Analysis of risk factors for the development of cognitive dysfunction in patients with cerebral small vessel disease and the construction of a predictive model

• Published in: Frontiers in neurology Vol. 13; p. 944205
• Main Authors: Zhang, Le, Gao, Fulin, Zhang, Yamin, Hu, Pengjuan, Yao, Yuping, Zhang, Qingzhen, He, Yan, Shang, Qianlan, Zhang, Yi
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 11-08-2022
• Subjects: cerebral small vessel disease; cognitive dysfunction; Neurology; nomogram; predictive model; risk factor; vascular dementia
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2022.944205

Background
Cognitive dysfunction in cerebral small vessel disease (CSVD) is a common cause of vascular dementia. The purpose of this study was to find independent risk factors for the development of cognitive dysfunction in patients with CSVD and establish a risk prediction model, in order to provide a reference for clinical diagnosis and treatment of such patients.
Methods
In this study, clinical data of patients with CSVD admitted to the Department of Neurology in Gansu Provincial Hospital from December 2019 to December 2021 were collected, and 159 patients were finally included after strict screening according to the inclusion and exclusion criteria. There were 43 patients with normal function and 116 patients with cerebral small vessel disease cognitive impairment (CSVDCI). The logistic multivariable regression model was used to screen out the independent risk factors of cognitive dysfunction in patients with CSVD, and the nomogram of cognitive dysfunction in patients with CSVD was constructed based on the results of the logistic multivariable regression analysis. Finally, the accuracy of the prediction model was evaluated by C-index, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
Results
The results of multivariable logistic regression analysis showed that hypertension (OR = 2.683, 95% CI 1.119–6.432,
P
= 0.027), homocysteine (Hcy) (OR = 1.083, 95% CI 1.026–1.143,
P
= 0.004), total CSVD MRI Score (OR = 1.593, 95% CI 1.025–2.475,
P
= 0.039) and years of schooling (OR = 0.883, 95% CI 0.798–0.978,
P
= 0.017) were independent risk factors for the development of cognitive dysfunction in patients with CSVD. The C-index of this prediction model was 0.806 (95% CI 0.735–0.877), and the calibration curve, ROC curve, and DCA curve all showed good predictive power in the nomogram.
Conclusions
The nomogram constructed in this study has high accuracy and clinical utility in predicting the occurrence of cognitive dysfunction in patients with CSVD. For patients with CSVD with the above risk factors, active clinical intervention and prevention are required during clinical consultation and disease management to avoid cognitive impairment as much as possible.