title: Use of a novel technology for presenting screening measures to detect mild cognitive impairment in elderly patients.
abstract: 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).
  
  
  CONCLUSIONS
  The DETECT system shows good agreement with standard NP tests and is capable of identifying elderly patients with cognitive impairment.
authors.affiliation: Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA 30303, USA. david.wright@emory.edu
authors.forename: D W
authors.initials: DW
authors.lastname: Wright
authors.forename: F C
authors.initials: FC
authors.lastname: Goldstein
authors.forename: P
authors.initials: P
authors.lastname: Kilgo
authors.forename: J R
authors.initials: JR
authors.lastname: Brumfield
authors.forename: T
authors.initials: T
authors.lastname: Ravichandran
authors.forename: M L
authors.initials: ML
authors.lastname: Danielson
authors.forename: M
authors.initials: M
authors.lastname: Laplaca
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003072:Cognition Disorders / Q000175:diagnosis*; D003430:Cross-Sectional Studies; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D042241:Early Diagnosis; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D011446:Prospective Studies; D012680:Sensitivity and Specificity; D012984:Software