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. 2014 Nov;10(6):646-55.
doi: 10.1016/j.jalz.2013.12.014. Epub 2014 Feb 1.

A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer's disease

Deborah E Barnes  1 , Irena S Cenzer  2 , Kristine Yaffe  3 , Christine S Ritchie  4 , Sei J Lee  5 ; Alzheimer's Disease Neuroimaging Initiative
Affiliations

A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer's disease

Deborah E Barnes et al. Alzheimers Dement. 2014 Nov.

Abstract

Background: Our objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD).

Methods: Subjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model.

Results: The final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale-cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points).

Conclusions: A point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.

Keywords: Aged; Alzheimer's disease; Disease progression; Human; Magnetic resonance imaging; Mild cognitive impairment; Neuropsychological tests; Prognostic modeling; Risk factors.

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Conflict of interest statement

Conflicts: Dr. Barnes reports research support from the National Institutes of Health, Alzheimer’s Association, Department of Veterans Affairs, Department of Defense, NARSAD, University of California School of Medicine, S.D. Bechtel Jr. Foundation, UCSF Osher Center for Integrative Medicine and UCB Pharma, Inc. and serves as a study design consultant for the UCSF Clinical and Translational Sciences Institute. Ms. Cenzer reports no disclosures. Dr. Yaffe reports serving on data safety monitoring boards for Takeda, Inc and a study sponsored by the NIH and has served as a consultant for Novartis, Inc. Dr. Ritchie reports research funding from the National Institutes of Health, The Commonwealth Fund, The Retirement Research Foundation, programmatic funding support from the S.D. Bechtel Foundation and royalties from UptoDate. Dr. Lee reports funding from the National Institute on Aging and the American Federation of Aging Research through the Beeson Career Development Award (K23AG040779) and the S.D. Bechtel Foundation.

Figures

Figure 1
Figure 1. Conversion to AD in Participants with Low, Moderate and High Risk Scores
Figure 1 shows actual conversion to AD as a function of risk score group, with low risk (0 to 3 points) shown in blue, moderate risk (4 to 6 points) shown in green and high risk (7 to 9 points) shown in red.
Figure 2
Figure 2. Actual and Predicted Conversion Rates Over 1 and 3 Years
Figure 2 shows actual and predicted conversion rates over 1 year and 3 years of follow-up as a function of point score. The grey bars show the number of subjects with each point score value. The overlapping of the actual and predicted curves suggests good calibration of the model.
See this image and copyright information in PMC

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