卒中后认知障碍评估工具筛查准确性的Meta分析
2024-08-06马玉霞杨依依魏晓琴陈燕茹秦江霞袁月陈雅婧吴银瓶韩琳
【摘要】 背景 卒中后认知障碍(PSCI)给患者及其家庭带来沉重的负担,早期识别及干预有助于延缓PSCI的发生及进展,因此,使用准确的神经心理评估工具对PSCI进行筛查,对于患者的管理和治疗至关重要。目的 采用Meta分析的方法评价PSCI筛查工具的筛查准确性,为准确筛查PSCI提供依据。方法 检索数据库中国知网、维普网、万方数据知识服务平台、中国生物医学文献服务系统、PubMed、Embase、Web of Science、Cochrane Library中有关PSCI筛查工具的诊断性试验研究,检索日期为建库至2022年12月。2位研究者各自筛选文献、提取数据、评估偏倚风险。采用Stata 17.0软件分析数据。结果 共纳入57篇文献,包含12 113例患者,报告了7种PSCI筛查工具:美国国立神经疾病和卒中研究院-加拿大卒中网5-min测验(NINDS-CSN 5-min测验)、蒙特利尔认知评估量表(MoCA)、简易精神状况检查量表(MMSE)、老年认知功能减退知情者问卷(IQCODE)、阿登布鲁克认知能力检查-修订版(ACE-R)、认知功能电话问卷修订版(TICS-m)、5分钟蒙特利尔评估(MoCA-5 min)。Meta分析结果显示:MoCA筛查PSCI的合并灵敏度及特异度分别为0.84(95%CI=0.80~0.87)和0.74(95%CI=0.67~0.80),合并AUC为0.87(95%CI=0.84~0.90);MMSE筛查PSCI的合并灵敏度及特异度为0.73(95%CI=0.67~0.79)和0.76(95%CI=0.69~0.82),合并AUC为0.81(95%CI=0.77~0.84);IQCODE筛查PSCI的合并灵敏度及特异度为0.73(95%CI=0.48~0.89)和0.95(95%CI=0.75~0.99),合并AUC为0.91(95%CI=0.88~0.93);NINDS-CSN 5-min测验筛查PSCI的合并灵敏度及特异度为0.83(95%CI=0.78~0.87)、0.69(95%CI=0.60~0.76),合并AUC为0.85(95%CI=0.81~0.88);ACE-R筛查PSCI的合并灵敏度及特异度为0.90(95%CI=0.80~0.95)、0.61(95%CI=0.19~0.91),合并AUC为0.90(95%CI=
0.87~0.92);TICS-m筛查PSCI的合并灵敏度及特异度为0.84(95%CI=0.75~0.91)、0.67(95%CI=0.61~0.74),合并AUC为0.66(95%CI=0.60~0.71)。结论 IQCODE和ACE-R的合并AUC较高,且IQCODE具有较高的合并特异度,ACE-R具有较高的合并灵敏度,故IQCODE和ACE-R是较为准确的PSCI筛查工具。因IQCODE和ACE-R纳入文献数量有限,以上结论仍需多中心、大样本研究予以验证。
【关键词】 卒中后认知障碍;筛查工具;Meta分析;诊断性试验
【中图分类号】 R 749.1 【文献标识码】 A DOI:10.12114/j.issn.1007-9572.2023.0873
The Accuracy of Screening for Post-stroke Cognitive Impairment Assessment Tools:a Meta-analysis
MA Yuxia1,2,YANG Yiyi1,WEI Xiaoqin1,CHEN Yanru1,QIN Jiangxia1,YUAN Yue1,CHEN Yajing1,WU Yinping3,HAN Lin1,4*
1.Center for Evidence-based Nursing,School of Nursing,Lanzhou University,Lanzhou 730011,China
2.The First School of Clinical Medicine,Lanzhou University,Lanzhou 730000,China
3.Department of Medical Oncology,the Second Hospital of Lanzhou University,Lanzhou 730030,China
4.Department of Nursing,Gansu Provincial Hospital,Lanzhou 730000,China
*Corresponding author:HAN Lin,Professor;E-mail:hanlin@lzu.edu.cn
【Abstract】 Background Post-stroke cognitive impairment(PSCI) brings a heavy burden to patients and their families. An early recognition and intervention can help delay the occurrence and development of PSCI. Therefore,the use of accurate neuropsychological assessment tools to screen for PSCI is essential for the management and treatment of PSCI. Objective To analyze the screening accuracy of assessment tools for PSCI by meta-analysis,thus providing references for an accurate