A sleep continuity scale in Alzheimer's disease: validation and relationship with cognitive and functional deterioration

Development of a depression in Parkinson's disease prediction model using machine learning

BACKGROUND

It is important to diagnose depression in Parkinson’s disease (DPD) as soon as possible and identify the predictors of depression to improve quality of life in Parkinson’s disease (PD) patients.

AIM

To develop a model for predicting DPD based on the support vector machine, while considering sociodemographic factors, health habits, Parkinson's symptoms, sleep behavior disorders, and neuropsychiatric indicators as predictors and provide baseline data for identifying DPD.

METHODS

This study analyzed 223 of 335 patients who were 60 years or older with PD. Depression was measured using the 30 items of the Geriatric Depression Scale, and the explanatory variables included PD-related motor signs, rapid eye movement sleep behavior disorders, and neuropsychological tests. The support vector machine was used to develop a DPD prediction model.

RESULTS

When the effects of PD motor symptoms were compared using “functional weight”, late motor complications (occurrence of levodopa-induced dyskinesia) were the most influential risk factors for Parkinson's symptoms.

CONCLUSION

It is necessary to develop customized screening tests that can detect DPD in the early stage and continuously monitor high-risk groups based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD patients.

Sleep Disturbance in Mild Cognitive Impairment and Association With Cognitive Functioning. A Case-Control Study

Objectives: The aims of the current study are to (1) report the frequency of specific sleep disturbance symptoms in Mild Cognitive Impairment (MCI) and cognitive healthy older persons; (2) examine whether overall poor sleep and specific sleep disturbance symptoms are more common in persons with MCI compared to cognitive healthy older controls and; (3) examine the association between sleep disturbances and performance in general and specific cognitive domains in persons with MCI and separately in cognitive healthy older persons.

Methods: Data were collected at the Fondazione Ospedale San Camillo Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Venice, Italy as part of the European VPH-DARE@IT project. We included 69 persons with MCI (mean age 75.7; SD = 7.7) and 72 sex-matched cognitively healthy controls (mean age 71.8; SD = 7.0). Participants underwent extensive neuropsychological assessment and evaluation of subjective sleep performance with the Sleep Continuity in Alzheimer’s Disease Scale(SCADS).

Results: A fifth of MCI patients (21.7%, n = 15) had poor sleep compared to 15.3% (n = 11) of cognitively healthy controls. MCI patients had a 3.2 higher odds of having poor sleep compared to cognitively healthy controls after adjustment for age, education, sex, and general cognitive functioning (Odds Ratio (OR) = 3.2; 95% Confidence Interval (CI) = 1.1–9.2). Persons who reported waking up twice or more during the night had higher odds of being MCI compared to those who never wake or wake only once (OR = 2.6; 95% CI = 1.1–6.1). In MCI patients, poor sleep was associated with better general cognitive functioning and short-term working memory, whereas in cognitive healthy older persons poor sleep was associated with impairment in episodic memory performance and executive functioning.

Discussion: Our results confirm previous studies showing that sleep disturbances are common in MCI, and this may be due to an ongoing neurodegenerative process rather than a symptom of cognitive impairment. Future research with objective sleep measurements are needed in MCI as well as interventions to improve sleep with the aim of preventing cognitive decline.

Sleep continuity scale in Alzheimer's disease (SCADS): application in daily clinical practice in an Italian center for dementia

Sleep disorders can occur in many neurodegenerative disorders; in a previous paper we constructed a scale investigating sleep discontinuity/fragmentation with the aim to obtain a rapidly and easily administered tool suitable for early identification and longitudinal monitoring of sleep disturbances in Alzheimer's disease (AD). We introduced this instrument in the daily clinical practice in a center for dementia; here we present the results of our experience. Two hundred and sixteen AD outpatients referred to the Alzheimer's Disease Assessment Unit at the IRCCS C. Mondino National Neurological Institute, Pavia, Italy, in the period October 2012 to March 2014 were administered the scale. The questionnaire global score was correlated with measures of cognitive, functional and behavioral impairment; a significant association was found with Mini-Mental State (p = 0.005), Activities of Daily Living (p = 0.01), Neuropsychiatric Inventory (p = 0.01) and Clinical Dementia Rating (p = 0.0005). The present data indicate that the previously validated questionnaire proves to be a suitable, rapid and easy to use tool in investigating sleep quality in AD in daily clinical practice. An early identification and longitudinal monitoring of sleep disturbances in AD may improve pharmacological and non-pharmacological interventions.