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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Montreal Cognitive Assessment as a screening instrument for cognitive impairments in schizophrenia
Zixu Yang1, Nur Amirah Abdul Rashid1, Yue Feng Quek2
1Research Division, Institute of Mental Health, Singapore, Singapore.
Background:
Cognitive impairment is one of the core features of schizophrenia. For its evaluation, current clinical practice relies on detailed neuropsychological batteries which require trained testers and considerable amount of time to administer. Therefore, a brief and reliable screening tool for identification of overall cognitive impairment prior to a detailed comprehensive neurocognitive assessment is needed in a busy clinical setting. This study evaluates the clinical utility of the Montreal Cognitive Assessment (MoCA) in detecting cognitive impairments in schizophrenia and its relationship with functional outcome and demographic characters.
Methods:
The MoCA, the Brief Assessment of Cognition in Schizophrenia (BACS), and the Brief UCSD Performance-based Skills Assessment (UPSA-B) were administered to 64 patients with schizophrenia. Mild and severe cognitive impairments were defined as BACS Z-score (calculated with the age and gender adjustments using previously published local norm data) of one or two standard deviations below the mean, respectively.
Results:
The results showed that the MoCA was significantly correlated with BACS (r=.61, p<.001) and sensitive to detect both mild (AUC=0.82, p<.001) and severe (AUC=0.81, p<.001) cognitive impairments in schizophrenia. The MoCA was significantly correlated with UPSA-B score (r=.51, p<.001), and accounted for significant additional variance in UPSA-B score beyond the BACS.
Conclusion:
These findings indicate that MoCA is a useful bedside cognitive screening instrument for people with schizophrenia.
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