title: A prediction model to calculate probability of Alzheimer's disease using cerebrospinal fluid biomarkers.
abstract: BACKGROUND
  We aimed to develop a prediction model based on cerebrospinal fluid (CSF) biomarkers, that would yield a single estimate representing the probability that dementia in a memory clinic patient is due to Alzheimer's disease (AD).
  
  
  METHODS
  All patients suspected of dementia in whom the CSF biomarkers had been analyzed were selected from a memory clinic database. Clinical diagnosis was AD (n = 272) or non-AD (n = 289). The prediction model was developed with logistic regression analysis and included CSF amyloid β42, CSF phosphorylated tau181, and sex. Validation was performed on an independent data set from another memory clinic, containing 334 AD and 157 non-AD patients.
  
  
  RESULTS
  The prediction model estimated the probability that AD is present as follows: p(AD) = 1/(1 + e (- [-0.3315 + score])), where score is calculated from -1.9486 × ln(amyloid β42) + 2.7915 × ln(phosphorylated tau181) + 0.9178 × sex (male = 0, female = 1). When applied to the validation data set, the discriminative ability of the model was very good (area under the receiver operating characteristic curve: 0.85). The agreement between the probability of AD predicted by the model and the observed frequency of AD diagnoses was very good after taking into account the difference in AD prevalence between the two memory clinics.
  
  
  CONCLUSIONS
  We developed a prediction model that can accurately predict the probability of AD in a memory clinic population suspected of dementia based on CSF amyloid β42, CSF phosphorylated tau181, and sex.
authors.affiliation: Department of Geriatric Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. PetraESpies@gmail.com
authors.forename: Petra E
authors.initials: PE
authors.lastname: Spies
authors.forename: Jurgen A H R
authors.initials: JA
authors.lastname: Claassen
authors.forename: Petronella G M
authors.initials: PG
authors.lastname: Peer
authors.forename: Marinus A
authors.initials: MA
authors.lastname: Blankenstein
authors.forename: Charlotte E
authors.initials: CE
authors.lastname: Teunissen
authors.forename: Philip
authors.initials: P
authors.lastname: Scheltens
authors.forename: Wiesje M
authors.initials: WM
authors.lastname: van der Flier
authors.forename: Marcel G M
authors.initials: MG
authors.lastname: Olde Rikkert
authors.forename: Marcel M
authors.initials: MM
authors.lastname: Verbeek
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid* / Q000175:diagnosis*; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid; D015415:Biomarkers / Q000134:cerebrospinal fluid; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008569:Memory Disorders / Q000134:cerebrospinal fluid* / Q000175:diagnosis*; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011336:Probability; D016875:tau Proteins / Q000134:cerebrospinal fluid
chemical_list: D016229:Amyloid beta-Peptides; D015415:Biomarkers; D016875:tau Proteins