Oximetry Alone Versus Portable Polygraphy for Sleep Apnea Screening Before Bariatric Surgery

Background: Screening for obstructive sleep apnea (OSA) is recommended as part of the preoperative assessment of obese patients scheduled for bariatric surgery. The objective of this study was to compare the sensitivity of oximetry alone versus portable polygraphy in the preoperative screening for OSA. Methods: Polygraphy (type III portable monitor) and oximetry data recorded as part of the preoperative assessment before bariatric surgery from 68 consecutive patients were reviewed. We compared the sensitivity of 3% or 4% desaturation index (oximetry alone) with the apnea-hypopnea index (AHI; polygraphy) to diagnose OSA and classify the patients as normal (30 events per hour). Results: Using AHI, the prevalence of OSA (AHI > 10 per hour) was 57.4%: 16.2% of the patients were classified as severe, 41.2% as mild to moderate, and 42.6% as normal. Using 3% desaturation index, 22.1% were classified as severe, 47.1% as mild to moderate, and 30.9% as normal. With 4% desaturation index, 17.6% were classified as severe, 32.4% as mild, and 50% as normal. Overall, 3% desaturation index compared to AHI yielded a 95% negative predictive value to rule out OSA (AHI > 10 per hour) and a 100% sensitivity (0.73 positive predictive value) to detect severe OSA (AHI > 30 per hour). Conclusions: Using oximetry with 3% desaturation index as a screening tool for OSA could allow us to rule out significant OSA in almost a third of the patients and to detect patients with severe OSA. This cheap and widely available technique could accelerate preoperative work-up of these patient

Effect of transnasal insufflation on sleep disordered breathing in acute stroke: a preliminary study

Background and Purpose: Sleep disordered breathing (SDB) is frequent in acute stroke patients and is associated with early neurologic worsening and poor outcome. Although continuous positive airway pressure (CPAP) effectively treats SDB, compliance is low. The objective of the present study was to assess the tolerance and the efficacy of a continuous high-flow-rate air administered through an open nasal cannula (transnasal insufflation, TNI), a less-intrusive method, to treat SDB in acute stroke patients. Methods: Ten patients (age, 56.8 ± 10.7years), with SDB ranging from moderate to severe (apnea-hypopnea index, AHI, >15/h of sleep) and on a standard sleep study at a mean of 4.8 ± 3.7days after ischemic stroke (range, 1-15days), were selected. The night after, they underwent a second sleep study while receiving TNI (18L/min). Results: TNI was well tolerated by all patients. For the entire group, TNI decreased the AHI from 40.4 ± 25.7 to 30.8 ± 25.7/h (p = 0.001) and the oxygen desaturation index >3% from 40.7 ± 28.4 to 31 ± 22.5/h (p = 0.02). All participants except one showed a decrease in AHI. The percentage of slow-wave sleep significantly increased with TNI from 16.7 ± 8.2% to 22.3 ± 7.4% (p = 0.01). There was also a trend toward a reduction in markers of sleep disruption (number of awakenings, arousal index). Conclusions: TNI improves SDB indices, and possibly sleep parameters, in stroke patients. Although these changes are modest, our findings suggest that TNI is a viable treatment alternative to CPAP in patients with SDB in the acute phase of ischemic strok

The effect of continuous positive airway pressure on total cerebral blood flow in healthy awake volunteers

