南開科技大學圖書館 |

Language: 繁體中文

說明(常見問題)

南開科技大學

圖書館首頁

編目中圖書申請

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Classifying neuropsychiatric symptom...

Michigan State University.

Classifying neuropsychiatric symptoms in patients with Alzheimer's disease.

紀錄類型:	書目-電子資源 : 單行本
正題名/作者:	Classifying neuropsychiatric symptoms in patients with Alzheimer's disease./
作者:	Tun, Saw-Myo.
面頁冊數:	78 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6339.
Contained By:	Dissertation Abstracts International68-09B.
標題:	Gerontology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3282208
ISBN:	9780549241638

Classifying neuropsychiatric symptoms in patients with Alzheimer's disease.
Tun, Saw-Myo.

Classifying neuropsychiatric symptoms in patients with Alzheimer's disease. - 78 p.

Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6339.

Thesis (Ph.D.)--Michigan State University, 2007.

Objective. The aim of the study was to conceptualize neuropsychiatric symptoms in Alzheimer's disease patients, as distinct symptom profiles with differential disease outcomes. Five outcomes of interest in the study were caregiver distress, quality of life, functional impairment, nursing home placement, and survival. Method. Cluster analysis was used to categorize 122 patients with Alzheimer's disease, based upon their neuropsychiatric symptoms, as assessed by the Neuropsychiatric Inventory (NPI). The presence, severity, and frequency of symptoms were considered. After identification of the subgroups, the predictive validity of the categorization was tested on the following: (1) group differences in caregiver distress at baseline using ANCOVA; (2) group differences in quality of life and functional impairment over a 2-year period using repeated measures ANOVA; and (3) group differences in time to nursing home placement and time to death over a 3-year period using Cox proportional hazard models. Results. Based on the presence of neuropsychiatric symptoms, three subgroups were identified: Minimally Symptomatic, Highly Symptomatic, and Predominantly Apathetic. At baseline, the scores on a caregiver distress measure differed significantly between the clusters (p&barbelow; = 0.00). Over a 2-year period, the subgroups were predictive of quality of life (p&barbelow; = 0.00). Similarly, over the same 2-year period, functional impairment was differentially predicted by the subgroups (p&barbelow; = 0.00). As for time to nursing home placement over a 3-year period, the results were significant (p&barbelow; < 0.05) with the Highly Symptomatic group showing the highest risk. In addition, the rates of survival were significantly predicted by the subgroups (p&barbelow; < 0.05), with the Minimally Symptomatic group having the lowest risk. Based on the severity and frequency of neuropsychiatric symptoms, 2-cluster and 4-cluster solutions were produced. The 4-cluster solution provided a better differentiation of the symptom profiles than the 2-cluster solution. The 4 clusters were: Minimally Symptomatic, Affective/Apathetic, Predominantly Apathetic, and Highly Symptomatic with Psychotic Features. Cross-sectionally, these four subgroups differentially predicted caregiver distress (p&barbelow; = 0.00). Over a 2-year period, the clustering predicted significant differential outcomes in quality of life (p&barbelow; = 0.00), and in functional impairment (p&barbelow; < 0.01). Moreover, cluster membership was predictive of nursing home placement (p&barbelow; < .05) and survival (p&barbelow; < 0.01) over a 3-year period. Conclusions. Neuropsychiatric subgroups, using the cluster analysis method, were able to predict differential outcomes, and identify those with an increased risk for a worse prognosis. Specifically, the clustering based on the presence, severity and frequency of symptoms, were able to predict outcome in caregiver distress, quality of life, functional impairment, nursing home placement, and survival. Thus, the results highlighted the importance of detecting and treating neuropsychiatric symptoms in Alzheimer's disease patients.

ISBN: 9780549241638Subjects--Topical Terms:

168436
Gerontology.

Classifying neuropsychiatric symptoms in patients with Alzheimer's disease.
LDR:04085nmm 2200277 4500 001 1000005354
005 20091102114813.5
008 091102s2007 ||||||||||||||||| ||eng d
020 $a 9780549241638
035 $a (UMI)AAI3282208
035 $a AAI3282208
040 $a UMI $c UMI{me_controlnum}
100 1 $a Tun, Saw-Myo. $3 1000006702
245 1 0 $a Classifying neuropsychiatric symptoms in patients with Alzheimer's disease.
300 $a 78 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6339.
500 $a Adviser: Anne Bogat.
502 $a Thesis (Ph.D.)--Michigan State University, 2007.
520 $a Objective. The aim of the study was to conceptualize neuropsychiatric symptoms in Alzheimer's disease patients, as distinct symptom profiles with differential disease outcomes. Five outcomes of interest in the study were caregiver distress, quality of life, functional impairment, nursing home placement, and survival. Method. Cluster analysis was used to categorize 122 patients with Alzheimer's disease, based upon their neuropsychiatric symptoms, as assessed by the Neuropsychiatric Inventory (NPI). The presence, severity, and frequency of symptoms were considered. After identification of the subgroups, the predictive validity of the categorization was tested on the following: (1) group differences in caregiver distress at baseline using ANCOVA; (2) group differences in quality of life and functional impairment over a 2-year period using repeated measures ANOVA; and (3) group differences in time to nursing home placement and time to death over a 3-year period using Cox proportional hazard models. Results. Based on the presence of neuropsychiatric symptoms, three subgroups were identified: Minimally Symptomatic, Highly Symptomatic, and Predominantly Apathetic. At baseline, the scores on a caregiver distress measure differed significantly between the clusters (p&barbelow; = 0.00). Over a 2-year period, the subgroups were predictive of quality of life (p&barbelow; = 0.00). Similarly, over the same 2-year period, functional impairment was differentially predicted by the subgroups (p&barbelow; = 0.00). As for time to nursing home placement over a 3-year period, the results were significant (p&barbelow; < 0.05) with the Highly Symptomatic group showing the highest risk. In addition, the rates of survival were significantly predicted by the subgroups (p&barbelow; < 0.05), with the Minimally Symptomatic group having the lowest risk. Based on the severity and frequency of neuropsychiatric symptoms, 2-cluster and 4-cluster solutions were produced. The 4-cluster solution provided a better differentiation of the symptom profiles than the 2-cluster solution. The 4 clusters were: Minimally Symptomatic, Affective/Apathetic, Predominantly Apathetic, and Highly Symptomatic with Psychotic Features. Cross-sectionally, these four subgroups differentially predicted caregiver distress (p&barbelow; = 0.00). Over a 2-year period, the clustering predicted significant differential outcomes in quality of life (p&barbelow; = 0.00), and in functional impairment (p&barbelow; < 0.01). Moreover, cluster membership was predictive of nursing home placement (p&barbelow; < .05) and survival (p&barbelow; < 0.01) over a 3-year period. Conclusions. Neuropsychiatric subgroups, using the cluster analysis method, were able to predict differential outcomes, and identify those with an increased risk for a worse prognosis. Specifically, the clustering based on the presence, severity and frequency of symptoms, were able to predict outcome in caregiver distress, quality of life, functional impairment, nursing home placement, and survival. Thus, the results highlighted the importance of detecting and treating neuropsychiatric symptoms in Alzheimer's disease patients.
590 $a School code: 0128.
650 4 $a Gerontology. $3 168436
650 4 $a Psychology, Clinical. $3 1000005545
690 $a 0351
690 $a 0622
710 2 $a Michigan State University. $3 1000005643
773 0 $t Dissertation Abstracts International $g 68-09B.
790 1 0 $a Bogat, Anne, $e advisor
790 $a 0128
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3282208