Deborah E Barnes1, Irena S Cenzer2, Kristine Yaffe3, Christine S Ritchie4, Sei J Lee5. 1. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA; San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA. Electronic address: deborah.barnes@ucsf.edu. 2. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Division of Geriatrics, University of California, San Francisco, San Francisco, CA, USA. 3. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA; San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA. 4. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Division of Geriatrics, University of California, San Francisco, San Francisco, CA, USA; The Jewish Home of San Francisco, San Francisco, CA, USA. 5. San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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.
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.
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