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Gross AL, Hassenstab JJ, Johnson SC, et al. A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium. Alzheimers Dement (Amst). 2017;8:147-155doi: 10.1016/j.dadm.2017.05.003.
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. Alzheimer's & dementia (Amsterdam, Netherlands), 8147-155. https://doi.org/10.1016/j.dadm.2017.05.003
Gross, Alden L, et al. "A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium." Alzheimer's & dementia (Amsterdam, Netherlands) vol. 8 (2017): 147-155. doi: https://doi.org/10.1016/j.dadm.2017.05.003
Gross AL, Hassenstab JJ, Johnson SC, Clark LR, Resnick SM, Kitner-Triolo M, Masters CL, Maruff P, Morris JC, Soldan A, Pettigrew C, Albert MS. A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium. Alzheimers Dement (Amst). 2017 May 30;8:147-155. doi: 10.1016/j.dadm.2017.05.003. eCollection 2017. PMID: 28653035; PMCID: PMC5476965.
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Alzheimers Dement (Amst). 2017 May 30;8:147-155. doi: 10.1016/j.dadm.2017.05.003. eCollection 2017.

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

Alzheimer's & dementia (Amsterdam, Netherlands)

Alden L Gross, Jason J Hassenstab, Sterling C Johnson, Lindsay R Clark, Susan M Resnick, Melissa Kitner-Triolo, Colin L Masters, Paul Maruff, John C Morris, Anja Soldan, Corinne Pettigrew, Marilyn S Albert

Affiliations

  1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  2. Johns Hopkins University Center on Aging and Health, Baltimore, MD, USA.
  3. Charles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
  4. Wisconsin Alzheimer's Institute and Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  5. Geriatric Research Education and Clinical Center, William S Middleton Veterans Memorial Hospital, Madison, WI, USA.
  6. Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD, USA.
  7. The Florey Institute, University of Melbourne, Australia.
  8. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.

PMID: 28653035 PMCID: PMC5476965 DOI: 10.1016/j.dadm.2017.05.003

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 (

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),

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.

Keywords: Cognitive testing; Diagnostic classification; Harmonization; Longitudinal follow-up; Preclinical Alzheimer's disease

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