Objective: To obtain insight in the spectrum of narcolepsy symptoms and associated burden in a large cohort of patients. Methods: We used the Narcolepsy Monitor, a mobile app, to easily rate the presence and burden of 20 narcolepsy symptoms. Baseline measures were obtained and analyzed from 746 users aged between 18 and 75 years with a reported diagnosis of narcolepsy. Results: Median age was 33.0 years (IQR 25.0–43.0), median Ullanlinna Narcolepsy Scale 19 (IQR 14.0–26.0), 78% reported using narcolepsy pharmacotherapy. Excessive daytime sleepiness (97.2%) and lack of energy were most often present (95.0%) and most often caused a high burden (79.7% and 76.1% respectively). Cognitive symptoms (concentration 93.0%, memory 91.4%) and psychiatric symptoms (mood 76.8%, anxiety/panic 76.4%) were relatively often reported to be present and burdensome. Conversely, sleep paralysis and cataplexy were least often reported as highly bothersome. Females experienced a higher burden for anxiety/panic, memory, and lack of energy. Conclusions: This study supports the notion of an elaborate narcolepsy symptom spectrum. Each symptom’s contribution to the experienced burden varied, but lesser-known symptoms did significantly add to this as well. This emphasizes the need to not only focus treatment on the classical core symptoms of narcolepsy.@en

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online. @en

Objective: Studies have shown an increased cardiovascular risk in obstructive sleep apnea (OSA) patients. In order to prioritize treatment of high risk patients, there is a need for improved cardiovascular OSA phenotyping. This study investigates the use of oxygen saturation (SpO 2 ) parameters for cardiovascular risk assessment of OSA patients. To this end, a novel multilevel interval coded scoring (mICS) algorithm is proposed. Methods: The study includes SpO 2 recordings from 1987 overnight polysomnographies, of which 974 are from patients suspected to have OSA, 931 from the general population based Sleep Heart Health Study and 83 from healthy controls. The minimal SpO 2 value, SpO 2 upslope and amplitude ratio of desaturation over resaturation are extracted for all oxygen desaturations and averaged per patient. These three SpO 2 parameters are used together with patient demographics to develop a mICS model to predict the probability that a patient had a cardiovascular condition, or had already experienced a cardiovascular event, at the time of the polysomnography. Results: Including the SpO 2 parameters in the mICS together with age and BMI improves the model's performance by 2.7% and leads to a test area under the curve (AUC) of 69.5% for the detection of any cardiovascular comorbidity. Moreover, an increase in AUC of 5% was obtained for the detection of cardiovascular events, resulting in an AUC of 93.5%. Conclusions: This study shows that parameters based on SpO 2 and the mICS model are useful to predict the cardiovascular comorbidity status of OSA patients. Significance: The proposed model could be used to assist in prioritizing OSA patients for treatment.@en