title: Diagnosing Alzheimer's disease--non-clinicians and computerised algorithms together are as accurate as the best clinical practice.
abstract: BACKGROUND
  An accurate diagnosis of Alzheimer's disease and an exclusion of other dementias is important in many clinical studies. Obtaining such a clinical diagnosis in epidemiological studies and clinical trials that recruit large numbers of patients is time consuming.
  
  
  OBJECTIVE
  To construct an algorithm using a limited number of data points to generate a diagnosis of the commonest forms of dementia using information collected by non clinicians.
  
  
  METHODS
  We constructed a computer algorithm to generate a diagnosis of Alzheimer's disease (AD), Dementia with Lewy Bodies (DLB), frontotemporal dementia (FTD), vascular dementia or to flag the case as needing a clinical review based on a limited number of data points taken from a largely structured interview using widely used scales. The diagnosis generated in life by the algorithm in a prospective, longitudinal study was compared to definitive diagnosis at post mortem.
  
  
  RESULTS
  Post mortem diagnosis was available for 43 cases. The positive predictive value of the algorithm was greater than 95%. AD was diagnosed by the algorithm and at post mortem in 36 of the cases. Two cases with FTD were wrongly diagnosed as having AD by the algorithm, five cases were flagged as needing a clinical review due to concomitant medical conditions of whom four had AD and one, who had been diagnosed clinically as having AD, was diagnosed on post mortem with corticobasal degeneration.
  
  
  CONCLUSIONS
  A combination of non-clinical researchers, a structured interview and a computerised algorithm is as effective at identifying AD as highly trained and skilled clinicians.
authors.affiliation: MRC Centre for Neurodegeneration Research, King's College London, Institute of Psychiatry, London, UK. C.Foy@iop.kcl.ac.uk
authors.forename: Catherine M L
authors.initials: CM
authors.lastname: Foy
authors.forename: Helen
authors.initials: H
authors.lastname: Nicholas
authors.forename: Paul
authors.initials: P
authors.lastname: Hollingworth
authors.forename: Harry
authors.initials: H
authors.lastname: Boothby
authors.forename: Julie
authors.initials: J
authors.lastname: Willams
authors.forename: Richard G
authors.initials: RG
authors.lastname: Brown
authors.forename: Safa
authors.initials: S
authors.lastname: Al-Sarraj
authors.forename: Simon
authors.initials: S
authors.lastname: Lovestone
mesh_terms: D000368:Aged; D000465:Algorithms; D000488:Allied Health Personnel; D000544:Alzheimer Disease / Q000175:diagnosis*; D001344:Autopsy; D003663:Decision Trees; D015140:Dementia, Vascular / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D018450:Disease Progression; D006801:Humans; D007406:Interview, Psychological; D020961:Lewy Body Disease / Q000175:diagnosis; D008137:Longitudinal Studies; D011446:Prospective Studies