screening of PSCI. Methods Diagnostic trials on screening tools of PSCI published from the establishment of the database to December 2022 were searched in CNKI,VIP,Wanfang Data,SinoMed,PubMed,Embase,Web of Science,Cochrane Library. Two researchers respectively screened literatures,extracted data,and assessed the risk of bias. Stata 17.0 software was used to analyze the data. Results A total of 57 articles were included,involving 7 assessment tools [the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network 5-Minute Battery(NINDS-CSN 5-Minutes),the Montreal Cognitive Assessment(MoCA),the Mini-Mental State Examination(MMSE),the Informant Questionnaire on Cognitive Decline in the Elderly(IQCODE),the Addenbrooke's Cognitive Examination-Revised(ACE-R),the Telephone Interview for Cognitive Status Modified(TICS-m)and the Montreal Cognitive Assessment 5-minute protocol(MoCA-5 min)] to screen 12 113 patients. Meta-analysis results showed that the combined sensitivity and specificity of MoCA in screening PSCI were 0.84(95%CI=0.80-0.87) and 0.74(95%CI=0.67-0.80),respectively,with a combined area under the curve(AUC) of 0.87(95%CI=0.84-0.90). The combined sensitivity and specificity of MMSE in screening PSCI were 0.73(95%CI=0.67-0.79)and 0.76(95%CI=0.69-0.82),respectively,with a combined AUC of 0.81(95%CI=0.77-0.84). The combined sensitivity and specificity of IQCODE in screening PSCI were 0.73(95%CI=0.48-0.89) and 0.95(95%CI=0.75-0.99),respectively,with a combined AUC of 0.91(95%CI=0.88-0.93). The combined sensitivity and specificity of the NINDS-CSN 5-min in screening PSCI were 0.83(95%CI=0.78-0.87) and 0.69(95%CI=0.60-0.76),respectively,with a combined AUC of 0.85(95%CI=0.81-0.88). The combined sensitivity and specificity of the ACE-R in screening PSCI were 0.90(95%CI=0.80-0.95) and 0.61(95%CI=0.19-0.91),respectively,with a combined AUC of 0.90(95%CI=0.87-0.92). The combined sensitivity and specificity of TICS-m in screening PSCI were 0.84(95%CI=0.75-0.91) and 0.67(95%CI=0.61-0.74),respectively,with a combined AUC of 0.66(95%CI=0.60-0.71). Conclusion The combined AUC of IQCODE and ACE-R is larger,and the former as a higher combined specificity and the latter has a higher combined sensitivity. Therefore,IQCODE and ACE-R are optimal assessment tools to accurately screen PSCI. Due to the limited number of literatures reporting the IQCODE and ACE-R in screening PSCI,our conclusions still need to be validated by multicenter and large-sample studies.
