Establishment and evaluation of a clinical prediction model for cognitive impairment in patients with cerebral small vessel disease.

IF 2.4 4区医学 Q3 NEUROSCIENCES

BMC Neuroscience Pub Date : 2024-08-02 DOI:10.1186/s12868-024-00883-y

Fangfang Zhu, Jie Yao, Min Feng, Zhongwu Sun

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引用次数: 0

Abstract

Background: There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD).

Aims: To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model.

Methods: A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction.

Results: A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867.

Conclusions: The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.

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建立和评估脑小血管疾病患者认知障碍的临床预测模型。

背景：目的：研究 CSVD 患者认知功能障碍的风险因素，并构建风险预测模型：方法：对227名CSVD患者进行回顾性研究。所有患者均接受了脑磁共振成像（MRI）评估，并使用蒙特利尔认知评估（MoCA）评估认知状况。此外，还记录了患者的医疗记录。临床数据分为认知功能正常组和认知障碍组。MoCA 评分结果：本研究共纳入 227 名 CSVD 患者（平均年龄为 66.7 ± 6.99 岁），其中 68.7% 为男性，100 名患者（44.1%）出现认知障碍。年龄（OR = 1.070; 95% CI = 1.015 ~ 1.128, p 结论：基于脑磁共振成像的提名图模型显示，年龄越大，认知功能越差：基于脑磁共振图像和临床数据的提名图模型有助于对 CSVD 患者认知障碍进展的概率进行个体化分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Neuroscience 医学-神经科学

CiteScore

3.90

自引率

0.00%

发文量

审稿时长

16 months

期刊介绍： BMC Neuroscience is an open access, peer-reviewed journal that considers articles on all aspects of neuroscience, welcoming studies that provide insight into the molecular, cellular, developmental, genetic and genomic, systems, network, cognitive and behavioral aspects of nervous system function in both health and disease. Both experimental and theoretical studies are within scope, as are studies that describe methodological approaches to monitoring or manipulating nervous system function.