TY - JOUR
T1 - A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts
T2 - The preclinical AD consortium
AU - Gross, Alden L.
AU - Hassenstab, Jason J.
AU - Johnson, Sterling C.
AU - Clark, Lindsay R.
AU - Resnick, Susan M.
AU - Kitner-Triolo, Melissa
AU - Masters, Colin L.
AU - Maruff, Paul
AU - Morris, John C.
AU - Soldan, Anja
AU - Pettigrew, Corinne
AU - Albert, Marilyn S.
N1 - Funding Information:
This study was supported by the Alzheimer's Association and Fidelity Biosciences (grant number AD-FBRI-16-392172). The individual studies in the consortium are funded, in part, by the following grants: U19-AG03365, P01-AG0262276, R01-AG27161, the Australian Commonwealth Scientific Industrial Research Organization (CSIRO) and the Intramural Program of the National Institute on Aging. A.D.L. was supported by K01-AG050699 from the National Institute on Aging.
Publisher Copyright:
© 2017 The Authors
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Cognitive testing
KW - Diagnostic classification
KW - Harmonization
KW - Longitudinal follow-up
KW - Preclinical Alzheimer's disease
UR - http://www.scopus.com/inward/record.url?scp=85020793277&partnerID=8YFLogxK
U2 - 10.1016/j.dadm.2017.05.003
DO - 10.1016/j.dadm.2017.05.003
M3 - Article
AN - SCOPUS:85020793277
SN - 2352-8729
VL - 8
SP - 147
EP - 155
JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
ER -