【Key words】 Post-stroke cognitive impairment;Screening tools;Meta-analysis;Diagnostic test
根据《中国卒中报告2020》,中国40岁及以上人群卒中年发病率为201/10万[1],死亡率为149.49/10万[2]。卒中会导致各种并发症,而卒中后认知障碍(post-stroke cognitive impairment,PSCI)是主要并发症之一[3]。PSCI是一种临床综合征,其特征是在卒中事件后持续6个月的认知障碍[4]。研究报道PSCI的发病率为24%~53.4%[5-6]。PSCI包括卒中后认知障碍非痴呆和卒中后痴呆,有研究显示卒中后认知障碍非痴呆患者5年生存率约为75%,而卒中后痴呆患者5年生存率仅为39%[7]。可见,PSCI对患者的生活质量和生存时间造成重大影响,同时给家庭、社会带来巨大负担[8]。
神经心理评估是筛查和诊断PSCI及观察认知障碍程度的重要工具[4]。目前临床常用的神经心理评估工具有美国国立神经疾病和卒中研究院-加拿大卒中网5-min测验(NINDS-CSN 5-min测验)、蒙特利尔认知评估量表(MoCA)、简易精神状况检查量表(MMSE)、老年认知功能减退知情者问卷(IQCODE)、阿登布鲁克认知能力检查-修订版(ACE-R)、认知功能电话问卷修订版(TICS-m)等[9]。而不同工具在筛查PSCI时的灵敏度、特异度、评估时间、评估内容等方面差异较大,增加了实际应用中选择的难度。因此,本研究采取Meta分析的方法,对不同的PSCI筛查工具的筛查准确性进行比较,为选择PSCI筛查工具提供有力的证据支持。
1 资料与方法
1.1 纳入与排除标准
1.1.1 纳入标准:研究类型:诊断试验以英文或中文发表。研究对象:年龄≥18岁的卒中患者。PSCI诊断金标准:经临床、影像学、神经心理三方面评估确诊为PSCI;MRI是PSCI影像学评估的金标准,至少包括脑萎缩(位置、程度)、脑梗死(位置、大小、数量)、脑白质病变(范围)、脑出血(位置、大小、数量);神经心理评估:可多种工具结合,以确定认知障碍及程度,包括最少5个核心认知领域:执行功能、注意、记忆、语言和视空间功能[4]。诊断试验:至少用一种工具筛查PSCI。结局指标包括报告的真阳性、假阳性、真阴性、假阴性、灵敏度、特异度。
1.1.2 排除标准:合并其他脑血管疾病所致认知障碍的研究,如阿尔茨海默病等;不能提取相关数据的研究;重复发表的研究;综述和会议摘要;文献检索时发现的包含同一种研究工具的研究少于2篇。
1.2 检索策略
检索数据库中国知网、维普网、万方数据知识服务平台、中国生物医学文献服务系统、PubMed、Embase、Web of Science、Cochrane Library,检索日期为建库至2022年12月。检索时使用主题词和自由词结合。中文检索词:中风、脑卒中、脑出血、脑梗死、脑血栓、脑血管疾病;认知障碍、神经行为障碍、记忆障碍、认知功能损害、血管性痴呆、精神衰退;评估工具、筛查工具、量表、评分、问卷、评估方法、综合评估。英文检索词:stroke,cerebrovascular accident,brain vascular,CVA,intracranial hemorrhage,cerebral thrombosis,cerebral embolism,brain ischemia,cerebrovascular disease,brain injury;cognitive dysfunction,cognitive impairment,cognitive decline,cognition disorder,vascular dementia,mental disorders,auditory perceptual disorders;tool,score,scoring,scale,questionnaire,instrument,assessment。
1.3 文献筛选与资料提取
将检索结果录入EndNote X9软件去重,并进行文献筛选。2名研究人员分别提取数据和交叉检查。存在分歧时由第3位研究员协商。初步筛选标题和摘要后,研究员阅读全文二次筛选后,确定纳入的文献。提取纳入文献基本信息:如作者、年份、国家等;纳入文献基本特征:患者来源、样本量、性别、年龄等;试验相关要素:筛查量表、参考标准等;结局指标包括灵敏度、特异度、真阳性、假阳性、真阴性、假阴性等。
