数字符号替换试验
蒙特利尔认知评估
神经认知
认知
精神分裂症(面向对象编程)
克朗巴赫阿尔法
心理学
听力学
认知缺陷
精神科
临床心理学
医学
心理测量学
认知障碍
替代医学
病理
安慰剂
作者
Mukesh Chand Daderwal,Vanteemar S. Sreeraj,Satish Suhas,Naren P. Rao,Ganesan Venkatasubramanian
标识
DOI:10.1016/j.psychres.2022.114731
摘要
Cognitive deficit is one of the core features of schizophrenia and is associated with poor functional outcomes. There is a lack of validated criteria to screen and monitor cognitive deficits in schizophrenia. This study aimed to evaluate the concurrent validity and sensitivity of MoCA (Montreal Cognitive Assessment) and DSST (Digit Symbol Substitution Test) in identifying cognitive deficits in Schizophrenia comparing with a comprehensive MCCB [MATRICS (Measurement And Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery] equivalent battery. We did clinical and cognitive assessments on 30 patients with schizophrenia and 30 age and gender-matched healthy controls. The Cronbach's Alpha of MoCA was 0.839, and on adding the DSST, it increased to 0.859. In stepwise binary logistic regression, adding DSST to MoCA improved the prediction of cognitive impairment as defined by a comprehensive battery with 86.7% classification accuracy. Receiver operating characteristic curve analysis suggested a score of 25 of MoCA and 59 of DSST as an optimal cut-off in identifying severe cognitive deficits with an additional MoCA cut-off of 27 for identifying mild cognitive deficits. Combined MoCA and DSST is a sensitive and quick method to screen for neurocognitive deficits in schizophrenia.
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