Simplify scales for identifying AD and MCI: A machine learning study based on six years of database

Abstract

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disorder characterized by cognitive functional decline accompanied with neuropsychiatric symptoms and impairment of activities of daily living (ADL). Mild Cognitive Impairment (MCI) is considered the prodromal state of AD. Early identification of MCI is a strategy to reduce the burden of caregivers and society. Neuropsychological scales are still the most used method for identification of cognitive decline. The purpose of this study was to simplify the scales by eliminating some insensitive items based on the six years of database and machine learning methods.MethodA total of 458 patients newly diagnosed as AD or MCI were recruited in Memory Clinic, Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University from 2014 to 2019. Neuropsychological battery was used to evaluate the cognitive function, neuropsychiatric symptoms and ADL, including Mini‐mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), Geriatric Depression Scale (GDS), Boston Naming Test (BNT), Digit Span (DS), Auditory Verbal Learning Test (AVLT), Trail Marking Test (TMT) A and B. Alzheimer’s Disease Assessment Scale (ADAS‐cog), Clinical Dementia Rating (CDR), Instrumental Activity of Daily Living Scale (IADL), Physical Self‐Maintenance Scale (PSMS). XGboost and CART regression tree were used to test all items contained in all scales and try to explore the importance of each item. Sensitivity was reflected by the AUC (Area Under Curve).ResultOf which 236 patients were diagnosed as AD and 222 as MCI. The mean age was 72.99±9.04 years and 51.53% were male. Among all the battery items, ADAs‐cog word recognition task showed the best importance in judging AD and MCI, followed correct numbers of AVLT delay recall, ADAs‐cog orientation, MMSE recall, TMT‐B time. The overall sensitivity for predicting AD or MCI for all items was 0.83. However, in our preliminary screening also showed the 31 items, most of which were based on the ADL scale and neuropsychiatry symptoms, contributed little to distinguishing AD from MCI. After removed these items, the sensitivity became to 0.87.ConclusionCognitive assessment such as recall and orientation may be a better strategy than neuropsychiatric symptoms and ADL when identifying AD from MCI.