基于神经因子及定量脑电图指标构建脑卒中后癫痫认知障碍的预测模型

doi:10.3969/j.issn.1671-7171.2025.12.017

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摘要 【目的】探讨基于神经因子和定量脑电图(QEEG)指标构建用于预测老年脑卒中后癫痫认知功能障碍(PSCI)的模型。【方法】106例脑卒中后老年癫痫患者,采用蒙特利尔认知基础评估量表(MocA-B)评分将患者分为PSCI组(79例)和脑卒中后癫痫认知功能正常(PSCN)组(27例)。比较两组患者血清脑源性神经营养因子(BDNF)、神经生长因子(NGF)、QEEG指标[α波、β波、θ波、δ波绝对功率,以及θ/α(TAR)、(θ+δ)/(α+β)(DTABR),通过二项Logistic回归分析筛选PSCI的影响因素,并构建列线图预测模型及分析其的预测效能和准确度。【结果】PSCI组BDNF、α波绝对功率低于PSCN组,NGF、θ波绝对功率、TAR及DTABR高于PSCN组(均P<0.05);两组间β波绝对功率、δ波绝对功率比较,差异无统计学意义(P>0.05);Logistics回归分析结果显示,BDNF、α波绝对功率是PSCI的保护因素,NGF、θ波绝对功率、TAR、DTABR是其危险因素;模型验证结果显示,列线图预测模型的一致性指数为0.932,校准曲线与理想曲线走势相对一致,有较好的准确度和符合度。【结论】基于神经营养因子和QEEG指标的预测模型可有效预测老年脑卒中后癫痫患者的认知障碍风险,为临床提供参考。

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	刘晓
	卢哲

关键词 ：卒中, 癫痫, 认知障碍, 神经生长因子类, 脑电描记术

Abstract：【Objective】 To investigate the development of a predictive model for post-stroke epilepsy cognitive impairment (PSCI) in elderly patients, based on neurotrophic factors and quantitative electroencephalography (QEEG) indicators.【Methods】 A total of 106 elderly patients with post-stroke epilepsy were enrolled. According to the Montreal Cognitive Assessment-Basic (MoCA-B) scores, patients were divided into the PSCI (post-stroke epilepsy cognitive impairment) group (79 cases) and the PSCN (post-stroke epilepsy cognitive normal) group (27 cases). Serum levels of brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), and QEEG indicators [α wave, β wave, θ wave, δ wave ratio,θ/α(TAR), and (θ+δ)/(α+β)(DTABR) ratio] were compared between the two groups. Binary logistic regression analysis was used to identify influencing factors of PSCI in elderly patients with post-stroke epilepsy. A nomogram prediction model was then constructed, and its predictive performance and accuracy were evaluated.【Results】 The PSCI group showed lower BDNF and α-wave ratio, and higher NGF, θ wave ratio, TAR, and DTABR compared with the PSCN group (all P<0.05). There were no statistically significant differences in β wave or δ wave between the two groups (P>0.05). Logistic regression analysis indicated that BDNF and α wave ratio were protective factors for PSCI, while NGF, θ wave, TAR, and DTABR were risk factors. Model validation demonstrated a concordance index of 0.932, with the calibration curve closely aligned with the ideal curve, indicating good accuracy and consistency.【Conclusion】 A predictive model based on neurotrophic factors and QEEG indicators can effectively assess the risk of cognitive impairment in elderly patients with post-stroke epilepsy, providing a valuable reference for clinical practice.

Key words： Stroke Epilepsy Cognition Disorders Nerve Growth Factors Electroencephalography

收稿日期: 2025-06-24

中图分类号:

R743

引用本文:

刘晓, 卢哲. 基于神经因子及定量脑电图指标构建脑卒中后癫痫认知障碍的预测模型[J]. 医学临床研究, 2025, 42(12): 2075-2078.
LIU Xiao, LU Zhe. Construction of a Predictive Model for Post-Stroke Epilepsy Cognitive Impairment Based on Neurotrophic Factors and Quantitative Electroencephalography Indicators. JOURNAL OF CLINICAL RESEARCH, 2025, 42(12): 2075-2078.

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http://journal07.magtech.org.cn/yxlcyj/CN/10.3969/j.issn.1671-7171.2025.12.017 或 http://journal07.magtech.org.cn/yxlcyj/CN/Y2025/V42/I12/2075

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