Diagnostic criteria for depression in Parkinson's disease: A study of symptom patterns using latent class analysis

2011 ◽  
Vol 26 (12) ◽  
pp. 2239-2245 ◽  
Author(s):  
Sergio Starkstein ◽  
Milan Dragovic ◽  
Ricardo Jorge ◽  
Simone Brockman ◽  
Marcelo Merello ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Latent Class Analysis ◽  
Diagnostic Criteria ◽  
Latent Class ◽  
Class Analysis ◽  
Symptom Patterns

scholarly journals Sleep disturbance subtypes in Parkinson’s disease based on the cross-culturally validated Korean version of the Parkinson’s Disease Sleep Scale-2

2019 ◽  
Author(s):  
Hui-Jun Yang ◽  
Han-Joon Kim ◽  
Seong-Beom Koh ◽  
Joong-Seok Kim ◽  
Tae-Beom Ahn ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Latent Class Analysis ◽  
Sleep Disturbance ◽  
Latent Class ◽  
Intraclass Correlation ◽  
Class Analysis ◽  
Korean Version ◽  
The Cross

Abstract Background: Sleep-related problems in Parkinson’s disease (PD) have received greater attention in recent years due to their clinical influence on morbidity, disability, and the health-related quality of life (HRQoL) of patients. This study aimed to evaluate the clinimetric properties of the Korean version of the Parkinson’s Disease Sleep Scale-2 (K-PDSS-2), and to analyze whether distinct sleep disturbance subtypes could be empirically identified in patients with PD based on the cross-culturally validated K-PDSS-2. Methods: The internal consistency, test-retest reliability, scale precision, and convergent validity of the K-PDSS-2 were assessed in a nationwide, multicenter study of 122 patients with PD. Latent class analysis (LCA) was used to derive subgroups of patients who experienced similar patterns of sleep-related problems and nocturnal disabilities. Results: The mean total K-PDSS-2 scores were 11.67 ± 9.87 (mean ± standard deviation) at baseline, and 12.61 ± 11.17 upon follow up testing. The Cronbach’s α coefficients of the total K-PDSS-2 score at baseline and at follow up testing were 0.851 and 0.880 respectively. Intraclass correlation coefficient over the 2-week period ranged from 0.672 to 0.848. The total K-PDSS-2 score was strongly correlated to HRQoL measures and other corresponding nonmotor scales. LCA indicated three distinct sleep disturbance classes in the study patients, namely “less troubled sleepers”, “PD-related nocturnal difficulties”, and “disturbed sleepers”. Conclusions: The K-PDSS-2 showed good clinimetric attributes in accordance with prior studies that were using the original version of the PDSS-2, therefore confirming the cross-cultural usefulness of the scale. Further, this study documents the first application of an LCA approach for identifying sleep disturbance subtypes in patients with PD. Keywords: Parkinson’s disease; sleep; PDSS-2; validity; reliability; Korean version; latent class analysis.

scholarly journals Neuropsychological Subgroups in Non-Demented Parkinson’s Disease: A Latent Class Analysis

2017 ◽  
Vol 7 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Laura Brennan ◽  
Kathryn M. Devlin ◽  
Sharon X. Xie ◽  
Dawn Mechanic-Hamilton ◽  
Baochan Tran ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Neuropsychological Subgroups

Diagnostic Criteria for Depression in Alzheimer Disease: A Study of Symptom Patterns Using Latent Class Analysis

2011 ◽  
Vol 19 (6) ◽  
pp. 551-558 ◽  
Author(s):  
Sergio E. Starkstein ◽  
Milan Dragovic ◽  
Ricardo Jorge ◽  
Simone Brockman ◽  
Robert G. Robinson
Keyword(s):  
Alzheimer Disease ◽  
Latent Class Analysis ◽  
Diagnostic Criteria ◽  
Latent Class ◽  
Class Analysis ◽  
Symptom Patterns

scholarly journals Comparison of the accuracy of three diagnostic criteria and estimating the prevalence of metabolic syndrome: A latent class analysis

2019 ◽  
Vol 24 (1) ◽  
pp. 108 ◽  
Author(s):  
Akbar Fotouhi ◽  
Hossein Ebrahimi ◽  
MohammadHassan Emamian ◽  
Ahmad Khosravi ◽  
Hassan Hashemi
Keyword(s):  
Metabolic Syndrome ◽  
Latent Class Analysis ◽  
Diagnostic Criteria ◽  
Latent Class ◽  
Class Analysis
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