title: Integrating Biomarkers for Underlying Alzheimer's Disease in Mild Cognitive Impairment in Daily Practice: Comparison of a Clinical Decision Support System with Individual Biomarkers.
abstract: BACKGROUND
  Recent criteria allow biomarkers to provide evidence of Alzheimer's disease (AD) pathophysiology. How they should be implemented in daily practice remains unclear, especially in mild cognitive impairment (MCI) patients.
  
  
  OBJECTIVE
  We evaluated how a clinical decision support system such as the PredictAD tool can aid clinicians to integrate biomarker evidence to support AD diagnosis.
  
  
  METHODS
  With available data on demographics, cerebrospinal fluid (CSF), and MRI, we trained the PredictAD tool on a reference population of 246 controls and 491 AD patients. We then applied the identified algorithm to 211 MCI patients. For comparison, we also classified patients based on individual biomarkers (MRI; CSF) and the NIA-AA criteria. Progression to dementia was used as outcome measure.
  
  
  RESULTS
  After a median follow up of 3 years, 72 (34%) MCI patients remained stable and 139 (66%) progressed to AD. The PredictAD tool assigned a likelihood of underlying AD to each patient (AUC 0.82). Excluding patients with missing data resulted in an AUC of 0.87. According to the NIA-AA criteria, half of the MCI patients had uninformative biomarkers, precluding an assignment of AD likelihood. A minority (41%) was assigned to high or low AD likelihood with good predictive value. The individual biomarkers showed best value for CSF total tau (AUC 0.86).
  
  
  CONCLUSIONS
  The ability of the PredictAD tool to identify AD pathophysiology was comparable to individual biomarkers. The PredictAD tool has the advantage that it assigns likelihood to all patients, regardless of missing or conflicting data, allowing clinicians to integrate biomarker data in daily practice.
authors.affiliation: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Hanneke F M
authors.initials: HF
authors.lastname: Rhodius-Meester
authors.affiliation: VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Juha
authors.initials: J
authors.lastname: Koikkalainen
authors.affiliation: VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Jussi
authors.initials: J
authors.lastname: Mattila
authors.affiliation: Neurochemistry Lab and Biobank, Department of Clinical Chemistry, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Charlotte E
authors.initials: CE
authors.lastname: Teunissen
authors.affiliation: Department of Radiology and Nuclear Medicine, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Frederik
authors.initials: F
authors.lastname: Barkhof
authors.affiliation: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Afina W
authors.initials: AW
authors.lastname: Lemstra
authors.affiliation: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Philip
authors.initials: P
authors.lastname: Scheltens
authors.affiliation: VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Jyrki
authors.initials: J
authors.lastname: Lötjönen
authors.affiliation: Alzheimer Center, Department of Neurology, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.|Department of Epidemiology and Biostatistics, VU University Medical Centre, Neuroscience Campus Amsterdam, Amsterdam, The Netherlands.
authors.forename: Wiesje M
authors.initials: WM
authors.lastname: van der Flier
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid* / Q000473:pathology*; D019540:Area Under Curve; D015415:Biomarkers / Q000134:cerebrospinal fluid*; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid* / Q000473:pathology*; D015331:Cohort Studies; D020000:Decision Support Systems, Clinical; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D009483:Neuropsychological Tests; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests
chemical_list: D015415:Biomarkers
keywords: KeywordsAlzheimer’s disease; clinical decision support system; diagnostic test assessment; mild cognitive impairment; prognosis