1.4 文献偏倚风险评估
2位研究者各自使用Cochrane Collaboration推荐的QUADAS-2[10]进行纳入文献的方法学质量评估。若存在分歧,由第3位研究员协商。采用RevMan 5.4绘制偏倚风险评估图。
1.5 统计学方法
采用Stata 17.0的“Midas”程序包进行Meta分析。采用随机效应模型进行双变量诊断分析,置信区间为95%,分析合并灵敏度、合并特异度、合并阳性似然比、合并阴性似然比、合并诊断比值比、合并综合受试者工作特征(SROC)曲线下面积(AUC)。I2表示异质性程度,如P>0.1、I2<50%,则说明纳入研究异质性低。若结果的异质性较大,对有足够研究数量的筛查工具采用单变量Meta回归探索异质性来源[11]。采用Z检验比较合并灵敏度、特异度,以P<0.05为差异有统计学意义。
2 结果
2.1 文献筛选结果
通过对数据库初步筛选,共获得20 456篇文献。EndNote X9去重后剩余18 251篇。在阅读标题、摘要后,不符合纳入标准的论文被排除,剩余597篇。阅读全文、按照排除标准进一步排除后,最终纳入文献57篇[12-68],见图1。
2.2 纳入文献的基本特征及偏倚风险
纳入文献的基本特征见表1。纳入57篇研究[12-68]中共涉及7种筛查工具,共12 113例。其中37篇研究[12-13,15-22,24-28,30-34,36-37,40-48,50,52-54,56,59]使用MoCA进行PSCI的筛查,26篇研究[14-15,19,23-24,27,30,34,37,40-41,43,45,48,50-54,56,58-59,61-63,68]使用MMSE进行PSCI的筛查,7篇研究[55,57,60-61,64,66-67]使用IQCODE进行PSCI筛查,6篇研究[18,29,35-36,47,49]使用NINDS-CSN 5-min测验进行PSCI的筛查,4篇研究[28,39,47,64]使用TICS-m进行PSCI的筛查,4篇研究[14,30,50-51]使用ACE-R进行PSCI的筛查,2篇研究[18,38]使用
5 min蒙特利尔评估(MoCA-5 min)进行PSCI的筛查。
QUADAS-2结果表明:31项研究[14,17,19,24-25,27,30-32,34-39,41,44,46,48,50-52,55,57-59,61-63,65-66]运用了盲法,28项研究[15-16,21-22,25-26,28-30,32-34,36,38,44,46-47,49-50,57-58,60-61,63-64,66-68]纳入连续病例,51项研究[12,14-23,25-36,38-52,54-62,65-68]避免病例对照,42项研究[14-15,17-18,20-27,29-30,32,34-35,37-38,41-43,45,47-50,52-57,59-61,63,65-68]事先设定了各筛查量表的阈值,所有研究描述了失访情况,偏倚风险整体较低,临床适用性整体较高。纳入文献的偏倚风险评估结果见图2。
2.3 Meta分析结果
本研究通过随机效应模型对有足够研究数量(n≥4)的6个筛查工具进行Meta分析,并对6个筛查工具的准确性进行汇总(表2)。
2.3.1 灵敏度:ACE-R的合并灵敏度为0.90(95%CI=0.80~0.95),MoCA的合并灵敏度为0.84(95%CI=0.80~0.87),TICS-m的合并灵敏度为0.84(95%CI=0.75~0.91),NINDS-CSN 5-min测验的合并灵敏度为0.83(95%CI=0.78~0.87),IQCODE的合并灵敏度为0.73(95%CI=0.48~0.89),MMSE的合并灵敏度为0.73(95%CI=0.67~0.79)。
2.3.2 特异度:IQCODE的合并特异度为0.95(95%CI=0.75~0.99),MMSE的合并特异度为0.76(95%CI=0.69~0.82),MoCA的合并特异度为0.74(95%CI=0.67~0.80),NINDS-CSN 5-min测验的合并特异度为0.69(95%CI=0.60~0.76),TICS-m的合并特异度为0.67(95%CI=0.61~0.74),ACE-R的合并特异度为0.61(95%CI=0.19~0.91)。
