Original Article
Measurement protocol and discriminant analysis of rapid screening tools for assessing the risk of mild cognitive impairment
Yang Yiru, Lyu Chenlong, Zhao Shaokun, Li He, Zhang Zhanjun
Published 2020-10-14
Cite as Chin J Geriatr, 2020, 39(10): 1146-1150. DOI: 10.3760/cma.j.issn.0254-9026.2020.10.010
Abstract
ObjectiveTo develop rapid screening tools for assessing the risk of mild cognitive impairment(MCI)based on neuropsychological scales and cognitive paradigms.
MethodsTwo baseline datasets from the Beijing Ageing Brain Rejuvenation Initiative(BABRI)cohort were studied: dataset 1 contained 5 593 subjects, with 1 500 cases with MCI and 4 093 cases with normal cognitive function(the control group); dataset 2 consisted of 588 subjects, with 92 cases with MCI and 496 cases with normal cognitive function(the control group). Dataset 1 was used to simplify the Mini-Mental State Examination(MMSE), and the sub-item combination with the strongest MCI discriminative ability was selected to integrate into the cognitive rapid assessment(BABRI-mini MMSE). Dataset 2 with scores of encoding-recognition episodic memory task was used for further MCI discriminant analysis and was adapted into an episodic memory test(BABRI-EMT). We applied the receiver operating characteristic curve(ROC)for those analyses.
ResultsThe control group and the MCI group showed significant differences in multi-domain cognitive ability and episodic memory task performance(P<0.01). Among sub-items of MMSE measured using dataset 1, MMSE12 and MMSE19 had the highest discriminative accuracy for MCI, and the area under the ROC(AUC)was 0.699 and 0.631, respectively.Dataset 2 was used to investigate the discriminative ability of the episodic memory score in combination with the above two MMSE sub-items for MCI, and the AUC value was 0.732, the sensitivity was 0.731, and the specificity was 0.656.
ConclusionsThe BABRI-mini MMSE and BABRI-EMT are suitable for the large-scale universal screening of MCI risk.
Key words:
Dementia; Cognition disorders
Contributor Information
Yang Yiru
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing 100875, China
Lyu Chenlong
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing 100875, China
Teaching and Research Section, Graduate School, Academy of Military Sciences, Beijing 100850, China
Zhao Shaokun
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing 100875, China
Li He
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing 100875, China
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
Zhang Zhanjun
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing 100875, China
