Predicting the timing of Alzheimer’s disease (AD) conversion for individuals with mild cognitive impairment (MCI) can be significantly improved by incorporating longitudinal change information of clinical and neuroimaging markers, in addition to baseline characteristics, according to projections made by investigators from The University of Texas Health Science Center at Houston. In an article published in Journal of Alzheimer’s Disease, the research team describes how their novel statistical models found that longitudinal measurements of ADAS-Cog was the strongest predictor for AD progression and the predictive utility was consistently significant with progression of disease.
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