Cerebral oxygenation during respiratory events in children with sleep-disordered breathing

Author(s):  
Laurence Tabone ◽  
Sonia Khirani ◽  
Jorge Olmo Arroyo ◽  
Alessandro Amaddeo ◽  
Lucie Griffon ◽  
...  
2019 ◽  
Vol 214 ◽  
pp. 134-140.e7 ◽  
Author(s):  
Laurence Tabone ◽  
Sonia Khirani ◽  
Jorge Olmo Arroyo ◽  
Alessandro Amaddeo ◽  
Abdelkebir Sabil ◽  
...  

SLEEP ◽  
2019 ◽  
Vol 42 (5) ◽  
Author(s):  
Knarik Tamanyan ◽  
Aidan Weichard ◽  
Sarah N Biggs ◽  
Margot J Davey ◽  
Gillian M Nixon ◽  
...  

PEDIATRICS ◽  
1996 ◽  
Vol 98 (5) ◽  
pp. 871-882 ◽  
Author(s):  
Christian Guilleminault ◽  
Rafael Pelayo ◽  
Damien Leger ◽  
Alex Clerk ◽  
Robert C. Z. Bocian

Objective. To determine whether upper airway resistance syndrome (UARS) can be recognized and distinguished from obstructive sleep apnea syndrome (OSAS) in prepubertal children based on clinical evaluations, and, in a subgroup of the population, to compare the efficacy of esophageal pressure (Pes) monitoring to that of transcutaneous carbon dioxide pressure (tcPco2) and expired carbon dioxide (CO2) measurements in identifying UARS in children. Study Design. A retrospective study was performed on children, 12 years and younger, seen at our clinic since 1985. Children with diagnoses of sleep-disordered breathing were drawn from our database and sorted by age and initial symptoms. Clinical findings, based on interviews and questionnaires, an orocraniofacial scale, and nocturnal polygraphic recordings were tabulated and compared. If the results of the first polygraphic recording were inconclusive, a second night's recording was performed with the addition of Pes monitoring. In addition, simultaneous measurements of tcPco2 and endtidal CO2 with sampling through a catheter were performed on this second night in 76 children. These 76 recordings were used as our gold standard, because they were the most comprehensive. For this group, 1848 apneic events and 7040 abnormal respiratory events were identified based on airflow, thoracoabdominal effort, and Pes recordings. We then analyzed the simultaneously measured tcPCo2 and expired CO2 levels to ascertain their ability to identify these same events. Results. The first night of polygraphic recording was inconclusive enough to warrant a second recording in 316 of 411 children. Children were identified as having either UARS (n = 259), OSAS (n = 83), or other sleep disorders (n = 69). Children with small triangular chins, retroposition of the mandible, steep mandibular plane, high hard palate, long oval-shaped face, or long soft palate were highly likely to have sleep-disordered breathing of some type. If large tonsils were associated with these features, OSAS was much more frequently noted than UARS. In the 76 gold standard children, Pes, tcPco2, and expired CO2 measurements were in agreement for 1512 of the 1848 apneas and hypopneas that were analyzed. Of the 7040 upper airway resistance events, only 2314 events were consonant in all three measures. tcPco2 identified only 33% of the increased respiratory events identified by Pes; expired CO2 identified only 53% of the same events. Conclusions. UARS is a subtle form of sleep-disordered breathing that leads to significant clinical symptoms and day and nighttime disturbances. When clinical symptoms suggest abnormal breathing during sleep but obstructive sleep apneas are not found, physicians may, mistakenly, assume an absence of breathing-related sleep problems. Symptoms and orocraniofacial information were not useful in distinguishing UARS from OSAS but were useful in distinguishing sleep-disordered breathing (UARS and OSAS) from other sleep disorders. The analysis of esophageal pressure patterns during sleep was the most revealing of the three techniques used for recognizing abnormal breathing patterns during sleep.


2020 ◽  
Vol 34 ◽  
pp. 18-23
Author(s):  
Laurence Tabone ◽  
Sonia Khirani ◽  
Alessandro Amaddeo ◽  
Guillaume Emeriaud ◽  
Brigitte Fauroux

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
D Linz ◽  
C Nalliah ◽  
M Baumert ◽  
K Kadhim ◽  
M Middeldorp ◽  
...  

