8 Citations (Scopus)


Introduction: To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). Methods: We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort. Results: The combined model (Harrell's C = 0.82 [0.78–0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm3, amyloid PET positive) has 35% (19–57) risk in one year and 85% (64–97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable. Discussion: The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment.
Original languageEnglish
Pages (from-to)529-537
Number of pages9
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Publication statusPublished - 2019


  • Alzheimer's disease
  • Amyloid positron emission tomography
  • Biomarkers
  • MCI
  • Progression

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