2.3.3 合并灵敏度及合并特异度比较:ACE-R与MMSE合并灵敏度比较,差异有统计学意义(Z=
-4.580,P<0.05);ACE-R与MoCA合并灵敏度比较,差异有统计学意义(Z=-2.092,P<0.05);ACE-R与IQCODE合并灵敏度比较,差异有统计学意义(Z=5.005,P<0.05);ACE-R与TICS-m合并灵敏度比较,差异无统计学意义(Z=1.244,P>0.05);ACE-R与NINDS-CSN 5-min合并灵敏度比较,差异有统计学意义(Z=5.875,P<0.05)。
ACE-R与MMSE合并特异度比较,差异无统计学意义(Z=-0.803,P>0.05);ACE-R与MoCA合并特异度比较,差异有统计学意义(Z=-2.574,P<0.05);ACE-R与IQCODE合并特异度比较,差异有统计学意义(Z=2.082,P<0.05);ACE-R与TICS-m合并特异度比较,差异有统计学意义(Z=2.787,P<0.05);ACE-R与NINDS-CSN 5-min合并特异度比较,差异有统计学意义(Z=2.609,P<0.05)。
IQCODE与MMSE合并灵敏度比较,差异无统计学意义(Z=-1.673,P>0.05);IQCODE与MoCA合并灵敏度比较,差异有统计学意义(Z=-5.980,P<0.05);IQCODE与TICS-m合并灵敏度比较,差异有统计学意义(Z=-2.523,P<0.05);IQCODE与NINDS-CSN 5-min合并灵敏度比较,差异有统计学意义(Z=-4.232,P<0.05)。
IQCODE与MMSE合并特异度比较,差异有统计学意义(Z=4.606,P<0.05);IQCODE与MoCA合并特异度比较,差异有统计学意义(Z=7.690,P<0.05);IQCODE与TICS-m合并特异度比较,差异有统计学意义(Z=5.863,P<0.05);IQCODE与NINDS-CSN 5-min合并特异度比较,差异有统计学意义(Z=6.874,P<0.05)。
2.3.4 SROC曲线:Meta分析结果表明,6个PSCI筛查工具AUC均>0.80。其中,IQCODE的合并AUC为0.91(95%CI=0.88~0.93),ACE-R的合并AUC为0.90(95%CI=0.87~0.92),MoCA的合并AUC为0.87(95%CI=0.84~0.90),TICS-m的合并AUC为0.66(95%CI=0.60~0.71),NINDS-CSN 5-min测验的合并AUC为0.85(95%CI=0.81~0.88),MMSE的合并AUC为0.81(95%CI=0.77~0.84)。
2.4 Meta回归
对MoCA进行Meta回归分析,探究异质性来源。结果显示,样本量、评估时间、是否前瞻性研究、研究地点、是否盲法可影响合并灵敏度的异质性;样本量、研究地点、是否盲法是合并特异度的异质性来源(图3)。
对MMSE进行Meta回归分析,探究异质性来源。结果显示,研究地点和是否盲法可影响合并灵敏度的异质性;研究地点和是否盲法是合并特异度的异质性来源(图4)。
2.5 其他筛查工具
MoCA 5-min纳入研究数量较少(n=2),且样本量较小,可参考价值有限。
3 讨论
PSCI是卒中后常见并发症,对卒中患者进行认知评估尤为重要[4]。本系统评价着眼于PSCI筛查工具的筛查准确性,Meta分析结果显示,IQCODE和ACE-R的AUC高于MMSE和MoCA,提示对PSCI的筛查准确性较好。与其他筛查试验相比,IQCODE还具有较高的合并特异度:0.95(95%CI=0.75~0.99),ACE-R具有较高的合并灵敏度:0.90(95%CI=0.80~0.95),且MMSE的灵敏度、特异度、ROC曲线的AUC点估计值均较低,MoCA特异度低于MMSE。但因IQCODE和ACE-R纳入文章数量有限,仍需进一步验证。
3.1 MoCA
MoCA包括记忆、视空间功能、执行功能、注意、语言、时间和地点取向等认知领域,评估时间15 min。若患者受教育年限<12年,MoCA评分需加1分。JAYWANT等[25]及GODEFROY等[56]发现其筛查PSCI的AUC为0.88,但PENDLEBURY等[47]及王慕秋等[54]发现其AUC仅为0.75~0.78,提示其筛查准确性不高。还有研究显示MoCA筛查PSCI的特异度较低(0.79)[69],WONG等[70]认为这与卒中患者的受教育程度低和病情不稳定或各研究截断值标准不统一有关,需进一步验证。
