卒中后认知障碍的危险因素及临床预测模型的研究进展

doi:10.3877/cma.j.issn.1673-9248.2025.02.001

卒中后认知障碍（PSCI）是卒中患者的常见并发症，它不但降低了卒中患者的生活质量，还会影响卒中患者的长期预后，随着年龄的增长其致残率及致死率也随之上升。在临床实践中，卒中后谵妄和一过性认知损伤等可早期恢复，而PSCI 诊断常在卒中后3～6 个月进行认知评估才能最终确诊，若等到PSCI 最终诊断明确再给予干预，会错过最佳的治疗时机。因此，若能充分了解PSCI 的危险因素，对急性卒中患者进行早期筛查，建立预测模型，早期识别PSCI 高危人群并予以专门的管理，可改善PSCI 患者的预后。本文总结了神经心理评估量表、影像学（磁共振、超声、脑网络）以及血液检查3 个方面PSCI 危险因素的研究进展，并总结了利用起主要作用且具有简单易得特征的危险因素构建的预测模型的优缺点，以期为早期识别和管理PSCI 高危患者提供参考。

关键词: 卒中后认知障碍, 危险因素, 临床预测模型

Post-stroke cognitive impairment (PSCI) is a common complication in stroke survivors,significantly reducing their quality of life and impairing long-term prognosis.Disability and mortality rates associated with PSCI increase with age.In clinical practice, conditions such as post-stroke delirium and transient cognitive impairment may resolve early, whereas PSCI diagnosis typically requires cognitive assessment conducted 3-6 months after stroke.Delaying intervention until a definitive PSCI diagnosis is confirmed may result in missing the optimal treatment window.Therefore, thorough understanding of PSCI risk factors, early screening of acute stroke patients, development of prediction models, and early identification of high-risk individuals for specialized management can improve outcomes for PSCI patients.This review summarizes recent progress on PSCI risk factors across three domains: neuropsychological assessment scales, neuroimaging techniques (magnetic resonance imaging, ultrasound, brain networks),and blood biomarkers.Additionally, it evaluates the advantages and limitations of clinical prediction models constructed using key, easily accessible risk factors, aiming to provide insights for early identification and management of high-risk PSCI populations.

Key words: Post-stroke cognitive impairment, Risk factors, Clinical predictive model

表1 卒中后认知障碍预测模型的基本特征

文献作者	发表年份	研究对象	曲线下面积（95% 置信区间）	训练集样本量（例）	评估时间段	纳入变量
Ashburner 等^[47]	2024	缺血性卒中患者	训练集：0.750（0.726～0.775）	3741	卒中后3 个月～5 年	年龄、保险类型、行动不便问题、既往跌倒史、谵妄、外周血管疾病、帕金森病、抑郁症、严重慢性肾病、体质量异常减轻和厌食症、出院后转院
			内部验证：0.731（0.694～0.768）
			外部验证：0.724（0.681～0.766）
李小杏等^[48]	2023	轻型急性脑梗死患者	训练集：0.837（0.76～0.91）	135	卒中后6 个月	年龄、Fazekas 评分、皮质梗死、基质金属蛋白酶9、组织金属蛋白酶抑制剂1、中性粒细胞/ 淋巴细胞比值和淋巴细胞/ 单核细胞比值
			内部验证：0.814（无置信区间）
Ding 等^[49]	2019	急性脑梗死患者	训练集：0.884（0.832～0.935）	145	卒中后6～12 月	总灰质体积、脑白质高信号体积、脑室脑脊液体积
			无验证集
Gong 等^[50]	2019	脑出血患者	训练集：0.911（0.856～0.967）	92	卒中后3～6 个月	出血量、格拉斯哥昏迷量表评分、脑室内出血
			内部验证：0.919（无置信区间）
Chander 等^[51]	2017	轻型急性脑梗死患者	训练集：0.82（0.76～0.88）	209	卒中后3～6 个月	慢性腔隙数量、脑白质高信号、年龄、非腔隙性皮质梗死、
			内部验证：0.78（0.71～0.85）			全皮层萎缩、受教育程度
			外部验证：0.75（0.71～0.79）
Kandiah 等^[52]	2016	轻型急性脑梗死患者	训练集：0.829（0.77～0.88）	209	卒中后3～6 个月，12～18 个月	年龄、受教育程度、急性皮质梗死、脑白质高信号、慢性腔隙性梗死、全皮质萎缩、颅内大血管狭窄
			内部验证：0.7755（0.700～0.851）

