title: Application of the PredictAD software tool to predict progression in patients with mild cognitive impairment.
abstract: BACKGROUND
  The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease.
  
  
  METHODS
  Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators.
  
  
  RESULTS
  A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%.
  
  
  CONCLUSIONS
  Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.
authors.affiliation: Memory Disorders Research Group, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. anja.hviid.simonsen@rh.regionh.dk
authors.forename: Anja H
authors.initials: AH
authors.lastname: Simonsen
authors.forename: Jussi
authors.initials: J
authors.lastname: Mattila
authors.forename: Anne-Mette
authors.initials: AM
authors.lastname: Hejl
authors.forename: Kristian S
authors.initials: KS
authors.lastname: Frederiksen
authors.forename: Sanna-Kaisa
authors.initials: SK
authors.lastname: Herukka
authors.forename: Merja
authors.initials: M
authors.lastname: Hallikainen
authors.forename: Mark
authors.initials: M
authors.lastname: van Gils
authors.forename: Jyrki
authors.initials: J
authors.lastname: Lötjönen
authors.forename: Hilkka
authors.initials: H
authors.lastname: Soininen
authors.forename: Gunhild
authors.initials: G
authors.lastname: Waldemar
mesh_terms: D000367:Age Factors; D000368:Aged; D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000523:psychology*; D060825:Cognitive Dysfunction / Q000145:classification / Q000175:diagnosis* / Q000523:psychology*; D018450:Disease Progression; D004522:Educational Status; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D059906:Neuroimaging; D009483:Neuropsychological Tests; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012959:Socioeconomic Factors; D012984:Software