Zhang Le, Gao Fulin, Zhang Yamin, Hu Pengjuan, Yao Yuping, Zhang Qingzhen, He Yan, Shang Qianlan, Zhang Yi
The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China.
The Department of Neurology, Gansu Provincial Hospital, Lanzhou, China.
Front Neurol. 2022 Aug 11;13:944205. doi: 10.3389/fneur.2022.944205. eCollection 2022.
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.
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).
The results of multivariable logistic regression analysis showed that hypertension (OR = 2.683, 95% CI 1.119-6.432, = 0.027), homocysteine (Hcy) (OR = 1.083, 95% CI 1.026-1.143, = 0.004), total CSVD MRI Score (OR = 1.593, 95% CI 1.025-2.475, = 0.039) and years of schooling (OR = 0.883, 95% CI 0.798-0.978, = 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.
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.
脑小血管病(CSVD)中的认知功能障碍是血管性痴呆的常见病因。本研究旨在寻找CSVD患者发生认知功能障碍的独立危险因素,并建立风险预测模型,为这类患者的临床诊断和治疗提供参考。
本研究收集了2019年12月至2021年12月在甘肃省人民医院神经内科住院的CSVD患者的临床资料,根据纳入和排除标准进行严格筛选后,最终纳入159例患者。其中功能正常患者43例,脑小血管病认知损害(CSVDCI)患者116例。采用logistic多变量回归模型筛选出CSVD患者认知功能障碍的独立危险因素,并根据logistic多变量回归分析结果构建CSVD患者认知功能障碍的列线图。最后,通过C指数、校准曲线、受试者工作特征(ROC)曲线和决策曲线分析(DCA)评估预测模型的准确性。
多变量logistic回归分析结果显示,高血压(OR = 2.683,95%CI 1.119 - 6.432,P = 0.027)、同型半胱氨酸(Hcy)(OR = 1.083,95%CI 1.026 - 1.143,P = 0.004)、CSVD MRI总评分(OR = 1.593,95%CI 1.025 - 2.475,P = 0.039)和受教育年限(OR = 0.883,95%CI 0.798 - 0.978,P = 0.017)是CSVD患者发生认知功能障碍的独立危险因素。该预测模型的C指数为0.806(95%CI 0.735 - 0.877),校准曲线、ROC曲线和DCA曲线在列线图中均显示出良好的预测能力。
本研究构建的列线图在预测CSVD患者认知功能障碍的发生方面具有较高的准确性和临床实用性。对于具有上述危险因素的CSVD患者,在临床诊疗和疾病管理过程中需要积极进行临床干预和预防,尽可能避免认知损害。