Carole Pesenti

Car crashes and central disorders of hypersomnolence: a French study

Background Drowsiness compromises driving ability by reducing alertness and attentiveness, and delayed reaction times. Sleep-related car crashes account for a considerable proportion of accident at the wheel. Narcolepsy type 1 (NT1), narcolepsy type 2 (NT2) and idiopathic hypersomnia (IH) are rare central disorders of hypersomnolence, the most severe causes of sleepiness thus being potential dangerous conditions for both personal and public safety with increasing scientific, social, and political attention. Our main objective was to assess the frequency of recent car crashes in a large cohort of patients affected with well-defined central disorders of hypersomnolence versus subjects from the general population. Methods We performed a cross-sectional study in French reference centres for rare hypersomnia diseases and included 527 patients and 781 healthy subjects. All participants included needed to have a driving license, information available on potential accident events during the last 5 years, and on potential confounders; thus analyses were performed on 282 cases (71 IH, 82 NT2, 129 NT1) and 470 healthy subjects. Results Patients reported more frequently than healthy subjects the occurrence of recent car crashes (in the previous five years), a risk that was confirmed in both treated and untreated subjects at study inclusion (Untreated, OR = 2.21 95%CI = [1.30–3.76], Treated OR = 2.04 95%CI = [1.26–3.30]), as well as in all disease categories, and was modulated by subjective sleepiness level (Epworth scale and naps). Conversely, the risk of car accidents of patients treated for at least 5 years was not different to healthy subjects (OR = 1.23 95%CI = [0.56–2.69]). Main risk factors were analogous in patients and healthy subjects. Conclusion Patients affected with central disorders of hypersomnolence had increased risk of recent car crashes compared to subjects from the general population, a finding potentially reversed by long-term treatment.

Effect of psychostimulants on blood pressure profile and endothelial function in narcolepsy

Objective To assess the effect of psychostimulant treatments on the 24-hour blood pressure (BP) profile of patients with narcolepsy type 1 (NT1). Methods Heart rate (HR) and BP were monitored for 24 hours and morning endothelial function was evaluated in 160 consecutive patients with NT1: 68 untreated (41 male, median age 34.9 years), 54 treated (32 male, median age 40.9 years), and 38 evaluated twice (21 male, median age 32 years), before and during treatment. Results Patients treated for NT1 showed higher 24-hour, daytime, and nighttime diastolic BP and HR values compared with the untreated group. Similarly, HR as well as 24-hour and daytime systolic BP were increased during treatment in the group evaluated twice. The combination of stimulant and anticataplectic drugs showed a synergistic effect on BP, without differences among stimulant categories. Based on 24-hour BP monitoring, hypertension was diagnosed in 58% of treated patients and in 40.6% of untreated patients. After adjustments for age, sex, and body mass index, the percentage of REM sleep remained associated with 24-hour hypertension in untreated and treated patients. Endothelial function was comparable in treated and untreated patients. Conclusions The finding that patients with NT1 treated with psychostimulants have higher diastolic BP and HR than untreated patients suggests an increased long-term risk of cardiovascular diseases that requires careful follow-up and specific management.

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing ‘ideas’ and promising candidates for future diagnostic classifications.