Dementia diagnosis in seven languages: the Addenbrooke’s Cognitive Examination-III in India

OBJECTIVE: With the rising burden of dementia globally, there is a need to harmonize dementia research across diverse populations. The Addenbrooke's Cognitive Examination-III (ACE-III) is a well-established cognitive screening tool to diagnose dementia. But there have been few efforts to standardize the use of ACE-III across cohorts speaking different languages. The present study aimed to standardize and validate ACE-III across seven Indian languages and to assess the diagnostic accuracy of the test to detect dementia and mild cognitive impairment (MCI) in the context of language heterogeneity.  METHODS: The original ACE-III was adapted to Indian languages: Hindi, Telugu, Kannada, Malayalam, Urdu, Tamil, and Indian English by a multidisciplinary expert group. The ACE-III was standardized for use across all seven languages. In total, 757 controls, 242 dementia, and 204 MCI patients were recruited across five cities in India for the validation study. Psychometric properties of adapted versions were examined and their sensitivity and specificity were established.  RESULTS: The sensitivity and specificity of ACE-III in identifying dementia ranged from 0.90 to 1, sensitivity for MCI ranged from 0.86 to 1, and specificity from 0.83 to 0.93. Education but not language was found to have an independent effect on ACE-III scores. Optimum cut-off scores were established separately for low education (≤10 years of education) and high education (>10 years of education) groups.  CONCLUSIONS: The adapted versions of ACE-III have been standardized and validated for use across seven Indian languages, with high diagnostic accuracy in identifying dementia and MCI in a linguistically diverse context

Total output and switching in ategory fluency successfully iscriminates Alzheimer's disease from Mild Cognitive Impairment, but not from frontotemporal dementia

Verbal fluency tasks require generation of words beginning with a letter (phonemic fluency; PF) or from a category (category fluency; CF) within a limited time period. Generally, total output on CF has been used to discriminate Mild Cognitive Impairment (MCI) from Alzheimer's disease (AD), while poor PF has been used as a marker for behavioral-variant frontotemporal dementia (bvFTD). However, in the absence of this disparate performance, further characterization of the task becomes necessary. Objective: We examined whether fluency, as well as its components, clustering (successively generated words belonging to a category) and switching (shifting between categories) carried diagnostic utility in discriminating AD from MCI and bvFTD. Methods: PF (letter 'P') and CF ('animals') tasks were administered in English to patients with MCI (n=25), AD (n=37), and bvFTD (n=17). Clustering and switching scores were calculated using established criteria. Results: Our findings suggested that up to 85% of AD and MCI could be successfully discriminated based on total number of responses and switching in CF alone. PF-CF disparity was not noted in AD or bvFTD. Performance on clustering or switching also proved insufficient to discriminate AD from bvFTD. Conclusion: Switching was found to be useful when differentiating AD from MCI. In AD and bvFTD, the course of progression of the disease may lead to attenuation of total number of responses produced on both tasks to an extent where clustering and switching may not be useful measures to discriminate these dementias from each other