Predictors of asthma control differ from predictors of asthma attacks in children: The Swiss Paediatric Airway Cohort.

BACKGROUND: It is unclear if predictors of asthma attacks are the same as those of asthma symptom control in children. OBJECTIVE: We evaluated predictors for these two outcomes in a clinical cohort study. METHODS: The Swiss Paediatric Airway Cohort (SPAC) is a multicentre prospective clinical cohort of children referred to paediatric pulmonologists. This analysis included 516 children (5-16 years old) diagnosed with asthma. At baseline, we collected sociodemographic information, symptoms, personal and family history and environmental exposures from a parental baseline questionnaire, and treatment and test results from hospital records. Outcomes were assessed 1 year later by parental questionnaire: asthma control in the last 4 weeks as defined by GINA guidelines, and asthma attacks defined as any unscheduled visit for asthma in the past year. We used logistic regression to identify and compare predictors for suboptimal asthma control and asthma attacks. RESULTS: At follow-up, 114/516 children (22%), reported suboptimal asthma control, and 114 (22%) an incident asthma attack. Only 37 (7%) reported both. Suboptimal asthma control was associated with poor symptom control at baseline (e.g. ≥1 night wheeze/week OR: 3.2; 95% CI: 1.7-6), wheeze triggered by allergens (2.2; 1.4-3.3), colds (2.3; 1.4-3.6) and exercise (3.2; 2-5), a more intense treatment at baseline (2.4; 1.3-4.4 for Step 3 vs. 1), history of preschool (2.6; 1.5-4.4) and persistent wheeze (2; 1.4-3.2), and exposure to tobacco smoke (1.7; 1-2.6). Incident asthma attacks were associated with previous episodes of severe wheeze (2; 1.2-3.3) and asthma attacks (2.8; 1.6-5 for emergency care visits), younger age (0.8; 0.8-0.9 per 1 year) and non-Swiss origin (0.3; 0.2-0.5 for Swiss origin). Lung function, exhaled nitric oxide (FeNO) and allergic sensitization at baseline were not associated with control or attacks. CONCLUSION: Children at risk of long-term suboptimal asthma control differ from those at risk of attacks. Prediction tools and preventive efforts should differentiate these two asthma outcomes.

Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep.

Narcolepsy is a chronic disorder of the sleep-wake cycle with pathological shifts between sleep stages. These abrupt shifts are induced by a sleep-regulating flip-flop mechanism which is destabilized in narcolepsy without obvious alterations in EEG oscillations. Here, we focus on the question whether the pathology of narcolepsy is reflected in EEG microstate patterns. 30 channel awake and NREM sleep EEGs of 12 narcoleptic patients and 32 healthy subjects were analyzed. Fitting back the dominant amplitude topography maps into the EEG led to a temporal sequence of maps. Mean microstate duration, ratio total time (RTT), global explained variance (GEV) and transition probability of each map were compared between both groups. Nine patients reached N1, 5 N2 and only 4 N3. All healthy subjects reached at least N2, 19 also N3. Four dominant maps could be found during wakefulness and all NREM- sleep stages in healthy subjects. During N3, narcolepsy patients showed an additional fifth map. The mean microstate duration was significantly shorter in narcoleptic patients than controls, most prominent in deep sleep. Single maps' GEV and RTT were also altered in narcolepsy. Being aware of the limitation of our low sample size, narcolepsy patients showed wake-like features during sleep as reflected in shorter microstate durations. These microstructural EEG alterations might reflect the intrusion of brain states characteristic of wakefulness into sleep and an instability of the sleep-regulating flip-flop mechanism resulting not only in pathological switches between REM- and NREM-sleep but also within NREM sleep itself, which may lead to a microstructural fragmentation of the EEG.

EEG microstates of wakefulness and NREM sleep.

EEG-microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture.