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dc.contributor.authorGross, AL
dc.contributor.authorHassenstab, JJ
dc.contributor.authorJohnson, SC
dc.contributor.authorClark, LR
dc.contributor.authorResnick, SM
dc.contributor.authorKitner-Triolo, M
dc.contributor.authorMasters, CL
dc.contributor.authorMaruff, P
dc.contributor.authorMorris, JC
dc.contributor.authorSoldan, A
dc.contributor.authorPettigrew, C
dc.contributor.authorAlbert, MS
dc.date.accessioned2020-12-21T01:01:56Z
dc.date.available2020-12-21T01:01:56Z
dc.date.issued2017
dc.identifierpii: S2352-8729(17)30033-7
dc.identifier.citationGross, 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.
dc.identifier.issn2352-8729
dc.identifier.urihttp://hdl.handle.net/11343/256338
dc.description.abstractINTRODUCTION: 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.
dc.languageeng
dc.publisherWiley
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleA classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium.
dc.typeJournal Article
dc.identifier.doi10.1016/j.dadm.2017.05.003
melbourne.affiliation.departmentAnatomy and Neuroscience
melbourne.affiliation.departmentFlorey Department of Neuroscience and Mental Health
melbourne.source.titleAlzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
melbourne.source.volume8
melbourne.source.issue1
melbourne.source.pages147-155
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1216745
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476965
melbourne.contributor.authorMaruff, Paul
melbourne.contributor.authorMasters, Colin
dc.identifier.eissn2352-8729
melbourne.accessrightsOpen Access


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