BACKGROUND: Available screening tools for mild cognitive impairment (MCI), often a precursor to Alzheimer's disease, are insensitive or not feasible for administration in a busy primary care setting. Display Enhanced TEsting for Cognitive impairment and Traumatic brain injury (DETECT) addresses these issues by creating an immersive environment for the brief administration of neuropsychological (NP) measures. OBJECTIVE: The aim of this study was to determine if the DETECT cognitive subtests can identify MCI patients as accurately as standard pen and paper NP tests. METHODS: Twenty patients with MCI recruited from a memory disorders clinic and 20 age-matched controls were given both a full battery of NP tests (standard NP) and the DETECT screen. Logistic regression models were used to determine whether individual tests were predictive of group membership (MCI or control). Demographic variables including age, race, education and gender were adjusted as covariates. Selection methods were used to identify subset models that exhibited maximum discrimination between MCI patients and controls for both testing methods. RESULTS: Both the standard NP model (C-index = 0.836) and the DETECT model (C-index = 0.865) showed very good discrimination and were not significantly different (p = 0.7323). CONCLUSION: The DETECT system shows good agreement with standard NP tests and is capable of identifying elderly patients with cognitive impairment.
BACKGROUND: Available screening tools for mild cognitive impairment (MCI), often a precursor to Alzheimer's disease, are insensitive or not feasible for administration in a busy primary care setting. Display Enhanced TEsting for Cognitive impairment and Traumatic brain injury (DETECT) addresses these issues by creating an immersive environment for the brief administration of neuropsychological (NP) measures. OBJECTIVE: The aim of this study was to determine if the DETECT cognitive subtests can identify MCI patients as accurately as standard pen and paper NP tests. METHODS: Twenty patients with MCI recruited from a memory disorders clinic and 20 age-matched controls were given both a full battery of NP tests (standard NP) and the DETECT screen. Logistic regression models were used to determine whether individual tests were predictive of group membership (MCI or control). Demographic variables including age, race, education and gender were adjusted as covariates. Selection methods were used to identify subset models that exhibited maximum discrimination between MCI patients and controls for both testing methods. RESULTS: Both the standard NP model (C-index = 0.836) and the DETECT model (C-index = 0.865) showed very good discrimination and were not significantly different (p = 0.7323). CONCLUSION: The DETECT system shows good agreement with standard NP tests and is capable of identifying elderly patients with cognitive impairment.
Authors: Albert M Anderson; Jeffrey L Lennox; Minh L Nguyen; Drenna Waldrop-Valverde; William R Tyor; David W Loring Journal: J Neurovirol Date: 2016-05-31 Impact factor: 2.643
Authors: Tamara R Espinoza; Kristopher A Hendershot; Brian Liu; Andrea Knezevic; Breanne B Jacobs; Russell K Gore; Kevin M Guskiewicz; Jeffery J Bazarian; Shean E Phelps; David W Wright; Michelle C LaPlaca Journal: Neurotrauma Rep Date: 2021-05-26