表1 卒中后认知障碍预测模型的基本特征

文献作者	发表年份	研究对象	曲线下面积（95% 置信区间）	训练集样本量（例）	评估时间段	纳入变量
Ashburner 等^[47]	2024	缺血性卒中患者	训练集：0.750（0.726～0.775）	3741	卒中后3 个月～5 年	年龄、保险类型、行动不便问题、既往跌倒史、谵妄、外周血管疾病、帕金森病、抑郁症、严重慢性肾病、体质量异常减轻和厌食症、出院后转院
			内部验证：0.731（0.694～0.768）
			外部验证：0.724（0.681～0.766）
李小杏等^[48]	2023	轻型急性脑梗死患者	训练集：0.837（0.76～0.91）	135	卒中后6 个月	年龄、Fazekas 评分、皮质梗死、基质金属蛋白酶9、组织金属蛋白酶抑制剂1、中性粒细胞/ 淋巴细胞比值和淋巴细胞/ 单核细胞比值
			内部验证：0.814（无置信区间）
Ding 等^[49]	2019	急性脑梗死患者	训练集：0.884（0.832～0.935）	145	卒中后6～12 月	总灰质体积、脑白质高信号体积、脑室脑脊液体积
			无验证集
Gong 等^[50]	2019	脑出血患者	训练集：0.911（0.856～0.967）	92	卒中后3～6 个月	出血量、格拉斯哥昏迷量表评分、脑室内出血
			内部验证：0.919（无置信区间）
Chander 等^[51]	2017	轻型急性脑梗死患者	训练集：0.82（0.76～0.88）	209	卒中后3～6 个月	慢性腔隙数量、脑白质高信号、年龄、非腔隙性皮质梗死、
			内部验证：0.78（0.71～0.85）			全皮层萎缩、受教育程度
			外部验证：0.75（0.71～0.79）
Kandiah 等^[52]	2016	轻型急性脑梗死患者	训练集：0.829（0.77～0.88）	209	卒中后3～6 个月，12～18 个月	年龄、受教育程度、急性皮质梗死、脑白质高信号、慢性腔隙性梗死、全皮质萎缩、颅内大血管狭窄
			内部验证：0.7755（0.700～0.851）

