Predicting the outcome of cholinesterase inhibitor treatment in Alzheimer's disease

预测胆碱酯酶抑制剂治疗阿尔茨海默病的效果

阅读:1

Abstract

OBJECTIVE: To investigate the possibility that response to cholinesterase inhibitor therapy could be predicted by easily measurable variables that are known to change as a result of treatment (such as the Mini Mental State Examination), measures of function (such as the instrumental activities of daily living and the social behaviour subscales of the Nurse's Observation Scale for Geriatric Patients), and measures of attention (such as the Digit Symbol Substitution Test; DSST), or that might influence response through structural (for example, age, cerebrovascular disease, medial temporal lobe (MTL) atrophy, hypertension) or chemical (for example, smoking) mechanisms. METHOD: This was a cohort study of 160 consecutive outpatients with probable Alzheimer's disease who commenced cholinesterase inhibitor treatment over a 3 year period in a semi-rural area of Scotland. RESULTS: The overall response rate was 42.1%. Stratification of response between good and poor responders was possible using baseline DSST and a measure of MTL thickness using CT. Among the patients, 60.4% of those above the cut off point for both DSST and MTL thickness (29/48 subjects) were classified as good responders, compared with 6.3% of subjects below the cut off point for both (1/16 subjects). Subjects above the cut off point for both measures were more likely to be classified as good responders than subjects with only one or no values above the respective cut off points (chi(2) = 10.61, df = 1, p = 0.001) CONCLUSIONS: The DSST and a measure of MTL thickness derived from CT scanning may be useful in improving the prediction of response to cholinesterase inhibitors in subjects with AD. Subjects with low DSST scores and more severe MTL atrophy are unlikely to respond to treatment. These preliminary data justify a prospective trial of the usefulness of our suggested predictive measures.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。