral infarction in MMD patients.
- To develop a predictive model for identifying MMD patients at high risk of postoperative stroke.
Main Methods:
- Retrospective study of 266 MMD patients undergoing surgery.
- Recorded preoperative, 24-h postoperative, and recovery-phase LDL levels (min, max, mean).
- Utilized LASSO regression for variable selection and multivariate logistic regression to build a risk prediction model.
Main Results:
- Preoperative, postoperative, and mean recovery-phase LDL levels were significantly associated with postoperative cerebral infarction.
- The integrated predictive model showed excellent predictive ability (AUC=0.82) and good calibration.
- Decision curve analysis confirmed the model's clinical utility in identifying high-risk patients.
Conclusions:
- Dynamic LDL monitoring is crucial for predicting postoperative stroke risk in MMD.
- The developed risk model aids in early identification of high-risk patients.
- This model supports individualized interventions to improve MMD patient outcomes.
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