[Show abstract][Hide abstract]ABSTRACT: Rationale:
Sleep disorders may lead to stress-induced premature aging and telomere shortening.
To determine whether obstructive sleep apnea syndrome causing intermittent hypoxemia episodes was associated with telomere shortening independently from the comorbidities associated with this syndrome.
Cross-sectional study in 161prospectivelly enrolled untreated middle-aged men free of known comorbidities related or unrelated to sleep apnea. Each participant underwent full standard overnight polysomnography. Patients with apnea sleep syndrome were naïve of treatment.
Measurements and main results:
By univariate analysis, telomere shortening was associated with older age, apnea-hypopnea index, oxygen desaturation index, apnea-hypopnea index, waist circumference, and fat mass. After adjustment for age, only apnea-hypopnea index and oxygen desaturation index were significantly related to telomere shortening. Mean telomere length ratio was 0.70±0.37 in the participants without sleep apnea, compared to 0.61±0.22 and 0.53±0.16 in those with mild-to-moderate and severe sleep apnea, respectively (P=0.01). By multivariate analysis, oxygen desaturation index was the only factor independently associated with telomere length. Arterial stiffness assessed by carotid-femoral pulse wave velocity correlated negatively with telomere length.
Intermittent hypoxemia due to obstructive sleep apnea syndrome is a major contributor to telomere shortening in middle-aged males. Oxidative stress may explain this finding.
Article · May 2016 · Annals of the American Thoracic Society
[Show abstract][Hide abstract]ABSTRACT: Purpose:
Sleep is an essential physiologic process that helps to restore normal body homeostasis. Sleep disturbances have been shown to be associated with poor clinical outcomes, such as a greater risk of cardiovascular disease and increasing mortality. Critically ill patients, particularly those receiving mechanical ventilation, may be more susceptible to sleep disruption.
Methods and results:
Mechanical ventilation is an important factor influencing sleep in critically ill patients as it may have positive or negative effects, depending on patient population, mode, and specific settings. Other causes of sleep disruption include the acute illness itself, the daily routine care, and the effects of medications. Improving sleep in patients admitted to an intensive care unit has the potential to improve both short- and long-term clinical outcomes. In this article we review the specific aspects of sleep in critically ill mechanically ventilated patients, including abnormal sleep patterns and loss of circadian rhythm, as well as the effects of mechanical ventilation and intravenous sedatives on sleep quality and quantity.
We provide recommendations for clinicians regarding optimal ventilatory settings and discuss fields for future research.
[Show abstract][Hide abstract]ABSTRACT: Reconnecting the ventilator in patient with prolonged weaning in the ICU. In tracheostomized patient, the ventilator should be restored at night to promote better sleep.
[Show abstract][Hide abstract]ABSTRACT: Promoting sleep using noninvasive ventilation in the ICU. To improve sleep during nighttime, noninvasive ventilation should be considered in patients with acute hypercapneic respiratory failure.
[Show abstract][Hide abstract]ABSTRACT: Background:
Sleep disordered breathing (SDB) is common in patients with heart failure with reduced ejection fraction (HFrEF). An increased apnea-hypopnea index (AHI) is associated with poor outcomes. We examined whether an analysis of nocturnal desaturations (NDs) can improve the risk stratification.
Three-hundred seventy-six consecutive patients with stable chronic HFrEF and LVEF ≤45% were prospectively screened using polygraphy. Sleep apnea (SA) was defined as an AHI ≥15. The mean age was 59±13years, the mean LVEF was 30±6%, and the median AHI was 18 [IQR: 9.33). The composite end-point of death, heart transplantation or LV assistance occurred in 98 patients (26%) within 3years. Minimal oxygen saturation (MOS) during sleep, the number of desaturations <90%/h and the time spent with oxygen saturation <90% were significantly associated with adverse events (adjusted HR 1.25 [1.03-1.52], 1.25 [1.03-1.53], and 1.28 [1.04-1.59]), whereas the AHI was not (1.10 [0.86-1.39]). The best MOS cut-off value for poor outcomes was ≤88%. The patients with an MOS ≤88% had a significantly higher event rate (31.9%) than those with an MOS >88% (15.6%; p<0.01). The risk assessment using an MOS of ≤88% in addition to established prognostic markers yielded a net reclassification index (NRI) of nearly 6% and was particularly useful in the subgroup of patients with events (NRI: 8.4%).
In HFrEF patients, ND ≤88% appears to be predictive of adverse events, independent of the presence of SA. This suggests that the risk assessment in HFrEF should also include ND in top of AHI.
Article · Nov 2015 · International journal of cardiology
[Show abstract][Hide abstract]ABSTRACT: 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.
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.
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.
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.
