J Clin Neurol.  2018 Oct;14(4):530-536. 10.3988/jcn.2018.14.4.530.

Clinical Predictors of Excessive Daytime Sleepiness in Patients with Parkinson's Disease

• Junho BT
• Kummer A
• Cardoso F
• Teixeira A
• Rocha NP

• KMID: 2424182
• DOI: http://doi.org/10.3988/jcn.2018.14.4.530

Abstract

BACKGROUND AND PURPOSE
Excessive daytime sleepiness (EDS) is a common complaint among patients with Parkinson's disease (PD). Several factors have been associated with EDS in PD, especially neuropsychiatric symptoms. This study aimed to determine the relationships between neuropsychiatric symptoms, sociodemographic and clinical parameters, and EDS in PD.
METHODS
This cross-sectional study analyzed 85 patients with PD. All patients underwent socioeconomic and clinical data evaluations followed by a psychiatric interview and a neurological examination, including the assessment of sleep features. Patients were divided into two groups according to the presence or absence of EDS, which was defined as a score higher than 10 on the Epworth Sleepiness Scale. Binary logistic regression was performed in order to describe the predictors of EDS.
RESULTS
We found that EDS affects 40% of PD patients and is associated with older age, restless legs syndrome, depressive and anxious symptoms, and worse sleep quality. In the multivariate analysis, older age, levodopa use, and worse sleep quality remained as significant predictors of EDS in PD.
CONCLUSIONS
Nighttime sleep problems, older age, and levodopa use are significantly associated with EDS in PD. A careful assessment and the management of sleep problems in PD patients might help to improve their quality of life.

Keyword

Parkinson's disease; nonmotor symptoms; excessive daytime sleepiness; depression; anxiety

MeSH Terms

Anxiety
Cross-Sectional Studies
Depression
Humans
Levodopa
Logistic Models
Multivariate Analysis
Neurologic Examination
Parkinson Disease*
Quality of Life
Restless Legs Syndrome
Levodopa

Figure

• 

  Fig. 1 ROC curve for the binary logistic regression. The logistic regression model provided a good fit to the data, since the area under the ROC curve was 0.820. ROC: receiver operating characteristics.

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