Objective: The objective of this study was to develop and validate a practical computerized prognostic model that uses baseline psychometric and imaging data, including results of PET imaging of amyloid deposition, to predict the progression to dementia in patients at risk for Alzheimer's disease (AD).
Patients and methods: Data from patients in a phase II trial of [F]flutemetamol for PET imaging of brain amyloid and from the Alzheimer's Disease Neuroimaging Initiative were used to train the prognostic model to yield a disease state index (DSI), a measure of the similarity of an individual patient's data to data from patients in specific diagnostic groups. Inputs to the model included amyloid PET results, MRI measurements of hippocampal volume, and the results of psychometric tests. The model was subsequently validated by using data from a prospective study of an independent cohort of patients with mild cognitive impairment.
Results: In total, data from 223 patients of the 233 enroled were suitable for analysis. The DSI predicted by the model and the risk of progression to AD dementia within 3 years were higher for patients with amyloid deposition and neurodegeneration than for patients with amyloid deposition without neurodegeneration. Rates of non-AD dementia among patients with neurodegeneration at baseline were consistent with the results of other studies. The results were consistent with the Jack model of AD progression.
Conclusion: The DSI from the model that included psychometric, MRI, and PET amyloid data provides useful prognostic information in cases of mild cognitive impairment.