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Title

 

 

 

 

Short-term response is predictive of long-term response to acetylcholinesterase inhibitors in Alzheimer’s disease: A starting point to explore Bayesian approximation in clinical practice

 

Authors

Eugenia Rota1*, Patrizia Ferrero2, Rita Ursone2, Giuseppe Migliaretti3

Affiliation

1 Neurology Unit, S. Croce Hospital, Mondovì (CN), Italy; 2 Department of Neuroscience, University of Turin, Italy; 3  Department of Public Health and Microbiology,  University of Turin, Italy

 

Email

eugenia_rota@yahoo.it

 

Phone

+39 0174 550593

Fax

+39 0174550539; * Corresponding author

Article Type

Hypothesis

 

Date

received July 31, 2007; revised August 07, 2007; accepted August 11, 2007; published online August 16, 2007

 

Abstract

This study was aimed at identifying, in 203 patients with Alzheimer’s disease followed during long-term treatment with Acetylcholinesterase inhibitors (ChEIs), the predictive factors of the clinical response among cognition (MMSE), functioning (BADL and IADL) measures and age and gender at the baseline (T0). The ANCOVA test showed a significant association between MMSE scores at time T0 and T3, and the variation T9-T0, T15-T0 and T21-T0 of the MMSE scores, using also gender, age and drug as covariates. The significance was higher for the patients affected by “mild” dementia. Regarding functional activities, a significant relationship was detected, by the ANCOVA test, only between the scores at T3 and the variation T15-T0 for BADL, and the variation T9-T0, T15-T0 for IADL, respectively. Our results confirm, in a “real world” setting, that ChEIs provide long-term cognitive benefit, which is correlated to, and predictable by, the short-term response (within the third month) as well as the cognitive status (evaluated by means of the MMSE) at the beginning of the treatment. These factors should be the basis of any cost/effectiveness algorithm in health economic decision models.

 

Keywords

 

acetylcholinesterase inhibitors; Alzheimer’s disease; dementia; mini mental state examination; Bayesian approximation; decision making

 

Citation

Rota et al., Bioinformation 2(2): 39-42 (2007)

Edited by

F. Chiappelli

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.