1	汪凯, 董强, 郁金泰, 等.卒中后认知障碍管理专家共识2021 [J].中国卒中杂志, 2021, 16(4): 376-389.
2	Huang YY, Chen SD, Leng XY, et al.Post-stroke cognitive impairment: epidemiology, risk factors, and management [J].J Alzheimers Dis, 2022, 86(3): 983-999.
3	Qu Y, Zhuo L, Li N, et al.Prevalence of post-stroke cognitive impairment in china: a community-based, cross-sectional study [J].PLoS One, 2015, 10(4): e0122864.
4	Cumming TB, Brodtmann A, Darby D, et al.The importance of cognition to quality of life after stroke [J].J Psychosom Res, 2014,77(5): 374-379.
5	Ezekiel L, Collett J, Mayo NE, et al.Factors associated with participation in life situations for adults with stroke: a systematic review [J].Arch Phys Med Rehabil, 2019, 100(5): 945-955.
6	Gaynor E, Rohde D, Large M, et al.Cognitive impairment,vulnerability, and mortality post ischemic stroke: a five-year follow-up of the action on secondary prevention interventions and rehabilitation in stroke (ASPIRE-S) cohort [J].J Stroke Cerebrovasc Dis, 2018,27(9): 2466-2473.
7	Yao J, Liu X, Liu Q, et al.Characteristics of non-linguistic cognitive impairment in post-stroke aphasia patients [J].Front Neurol, 2020, 11: 1038.
8	Tay J, Morris RG, Markus HS.Apathy after stroke: diagnosis,mechanisms, consequences, and treatment [J].Int J Stroke, 2021,16(5): 510-518.
9	Chau JPC, Lo SHS, Zhao J, et al.Prevalence of post-stroke cognitive impairment and associated risk factors in Chinese stroke survivors [J].J Neurol Sci, 2023, 455: 122805.
10	Hobden G, Tang EYH, Demeyere N.A modified Delphi survey to build expert consensus on the structure and content of an enhanced care pathway for cognitive changes after stroke in the UK [J].BMC Health Serv Res, 2024, 24(1): 1162.
11	Hong CT, Lee HH, Chung CC, et al.Poststroke cognitive impairment:a longitudinal follow-up and pre/post stroke mini-mental state examination comparison [J].Curr Alzheimer Res, 2022, 19(10): 716-723.
12	Zhao L, Biesbroek JM, Shi L, et al.Strategic infarct location for poststroke cognitive impairment: a multivariate lesion-symptom mapping study [J].J Cereb Blood Flow Metab, 2018, 38(8): 1299-1311.
13	Ye B, Wei D, Pan L.Montreal cognitive assessment of cognitive dysfunction after basal ganglia stroke [J].Acta Neurol Belg, 2022,122(4): 881-884.
14	Weaver NA, Kuijf HJ, Aben HP, et al.Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts [J].Lancet Neurol,2021, 20(6): 448-459.
15	Coenen M, de Kort FA, Weaver NA, et al.Strategic white matter hyperintensity locations associated with post-stroke cognitive impairment: a multicenter study in 1568 stroke patients [J].Int J Stroke, 2024, 19(8): 916-924.
16	Teng Z, Dong Y, Zhang D, et al.Cerebral small vessel disease and post-stroke cognitive impairment [J].Int J Neurosci, 2017, 127(9):824-830.
17	Du J, Wang Y, Zhi N, et al.Structural brain network measures are superior to vascular burden scores in predicting early cognitive impairment in post stroke patients with small vessel disease [J].Neuroimage Clin, 2019, 22: 101712.
18	Duering M, Biessels GJ, Brodtmann A, et al.Neuroimaging standards for research into small vessel disease-advances since 2013 [J].Lancet Neurol, 2023, 22(7): 602-618.
19	Huijts M, Duits A, van Oostenbrugge RJ, et al.Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function.A study in first-ever lacunar stroke and hypertensive patients [J].Front Aging Neurosci, 2013, 5: 72.
20	Molad J, Kliper E, Korczyn AD, et al.Only white matter hyperintensities predicts post-stroke cognitive performances among cerebral small vessel disease markers: results from the TABASCO study [J].J Alzheimers Dis, 2017, 56(4): 1293-1299.
21	Caunca MR, Siedlecki K, Cheung YK, et al.Cholinergic white matter lesions, AD-signature cortical thickness, and change in cognition: the Northern Manhattan study [J].J Gerontol A Biol Sci Med Sci, 2020,75(8): 1508-1515.
22	Yamasaki T, Ikawa F, Ichihara N, et al.Factors associated with the location of perivascular space enlargement in middle-aged individuals undergoing brain screening in Japan [J].Clin Neurol Neurosurg, 2022,223: 107497.
23	Shulyatnikova T, Hayden MR.Why are perivascular spaces important?[J].Medicina (Kaunas), 2023, 59(5): 917.
24	Passiak BS, Liu D, Kresge HA, et al.Perivascular spaces contribute to cognition beyond other small vessel disease markers [J].Neurology,2019, 92(12): e1309-e1321.
25	Hu W, Guo X, Du Y.Lacunar infarction aggravates the cognitive deficit in the elderly with white matter lesion [J].Open Life Sci, 2022,17(1): 272-278.
