The Accuracy of Screening for Post-stroke Cognitive Impairment Assessment Tools：a Meta-analysis

Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2024-07-19 Cooperative journals: 《中国全科医学》

MA Yuxia YANG Yiyi WEI Xiaoqin CHEN Yanru QIN Jiangxia YUAN Yue CHEN Yajing WU Yinping HAN Lin

Abstract： Background  Post-stroke cognitive impairment（PSCI） brings a heavy burden to affected 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 China National Knowledge Infrastructure（CNKI），VIP，Wanfang Database，Chinese Biomedical Literature Database（CBM），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（TICSm）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 0.83（95%CI=0.79-0.87） and 0.79 （95%CI=0.72-0.84），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 0.75（95%CI=0.69-0.80）and 0.76（95%CI=0.68-0.83），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 TICSm 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 0.86. Conclusion  Based on the available evidence，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，this meta-analysis suggested that IQCODE and ACE-R are optimal assessment tools to accurately screen PSCI. Although the MMSE and MoCA are common tools to screen PSCI，their combined AUCs are lower than the AUC of IQCODE and ACE-R. 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.

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