Precision Medicine in Rapid Eye Movement Sleep Behavior Disorder

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

BACKGROUND

The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.

METHOD

The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.

RESULTS

Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.

CONCLUSION

Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

Age estimation from sleep studies using deep learning predicts life expectancy

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

What Is the Prognostic Significance of Rapid Eye Movement Sleep Without Atonia in a Polysomnogram?

SUMMARY

Freud said we are lucky to be paralyzed during sleep, so we cannot act out our dreams. Atonia of skeletal muscles normally present during rapid eye movement sleep prevents us from acting out our dreams. Observing rapid eye movement sleep without atonia in a polysomnogram in older adults first and foremost warrants consideration of rapid eye movement behavior disorder. Seventy-five to 90% of older adults with isolated rapid eye movement behavior disorder will develop a neurodegenerative disease within 15 years, most often a synucleinopathy. Rapid eye movement sleep without atonia in those younger than 50 years is commonly found in individuals with narcolepsy and those taking antidepressant medications.

Current Update on Clinically Relevant Sleep Issues in Parkinson's Disease: A Narrative Review

Sleep disturbances are among the common nonmotor symptoms in patients with Parkinson’s disease (PD). Sleep can be disrupted by nocturnal motor and nonmotor symptoms and other comorbid sleep disorders. Rapid eye movement sleep behavior disorder (RBD) causes sleep-related injury, has important clinical implications as a harbinger of PD and predicts a progressive clinical phenotype. Restless legs syndrome (RLS) and its related symptoms can impair sleep initiation. Excessive daytime sleepiness (EDS) is a refractory problem affecting patients’ daytime activities. In particular, during the COVID-19 era, special attention should be paid to monitoring sleep problems, as infection-prevention procedures for COVID-19 can affect patients’ motor symptoms, psychiatric symptoms and sleep. Therefore, screening for and managing sleep problems is important in clinical practice, and the maintenance of good sleep conditions may improve the quality of life of PD patients. This narrative review focused on the literature published in the past 10 years, providing a current update of various sleep disturbances in PD patients and their management, including RBD, RLS, EDS, sleep apnea and circadian abnormalities.

The Isolated Form of Rapid Eye Movement Sleep Behavior Disorder: The Upcoming Challenges

The diagnosis of rapid eye movement (REM) sleep behavior disorder (SBD) requires videopolysomnography detection of excessive electromyographic activity during REM sleep, which is time consuming and difficult. An easier, faster, reliable, and reproducible methodology is needed for its diagnosis. The isolated form of RBD represents an early manifestation of the synucleinopathies Parkinson disease and dementia with Lewy bodies. There is a need to find neuroprotective drugs capable of preventing parkinsonism and dementia onset in isolated RBD. Clonazepam and melatonin ameliorate the RBD symptoms, but therapeutic alternatives are needed when these medications fail or show produce side effects.

Specialist approaches to prognostic counseling in isolated REM sleep behavior disorder

OBJECTIVES/BACKGROUND

Most middle-aged and older adult patients with isolated (idiopathic) REM sleep behavior disorder (RBD) eventually develop parkinsonism, dementia with Lewy bodies, or multiple system atrophy. We aimed to describe the current sleep medicine specialist approach toward RBD prognostic counseling, and to determine physician beliefs and characteristics that impact provision of counseling.

PATIENTS/METHODS

We surveyed 70 sleep medicine physicians with RBD expertise for demographic information, counseling practices, and their beliefs and understandings concerning the association between RBD and synucleinopathies, among other questions. Responses were summarized by descriptive statistics.

RESULTS

Among the 44 respondents (63% response rate), 41 (93.2%) regularly provided prognostic counseling for most RBD patients, but only 31.8% routinely asked about patient preferences on receiving counseling. 41.8% believed that the risk for developing overt synucleinopathy following RBD diagnosis was >80%, but only 15.9% routinely provided this detailed phenoconversion risk estimate to their patients. Most respondents were concerned that RBD prognostic counseling could adversely impact on the patient's and family's mental health.

CONCLUSIONS

Most expert RBD sleep clinicians routinely counsel their patients regarding the high risk for phenoconversion to parkinsonism or dementia, yet relatively few routinely ask patients about their preferences for receiving this information, and fewer provide details concerning the known high risk estimates for developing a synucleinopathy. Future research should analyze patients' values and preferences in RBD populations to inform approaches toward shared decision making for RBD prognostic counseling.