26	Chen CF, Lan SH, Khor GT, et al.Cognitive dysfunction after acute lacunar infarct [J].Kaohsiung J Med Sci, 2005, 21(6): 267-271.
27	Grau-Olivares M, Arboix A.Mild cognitive impairment in stroke patients with ischemic cerebral small-vessel disease: a forerunner of vascular dementia? [J].Expert Rev Neurother, 2009, 9(8): 1201-1217.
28	Wu R, Feng C, Zhao Y, et al.A meta-analysis of association between cerebral microbleeds and cognitive impairment [J].Med Sci Monit,2014, 20: 2189-2198.
29	Poels MM, Ikram MA, van der Lugt A, et al.Cerebral microbleeds are associated with worse cognitive function: the Rotterdam Scan Study [J].Neurology, 2012, 78(5): 326-333.
30	Ding J, Sigurðsson S, Jónsson PV, et al.Space and location of cerebral microbleeds, cognitive decline, and dementia in the community [J].Neurology, 2017, 88(22): 2089-2097.
31	Betrouni N, Jiang J, Duering M, et al.Texture features of magnetic resonance images predict poststroke cognitive impairment: validation in a multicenter study [J].Stroke, 2022, 53(11): 3446-3454.
32	Mu J, Li J.Analysis of radiological features in patients with poststroke depression and cognitive impairment [J].Rev Neurosci, 2024,35(5): 565-573.
33	司倩倩, 程安琪, 范晓媛, 等.颅内动脉粥样硬化性狭窄与认知功能的关系研究 [J].中国卒中杂志, 2023, 18(4): 418-427.
34	司倩倩, 程安琪, 范晓媛, 等.症状性颅内动脉粥样硬化性狭窄患者的认知功能及脑结构改变 [J].中国卒中杂志, 2023, 18(11):1228-1237.
35	梁夏, 王金辉, 贺永.人脑连接组研究:脑结构网络和脑功能网络[J].科学通报, 2010, 55(16): 1565-1583.
36	Liu J, Qin W, Wang H, et al.Altered spontaneous activity in the default-mode network and cognitive decline in chronic subcortical stroke [J].J Neurol Sci, 2014, 347(1-2): 193-198.
37	Li H, Jia X, Li Y, et al.Aberrant amplitude of low-frequency fluctuation and degree centrality within the default mode network in patients with vascular mild cognitive impairment [J].Brain Sci, 2021,11(11): 1534.
38	Kohn A, Jasper AI, Semedo JD, et al.Principles of corticocortical communication: proposed schemes and design considerations [J].Trends Neurosci, 2020, 43(9): 725-737.
39	Favaretto C, Allegra M, Deco G, et al.Subcortical-cortical dynamical states of the human brain and their breakdown in stroke [J].Nat Commun, 2022, 13(1): 5069.
40	Liu C, Zuo L, Li Z, et al.Brain structural-functional coupling mechanism in mild subcortical stroke and its relationship with cognition [J].Brain Res, 2024, 1845: 149167.
41	Biesbroek JM, Weaver NA, Aben HP, et al.Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke [J].Neuroimage Clin, 2022, 34: 103018.
42	Zhu J, Wang Y, Li J, et al.Intracranial artery stenosis and progression from mild cognitive impairment to Alzheimer disease [J].Neurology,2014, 82(10): 842-849.
43	Rost NS, Brodtmann A, Pase MP, et al.Post-stroke cognitive impairment and dementia [J].Circ Res, 2022, 130(8): 1252-1271.
44	Schreiber S, Scheumann V, Perosa V, et al.Reader response: serum neurofilament light is sensitive to active cerebral small vessel disease [J].Neurology, 2018, 90(24): 1126.
45	Liu Y, Chen H, Zhao K, et al.High levels of plasma fibrinogen are related to post-stroke cognitive impairment [J].Brain Behav, 2019,9(10): e01391.
46	Altendahl M, Maillard P, Harvey D, et al.An IL-18-centered inflammatory network as a biomarker for cerebral white matter injury [J].PLoS One, 2020, 15(1): e0227835.
47	Ashburner JM, Chang Y, Porneala B, et al.Predicting post-stroke cognitive impairment using electronic health record data [J].Int J Stroke, 2024, 19(8): 898-906.
48	李小杏, 焦冰, 方桦, 等.基于血清生物标志物的卒中后痴呆发生风险评估模型研究 [J].广东医学, 2023, 44(12): 1548-1553.
49	Ding MY, Xu Y, Wang YZ, et al.Predictors of cognitive impairment after stroke: a prospective stroke cohort study [J].J Alzheimers Dis,2019, 71(4): 1139-1151.
50	Gong K, Zhao L, Guo J, et al.A nomogram to predict cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients: a retrospective cohort study [J].Medicine (Baltimore),2019, 98(42): e17626.
51	Chander RJ, Lam BYK, Lin X, et al.Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke [J].Sci Rep, 2017, 7(1): 12441.
52	Kandiah N, Chander RJ, Lin X, et al.Cognitive impairment after mild stroke: development and validation of the SIGNAL2 risk score [J].J Alzheimers Dis, 2016, 49(4): 1169-1177.

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Advances in risk factors and clinical prediction models for post-stroke cognitive impairment

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Advances in risk factors and clinical prediction models for post-stroke cognitive impairment