Predicting Long-Term Cognitive Outcome with New Regression Models in Donepezil-Treated Alzheimer Patients in a Naturalistic Setting

Background/Aims: To build and analyze regression models predicting (1) the long-term cognitive outcome in donepezil-treated patients with Alzheimer's disease, and (2) the short-term (6 months) cognitive impact of treatment depending on cognitive severity at baseline. Methods: The Swedish Alzheimer Treatment Study (SATS) is an open-label, non-randomized, 3-year, multicentre study in a routine clinical setting. A total of 435 patients, mostly in the mild and moderate stages of Alzheimer's disease, received the cholinesterase inhibitor donepezil. They were assessed with the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) at baseline and every 6 months for a total period of 3 years. Regression models were fitted from the actual scores at different intervals for the prediction of the cognitive outcome. Results: The ADAS-cog and MMSE scores during the 3-year treatment period could be predicted with a high degree of explanation using regression models (p < 0.001). Moreover, there was a significant relation between the mean cognitive change after 6 months of treatment and the baseline scores on MMSE (p < 0.01) and ADAS-cog (p < 0.001), respectively. Conclusion: Statistical models can be used to predict cognitive outcome in donepezil-treated cohorts of AD patients. These models can be clinically valuable, for example when assessing the efficacy of new therapies when added to cholinesterase inhibitor treatment. Copyright (C) 2008 S. Karger AG, Basel