Abstract Background Studies investigating the relationship between sleep-disordered breathing (SDB) and atrial fibrillation (AF) have largely assessed SDB-severity by the apnea–hypopnea index (AHI). However, the AHI does not incorporate nocturnal hypoxemic burden, which may increase the risk of non-paroxysmal AF (nPAF) as the clinical manifestation of more progressed AF substrates. This investigation sought to systematically characterize and compare the composition of AHI and hypoxemic burden with the aim to defining a disease-orientated metric for SDB-severity best associated with prevalent nPAF. Methods Polysomnography including overnight oximetry data were obtained in 435 consecutive ambulatory AF patients to determine the composition of AHI (apneas vs. hypopneas), the number of acute episodic desaturations per hour (oxygen desaturation index, ODI) and the composition of total time spent below 90% oxygen saturation (T90Total) attributed to acute desaturations (T90Desaturation). Logistic regression analysis was used to characterize the association with prevalent nPAF. Results One hundred sixty-nine AF patients (38%) had nPAF and one third (n=149, 34%) had moderate-to-severe SDB (AHI>15). 82% of the median total AHI (9.4 [3.6–20.1]) could be attributed to hypopneas. Only 29% of events were associated with episodic desaturations, which contributed to 96% (T90Desaturation) of the variation in T90Total. The high variability in durations and nadirs of distinct desaturation events can expose patients to long T90Total, even if the AHI is low. Not AHI, but T90Total and ODI were associated with nPAF independent of gender and age. However, diabetes, hypertension and body mass index contributed more significantly to the overall risk of nPAF. Conclusions In AF patients, hypopneas constitute a majority of respiratory events during sleep. Patients with low AHI can still be exposed to high nocturnal hypoxemic burden, which is mainly a cumulative consequence of episodic desaturations. T90Total and ODI, but not AHI, were associated with nPAF independent of gender and age, but concomitant modifiable risk factors made a more significant contribution to the overall risk of nPAF versus PAF.


2005 ◽  
Vol 6 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Indu Ayappa ◽  
Beth S. Rapaport ◽  
Robert G. Norman ◽  
David M. Rapoport

2015 ◽  
Vol 78 (5) ◽  
pp. 560-566 ◽  
Author(s):  
Lisa M. Walter ◽  
Sarah N. Biggs ◽  
Lauren C. Nisbet ◽  
Aidan J. Weichard ◽  
Milou Muntinga ◽  
...  

2019 ◽  
Vol 90 (3) ◽  
pp. e20.1-e20
Author(s):  
L Pérez-Carbonell ◽  
S Higgins ◽  
M Koutroumanidis ◽  
G Leschziner

ObjectivesVagal nerve stimulation (VNS) is a neuromodulatory therapy indicated in drug-resistant epilepsy (DRE). Its side effects are frequently minor, however, sleep-disordered breathing (SDB) has been previously reported.1Obstructive sleep apnoea (OSA) is highly prevalent in individuals with refractory epilepsy, and may be a cause of poor control of seizures.2MethodsThree DRE patients with active VNS underwent a video-polysomnography with 21-channel montage electroencephalography in our centre.ResultsFirst and second patients showed OSA at the time of VNS activation. In the first patient, the apnoeic-induced arousals triggered VNS auto-firing and consequent respiratory events, perpetuating the SDB. The third patient had episodes of stridor, and an increased respiratory rate, coinciding with VNS activation. Our cases are representative of different forms of SDB that occurred as a consequence of the switch-on phase of the VNS device.ConclusionsSleep-related breathing disturbances should be considered before VNS implantation, and should be routinely assessed after having started the therapy. Changes in stimulation parameters, and positive airway pressure therapy, may be required to treat the SDB.


SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A228-A229
Author(s):  
S Zeineddine ◽  
A Sankari ◽  
k Arvai ◽  
A Salloum ◽  
Y Abu Awad ◽  
...  

Abstract Introduction Sleep-disordered breathing (SDB) is highly prevalent among patients with spinal cord injury or disease (SCI/D). In-laboratory polysomnography (PSG) is difficult for these patients due to functional limitations and the physical construction of most sleep laboratories. Our objective was to evaluate the concordance between simulated HSAT and PSG in identifying SDB severity and subtypes of respiratory events in this patient population. Methods Within a larger study, 33 Veterans with SCI/D completed one night of in-laboratory PSG. Limited-channel HSAT was simulated by extracting 5 channels from PSG signals to include nasal pressure, thermistor, thoracic and abdominal belts, and oxygen saturation. Results Mean age of patients was 59.8 ± 10.9 years; 87.9% were male, and the average BMI was 28.1 ± 6.3. The mean Apnea-Hypopnea Index (AHI) from PSG was 35.5 ± 22.7. The mean Respiratory Event Index (REI) based on simulated HSAT was 22.5 ± 18.6. Thirty-one patients (93.9%) had SDB defined as AHI ≥5/hour. Simulated limited-channel HSAT accurately identified 32 out of 33 patients (96.96%). When SDB was further classified into mild (AHI 5-15 events/hr), moderate (AHI 15-30 events/hr), and severe (AHI>30/hr), simulated HSAT consistently underestimated the severity of underlying SDB. Spearman correlation between estimating AHI (PSG-HSAT) and subtypes of respiratory events was primarily accounted for by the difference in the number of hypopneas (r=0.72, -0.021 and -0.001 for hypopneas, obstructive and central apneas, respectively). Conclusion Our findings support the diagnostic utility of HSAT in SCI/D patients with SDB; however, HSAT underestimation of SDB may lead to difficulties in optimizing therapy. The misclassification of SDB severity is mainly driven by the number of hypopneas. Classification of hypopneas as obstrcutive or central may shed further light on the nature of this difference. Further research on the usability of HSAT devices in this patient population is needed. Support VA Rehabilitation Research and Development Service (RX002116; PI Badr and RX002885; PI Sankari) and NIH/NHLBI (K24HL143055; PI: Martin)


Sign in / Sign up

Export Citation Format

Share Document