Exploration of a comprehensive index for predicting cognitive impairment in patients with cerebral small vessel disease and white matter lesions

Abstract

Background & Objective: The objective of this study was to explore the risk factors for cognitive impairment in patients with cerebral small vessel disease (CSVD), and to construct a predictive model for cognitive impairment in CSVD patients, providing personalized diagnostic and treatment strategies for patients. Methods: Clinical data and blood indicators of CSVD patients admitted to the Department of Neurology at the Second Affiliated Hospital of Shandong First Medical University from February 2022 to February 2023 were collected. Additionally, these patients underwent cranial MRI examinations and completed neurological and psychological assessments, including the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Based on the MoCA and MMSE results, the patients were divided into the cognitive impairment group and the normal cognitive group. Clinical data, blood indicators, and white matter lesion (WML) grades were compared between the two groups. Univariate logistic regression analysis was performed to identify the risk factors for cognitive impairment in CSVD patients. Using MoCA assessment results as the gold standard and several clinical indicators as independent variables, a logistic regression model was constructed. Predicted values were calculated based on this model, and a receiver operating characteristic (ROC) curve for the comprehensive diagnosis of multiple variables was plotted to evaluate the model’s accuracy. Results: A total of 134 CSVD patients were included, and cognitive impairment occurred in 98 cases, with an incidence rate of 73.13%, while 36 patients did not have cognitive impairment. Univariate logistic regression analysis of the collected variables identified eight factors: age, education level, hypertension, diabetes, cerebral hemorrhage, low-density lipoprotein cholesterol (LDL-C), hyperhomocysteine (HHCY), and WML grading. Multivariate logistic regression analysis identified age, LDL-C, and WML grading as the final predictive factors, establishing a combined diagnostic model to predict the probability of cognitive impairment in patients. The constructed ROC curve for the comprehensive diagnosis of multiple variables yielded an area under the curve of 0.870, indicating good accuracy. To facilitate clinical diagnosis, the combined diagnostic model was simplified into an L score calculation formula, with the optimal cutoff value of 5.223. When the L score is <5.223, the patient can be considered not having cognitive impairment, while an L score >5.223 indicates cognitive impairment, allowing for the prediction of the risk of cognitive impairment in patients. Conclusion: Age, education level, hypertension, diabetes, cerebral hemorrhage, LDL-C, HHCY, and WML grading are related risk factors for cognitive impairment in CSVD patients. Age, LDL-C, and WML grading are independent risk factors for cognitive impairment in CSVD patients. The clinical predictive model for cognitive impairment in cerebral small vessel disease, constructed using the final predictive factors, showed good performance and clinical utility. It facilitates individualized risk assessment for cognitive impairment in CSVD patients and allows for targeted follow-up observation for high-risk individuals.