title: Identifying cost-effective predictive rules of amyloid-β level by integrating neuropsychological tests and plasma-based markers.
abstract: BACKGROUND
  Detecting participants who are positive for amyloid-β (Aβ) pathology is germane in designing prevention trials by enriching for those cases that are more likely to be amyloid positive. Existing brain amyloid measurement techniques, such as the Pittsburgh Compound B-positron emission tomography and cerebrospinal fluid, are not reasonable first-line approaches limited by either feasibility or cost.
  
  
  OBJECTIVE
  We aimed to identify simple and cost-effective rules that can predict brain Aβ level by integrating both neuropsychological measurements and blood-based markers.
  
  
  METHODS
  Several decision tree models were built for extracting the predictive rules based on the Alzheimer's Disease Neuroimaging Initiative cohort.
  
  
  RESULTS
  We successfully extracted predictive rules of Aβ level. For cognitive function variables, cases above the 45th percentile in total cognitive score (TOTALMOD), above the 52nd percentile of delayed word recall, and above the 70th percentile in orientation resulted in a group that was highly enriched for amyloid negative cases. Conversely scoring below the 15th percentile of TOTALMOD resulted in a group highly enriched for amyloid positive cases. For blood protein markers, scoring below the 57th percentile for apolipoprotein E (ApoE) levels (irrespective of genotype) enriched two fold for the risk of being amyloid positive. In the high ApoE cases, scoring above the 60th percentile for transthyretin resulted in a group that was >90% amyloid negative. A third decision tree using both cognitive and blood-marker data slightly improved the classification of cases.
  
  
  CONCLUSIONS
  Our study demonstrated that the integration of the neuropsychological measurements and blood-based markers significantly improved prediction accuracy. The prediction model has led to several simple rules, which have a great potential of being naturally translated into clinical settings such as enrichment screening for AD prevention trials of anti-amyloid treatments.
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA.
authors.forename: Mona
authors.initials: M
authors.lastname: Haghighi
authors.affiliation: Byrd Alzheimer's Institute, University of South Florida, Tampa, FL, USA.
authors.forename: Amanda
authors.initials: A
authors.lastname: Smith
authors.affiliation: Byrd Alzheimer's Institute, University of South Florida, Tampa, FL, USA.
authors.forename: Dave
authors.initials: D
authors.lastname: Morgan
authors.affiliation: School of Aging Studies, University of South Florida, Tampa, FL, USA.
authors.forename: Brent
authors.initials: B
authors.lastname: Small
authors.affiliation: Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, USA Byrd Alzheimer's Institute, University of South Florida, Tampa, FL, USA.
authors.forename: Shuai
authors.initials: S
authors.lastname: Huang
mesh_terms: D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism* / Q000523:psychology; D016229:Amyloid beta-Peptides / Q000378:metabolism*; D001057:Apolipoproteins E / Q000097:blood* / Q000235:genetics; D015415:Biomarkers / Q000097:blood; D001921:Brain / Q000378:metabolism*; D015331:Cohort Studies; D003362:Cost-Benefit Analysis; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D012372:ROC Curve; D012306:Risk
chemical_list: D016229:Amyloid beta-Peptides; D001057:Apolipoproteins E; D015415:Biomarkers
keywords: Amyloid; decision rules; neuropsychological tests; plasma markers; prediction