Adaptation Of Addenbrooke’s Cognitive Examination - Revised (Ace-R) To Lithuanian Speaking Population

There is a clear need for brief, but multidimentional, sensitive and specific, dementia screening instruments for all types of dementia in many countries as evidenced by the popularity of the Addenbrooke's Cognitive Examination - Revised (ACE-R) adaptations. Objective. To translate and adapt the ACE-R to Lithuanian speaking population (create the ACE-R (LT)) and provide with measures of its clinical utility. Methods. This study received ethical approval from Lithuanian Bioethics Committee. The ACE-R was translated to Lithuanian and backtranslated to English, several Lithuanian culture-specific ACE-R modifications were made. Pilot study and adaptation studies were conducted. A total of 100 persons aged 50 or older were enrolled, 58 controls and 42 patients: 5 patients with mild cognitive impairment (MCI) and 37 patients with the following types of dementia (number of patients with each disorder provided in brackets): Alzheimer’s (15), Fronto-temporal (4), dementia with Lewy Bodies (4), Parkinson’s (3), Progressive Supranuclear Palsy (1), Vascular (1), multiple sclerosis (1) and mixed or unspecified dementia (8). Results. Reliability of the ACE-R (LT) was excellent (alpha coefficient = 0.895). Two cutoffs were defined (77 for MCI: sensitivity = 87.7%, specificity = 99%; 74 for dementia: sensitivity = 64.9%, specificity = 99%) with minor deviations from other ACE-R adaptations. A comparison of age and education matched groups placed the MCI group’s performance between control and dementia groups’ and revealed domains most sensitive (memory and language) and least sensitive (attention/ orientation and visuospatial skills) to dementia-related cognitive impairments. Conclusions. The ACE-R (LT) accomplishes standards of a valid dementia screening test, is sensitive to cognitive dysfunction and thus potentially suitable for employment in research and clinical practice in Lithuania. In addition, it allows receiving domain-specific data reducing the probability of false diagnosis