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    A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium.

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    11
    Author
    Gross, AL; Hassenstab, JJ; Johnson, SC; Clark, LR; Resnick, SM; Kitner-Triolo, M; Masters, CL; Maruff, P; Morris, JC; Soldan, A; ...
    Date
    2017
    Source Title
    Alzheimer's & dementia (Amsterdam, Netherlands)
    Publisher
    Wiley
    University of Melbourne Author/s
    Maruff, Paul; Masters, Colin
    Affiliation
    Anatomy and Neuroscience
    Florey Department of Neuroscience and Mental Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Gross, A. L., Hassenstab, J. J., Johnson, S. C., Clark, L. R., Resnick, S. M., Kitner-Triolo, M., Masters, C. L., Maruff, P., Morris, J. C., Soldan, A., Pettigrew, C. & Albert, M. S. (2017). A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium.. Alzheimers Dement (Amst), 8 (1), pp.147-155. https://doi.org/10.1016/j.dadm.2017.05.003.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256338
    DOI
    10.1016/j.dadm.2017.05.003
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476965
    Abstract
    INTRODUCTION: We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition. METHODS: Cognitive and clinical data were used from five studies (N = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm-based classifications. RESULTS: In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85-0.95). Over an average 6.6 years of follow-up (up to 28 years), N = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person-years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification. DISCUSSION: Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time-consuming adjudication panel.

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