Management Of Excessive Sleepiness In Patients With Narcolepsy And OSA: Current Challenges And Future Prospects

Estimating the prevalence and clinical causality of obstructive sleep apnea in paediatric narcolepsy patients

BACKGROUND

Numerous risk factors in paediatric narcolepsy may predispose them to obstructive sleep apnea (OSA). The concurrent presence of OSA in these patients might lead to underdiagnosing narcolepsy. This research investigates the prevalence and potential causality between OSA and paediatric narcolepsy.

METHODS

A case-control study coupled with a two-sample Mendelian randomization (MR) analysis was employed to explore the prevalence and causal link between paediatric narcolepsy and OSA risk.

RESULTS

The case-control study revealed that paediatric narcolepsy patients are at an increased risk of OSA, with an Odds ratio (OR) of 4.87 (95% CI: 2.20-10.71; P < 0.001). The inverse-variance weighted (IVW) model further suggests a potential causal link between narcolepsy and OSA (IVW OR: 4.671, 95% CI: 1.925-11.290; P < 0.001). Additionally, sensitivity analysis confirmed these findings' reliability.

CONCLUSION

The findings highlight an elevated prevalence and genetic susceptibility to OSA among paediatric narcolepsy patients, underscoring the necessity for clinical screening of OSA. Continued research is essential to clarify the pathogenic mechanisms and develop potential treatments.

Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness

Background

Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.

Objective

The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.

Methods

Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).

Results

Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.

Conclusion

The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.

Machine learning polysomnographically-derived electroencephalography biomarkers predictive of epworth sleepiness scale

Excessive daytime sleepiness (EDS) causes difficulty in concentrating and continuous fatigue during the day. In the clinical setting, the assessment and diagnosis of EDS rely mostly on subjective questionnaires and verbal reports, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of previously collected encephalography (EEG) data to identify surrogate biomarkers for EDS, thereby defining the quantitative EEG changes in individuals with high Epworth Sleepiness Scale (ESS) (n = 31), compared to a group of individuals with low ESS (n = 41) at the Cleveland Clinic. The epochs of EEG analyzed were extracted from a large overnight polysomnogram registry during the most proximate period of wakefulness. Signal processing of EEG showed significantly different EEG features in the low ESS group compared to high ESS, including enhanced power in the alpha and beta bands and attenuation in the delta and theta bands. Our machine learning (ML) algorithms trained on the binary classification of high vs. low ESS reached an accuracy of 80.2%, precision of 79.2%, recall of 73.8% and specificity of 85.3%. Moreover, we ruled out the effects of confounding clinical variables by evaluating the statistical contribution of these variables on our ML models. These results indicate that EEG data contain information in the form of rhythmic activity that could be leveraged for the quantitative assessment of EDS using ML.

pitolisant, a novel histamine-3 receptor competitive antagonist, and inverse agonist, in the treatment of excessive daytime sleepiness in adult patients with narcolepsy

Narcolepsy is a debilitating sleep disorder that presents with excessive daytime sleepiness (EDS) and cataplexy, which is a sudden paralysis of muscle tone triggered by strong emotions such as laughing. It is also associated with many other disorders, including psychiatric disorders, neurologic illnesses, and medication side effects. Common causes of delayed and incorrect diagnoses of these conditions include lack of physician familiarity with narcolepsy symptoms and comorbidities which mask narcolepsy signs and symptoms. Current pharmacologic therapies include Modafinil and Armodafinil for EDS and sodium oxybate for cataplexy. This review discusses the epidemiology, pathophysiology, risk factors, presentation, treatment of narcolepsy, and the role of a novel drug, Pitolisant, in the treatment of EDS in adults with narcolepsy. Pitolisant is a histamine-3 receptor (H3R), competitive antagonist, and inverse agonist, acting through the histamine system to regulate wakefulness. It is a novel drug approved in August 2019 by the FDA, is not classified as a controlled substance, and is approved for use in Europe and the United States to treat EDS and cataplexy in narcolepsy. Recent phase II and III trials have shown that Pitolisant helps reduce the ESS score and cataplexy. In summary, based on comparative studies, recent evidence has shown that Pitolisant is non-inferior to Modafinil in the treatment of EDS but superior to Modafinil in reducing cataplexy.

Narcolepsy: A clinical review

ABSTRACT

Narcolepsy continues to be a significantly underdiagnosed/misdiagnosed condition worldwide. According to the National Institutes of Health (NIH), an estimated 135,000 to 200,000 patients in the United States are living with narcolepsy. However, due to the number of patients who either do not seek medical advice for their symptoms or receive an incorrect initial diagnosis at onset, this number may be higher. This article reviews the different subtypes of narcolepsy along with the pathophysiology, screening guidelines, clinical features, diagnosis, and management of the disorder. Educational awareness from a healthcare and patient standpoint can enhance early detection and accurate diagnosis of narcolepsy and improve patient quality of life.

Solriamfetol for the Management of Excessive Daytime Sleepiness

OBJECTIVE

To review efficacy, safety, and place in therapy of solriamfetol for management of excessive daytime sleepiness (EDS) in patients with narcolepsy and obstructive sleep apnea (OSA).

METHODS

PubMed (1966 to January 2021) was searched using the terms solriamfetol, JZP-110, ADX-N05 and Sunosi. Human studies published in peer-reviewed medical journals in English language were reviewed.

RESULTS

The efficacy and safety of solriamfetol has been reported in 2 phase II trials and 4 phase III trials (TONES 2, TONES 3, TONES 4, and TONES 5). Statistically significant improvements in the maintenance of wakefulness test were reported with solriamfetol 150 mg and 300 mg vs placebo in participants with narcolepsy (7.65- to 10.14-minute difference from placebo). In subjects with OSA, statistically significant improvements in maintenance of wakefulness test difference from placebo were also observed in those taking solriamfetol 75 mg, 150 mg, or 300 mg vs placebo (4.5- to 12.8-minute difference from placebo). Statistically significant reductions in Epworth Sleepiness Scale scores were also reported in phase III trials in subjects with narcolepsy or OSA taking solriamfetol vs placebo (ranging from - 4.7 to - 1.9 difference from placebo). Common adverse events in reported in phase III trials were headache, nausea, decreased appetite, anxiety, dry mouth, and diarrhea. Solriamfetol appears to have a reduced risk for drug interactions and fewer adverse effects compared to other agents available for management of EDS in patients with narcolepsy and OSA.

CONCLUSIONS

Solriamfetol is an effective option for management of EDS in patients with narcolepsy and OSA.