[Show abstract][Hide abstract]ABSTRACT: INTRODUCTION: Polysomnography (PSG) is the recording during sleep of physiological parameters
enabling to diagnose sleep disorders and to characterize sleep fragmentation. This work we propose to
build a mathematical model of sleep fragmentation diagnosis based on three main sleep characteristics
each having its own threshold and weight values for each clinician. MATERIALS AND METHODS: From
PSG several sleep characteristics such as the number of sleep stages shifts (SSS), the micro arousal rate
(MAR) and the number of intra sleep awakenings (ISA) can be deduced each having its own
fragmentation threshold value and each being more or less important (weight) in the clinician’s
diagnosis according to his specialization (pulmonologist or neurophysiologist). We use a decision
algorithm which consists in assigning the value 1 if a patient's sleep is considered as fragmented and the
value 0 if it is not. This allows representing by an index, on the one hand each clinician’s diagnosis.
RESULTS: Thus, from a database of 111 PSG consisting of 55 healthy adults and 56 adult patients with a
suspicion of obstructive sleep apnoea syndrome (OSAS), we show that a measurement of the agreement
between each clinician’s diagnosis (CDI) and each corresponding mathematical model (MDI) is
substantial. CONCLUSION: It follows from this result that each of our predictive mathematical model
MDI of sleep fragmentation diagnosis is a posteriori validated for each clinician.
ACKNOWLEDGEMENTS: Authors would like to thank the staff of the sleep laboratory of Ste Musse's
Hospital.These research have been partially supported by Isis Medical®.
[Show abstract][Hide abstract]ABSTRACT: La polysomnographie (PSG) est l’examen de référence dans le diagnostic des troubles du sommeil. Il consiste en l’enregistrement d’un très grand nombre de variables ventilatoires et neurophysiologique permettant par leur analyse simultanée le codage en stades de sommeil.
Afin de déterminer la qualité du sommeil d’un patient, le clinicien évalue sa fragmentation à partir de plusieurs critères déduits de la PSG tels que le nombre de changement de stades (SSS), le taux de micro-éveil (MAR) et le nombre d’éveils intra-sommeil (ISA). Chacun de ces critères ayant son seuil de fragmentation et son importance (poids) dans le diagnostic du clinicien en fonction de sa spécialité (pneumologue ou neurophysiologiste).
Ce travail a pour but de modéliser le diagnostic de la fragmentation du sommeil sur la base de ces trois caractéristiques du sommeil (MAR, SSS, ISA). Nous utilisons un algorithme de décision qui consiste à attribuer la valeur 1 si le sommeil d'un patient est considéré comme fragmenté et la valeur 0 s’il ne l'est pas.
Ceci permet de représenter par un indice, d'une part, le diagnostic du clinicien (CDI) et, d'autre part, le modèle mathématique de ce diagnostic (MDI).
Ainsi, à partir de 111 PSG provenant du laboratoire de sommeil du centre hospitalier de Toulon composé de 55 sujets sains et 56 patients avec une suspicion d’un syndrome d'apnées obstructives du sommeil, les valeurs de seuil et de poids impliqués dans notre MDI est statistiquement déterminés pour chaque clinicien.
L'accord entre le MDI et le diagnostic de chaque clinicien est substantiel (valeur ?, test Kappa).
Cet outil, spécifique à la spécialité du clinicien, peut être implémenté sur les logiciels de polysomnographie.
[Show abstract][Hide abstract]ABSTRACT: IntroductionSleep in intensive care unit (ICU) patients is severely altered. In a large proportion of critically ill patients, conventional sleep electroencephalogram (EEG) patterns are replaced by atypical sleep. On the other hand, some non-sedated patients can display usual sleep EEG patterns. In the latter, sleep is highly fragmented and disrupted and conventional rules may not be optimal. We sought to determine whether sleep continuity could be a useful metric to quantify the amount of sleep with recuperative function in critically ill patients with usual sleep EEG features.Methods
We retrospectively reanalyzed polysomnographies recorded in non-sedated critically ill patients requiring non-invasive ventilation (NIV) for acute hypercapnic respiratory failure. Using conventional rules, we built two-state hypnograms (sleep and wake) and identified all sleep episodes. The percentage of time spent in sleep bouts (<10 min), short naps (>10 and <30 min) and long naps (>30 min) was used to describe sleep continuity. In a first study, we compared these measures regarding good (NIV success) or poor outcome (NIV failure). In a second study performed on a different patient group, we compared these measurements during NIV and during spontaneous breathing.ResultsWhile fragmentation indices were similar in the two groups, the percentage of total sleep time spent in short naps was higher and the percentage of sleep time spent in sleep bouts was lower in patients with successful NIV. The percentage of total sleep time spent in long naps was higher and the percentage of sleep time spent in sleep bouts was lower during NIV than during spontaneous breathing; the level of reproducibility of sleep continuity measures between scorers was high.Conclusion
Sleep continuity measurements could constitute a clinically relevant and reproducible assessment of sleep disruption in non-sedated ICU patients with usual sleep EEG.
Full-text Article · Nov 2014 · Critical care (London, England)