3.2 MMSE
MMSE包括30个条目,提供有关定向、注意力、学习、计算、延迟回忆和结构的信息。评分依据为未受教育者>17分,小学>20分,中学及以上>24分者为认知正常。有研究证实了MMSE作为卒中患者认知功能筛查工具的可行性和有效性[71-72]。但有学者指出MMSE不能区分局灶性和弥漫性脑损伤,且对右侧脑损伤不敏感[62,73]。结合本Meta分析结果,MMSE对PCSI的筛查价值有待进一步研究。
3.3 IQCODE
IQCODE包含26个项目,通过询问了解患者情况的主要照护者来完成,评价过去10年中的记忆和其他认知能力的变化。因IQCODE的评估是由研究者向患者的主要照护者提问,不需要卒中患者的参与,可防止患者年龄、教育程度、抑郁等症状对结果的影响。包绍智等[55]发现,IQCODE适合初步筛查腔隙性卒中患者的认知功能损害,具有较高的灵敏度(0.82)。但有学者发现IQCODE识别卒中后痴呆缺乏特异性[74],可能的原因是该问卷易受患者主要照顾者的主观期望和对患者病情熟悉程度等因素的影响。
3.4 ACE-R
ACE-R比MMSE涵盖更多个认知筛查领域,尤其增加了执行功能的测量,并提供了5个分量表的常模数据,可分析认知障碍模式。有研究表明ACR-R对卒中后1年内的认知障碍筛查具有良好的灵敏度[14,50]。MORRIS等[51]发现,ACE-R特异性存在争议,可能与ACE-R定向、注意和记忆部分占比较大和纳入人群文化差异有关。ACE-R在理想截断值方面也存在争议[75],因此,可在我国临床背景下进一步验证ACE-R对PSCI的最佳阈值及受教育程度等对ACE-R筛查准确性的影响。
3.5 本研究的局限性
(1)本次纳入研究限于中、英文,可能存在选择偏倚;(2)部分筛查工具纳入的研究较少,今后可进一步探究其筛查准确性;(3)纳入研究的截断值不尽相同,可能导致研究间存在异质性,今后可探讨各量表使用不同截断值时筛查PSCI的准确性;(4)受少数纳入研究限制,结果可能存在偏倚。产生偏倚风险的主要原因是:在最后的分析中未包含所有患者、未报告判读待评价试验结果时是否在不知晓金标准结果的情况下进行、未报告是否预先设定了阈值、未报告金标准结果判读是否使用了盲法。因此,在今后的诊断性试验设计中,应注意尽量避免病例对照设计、结果分析时尽量纳入所有患者、盲法的合理使用和事先设定量表阈值。
4 小结
本研究结果显示,IQCODE和ACE-R的合并AUC较高,且与其他筛查试验相比,IQCODE还具有较高的合并特异度,ACE-R具有较高的合并灵敏度,故IQCODE和ACE-R是较为准确的PSCI筛查工具。另外,MMSE、MoCA的AUC均低于IQCODE和ACE-R,提示其PSCI筛查准确性低于IQCODE和ACE-R。但受纳入研究工具数量与质量的限制,在未来的研究中可以开展IQCODE、ACE-R、MMSE、MoCA对PSCI筛查准确性的比较研究,进一步证实PSCI筛查的最优工具。
作者贡献:马玉霞、杨依依、陈燕茹进行文章的构思与设计;马玉霞、杨依依、魏晓琴进行数据整理;杨依依、魏晓琴进行文献检索、筛查和统计学处理;杨依依、魏晓琴、秦江霞、袁月、陈雅婧进行结果的分析与解释,论文的修订;马玉霞、杨依依负责撰写论文;吴银萍、韩琳负责文章的质量控制及审核;马玉霞、韩琳对文章整体负责、行政或技术材料的支持。
本文无利益冲突。
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(收稿日期:2024-03-16;修回日期:2024-05-19)
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基金项目:国家自然科学基金资助项目(72274087);甘肃省自然科学基金资助项目(22JR5RA218);甘肃省重点研发计划(23YFFA0006);中华医学会基金(20-374);兰州大学护理学院科研基金(LZUSON202002);兰州市城关区科技局项目(2022SHFZ0002);兰州市卫生科技项目(2021003)
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