Purpose: Continuous positive airway pressure (CPAP) is the gold standard treatment for obstructive sleep apnea. However, the physiologic impact of CPAP on cerebral blood flow (CBF) is not well established. Ultrasound can be used to estimate CBF, but there is no widespread accepted protocol. We studied the physiologic influence of CPAP on CBF using a method integrating arterial diameter and flow velocity (FV) measurements obtained for each vessel supplying blood to the brain. Methods: FV and lumen diameter of the left and right internal carotid, vertebral, and middle cerebral arteries were measured using duplex Doppler ultrasound with and without CPAP at 15cmH2O, applied in a random order. Transcutaneous carbon dioxide (PtcCO2), heart rate (HR), blood pressure (BP), and oxygen saturation were monitored. Results were compared with a theoretical prediction of CBF change based on the effect of partial pressure of carbon dioxide on CBF. Results: Data were obtained from 23 healthy volunteers (mean ± SD; 12 male, age 25.1 ± 2.6years, body mass index 21.8 ± 2.0kg/m2). The mean experimental and theoretical CBF decrease under CPAP was 12.5% (p < 0.001) and 11.9% (p < 0.001), respectively. The difference between experimental and theoretical CBF reduction was not statistically significant (3.84 ± 79ml/min, p = 0.40). There was a significant reduction in PtcCO2 with CPAP (p = <0.001) and a significant increase in mean BP (p = 0.0017). No significant change was observed in SaO2 (p = 0.21) and HR (p = 0.62). Conclusion: Duplex Doppler ultrasound measurements of arterial diameter and FV allow for a noninvasive bedside estimation of CBF. CPAP at 15cmH2O significantly decreased CBF in healthy awake volunteers. This effect appeared to be mediated predominately through the hypocapnic vasoconstriction coinciding with PCO2 level reduction. The results suggest that CPAP should be used cautiously in patients with unstable cerebral hemodynamic

Oximetry alone versus portable polygraphy for sleep apnea screening before bariatric surgery.

BACKGROUND: Screening for obstructive sleep apnea (OSA) is recommended as part of the preoperative assessment of obese patients scheduled for bariatric surgery. The objective of this study was to compare the sensitivity of oximetry alone versus portable polygraphy in the preoperative screening for OSA. METHODS: Polygraphy (type III portable monitor) and oximetry data recorded as part of the preoperative assessment before bariatric surgery from 68 consecutive patients were reviewed. We compared the sensitivity of 3% or 4% desaturation index (oximetry alone) with the apnea-hypopnea index (AHI; polygraphy) to diagnose OSA and classify the patients as normal (&lt;10 events per hour), mild to moderate (10-30 events per hour), or severe (&gt;30 events per hour). RESULTS: Using AHI, the prevalence of OSA (AHI &gt; 10 per hour) was 57.4%: 16.2% of the patients were classified as severe, 41.2% as mild to moderate, and 42.6% as normal. Using 3% desaturation index, 22.1% were classified as severe, 47.1% as mild to moderate, and 30.9% as normal. With 4% desaturation index, 17.6% were classified as severe, 32.4% as mild, and 50% as normal. Overall, 3% desaturation index compared to AHI yielded a 95% negative predictive value to rule out OSA (AHI &gt; 10 per hour) and a 100% sensitivity (0.73 positive predictive value) to detect severe OSA (AHI &gt; 30 per hour). CONCLUSIONS: Using oximetry with 3% desaturation index as a screening tool for OSA could allow us to rule out significant OSA in almost a third of the patients and to detect patients with severe OSA. This cheap and widely available technique could accelerate preoperative work-up of these patients

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering.

BACKGROUND AND OBJECTIVES Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed and the question arises whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see if data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers. METHODS We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups. RESULTS We included 1078 unmedicated adolescents and adults. Seven clusters were identified, of which four clusters included predominantly individuals with cataplexy. The two most distinct clusters consisted of 158 and 157 patients respectively, were dominated by those without cataplexy and, amongst other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening and weekend-week sleep length difference. Patients formally diagnosed as narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these two clusters. DISCUSSION Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset rapid eye moment periods (SOREMPs) in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

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.</p

Respiratory Polygraphy Patterns and Risk of Recurrent Cardiovascular Events in Patients With Acute Coronary Syndrome

Introduction: Obstructive sleep apnea (OSA) severity is based on the apnea-hypopnea index (AHI). The AHI is a simplistic measure that is inadequate for capturing disease severity and its consequences in cardiovascular diseases (CVDs). Deleterious effects of OSA have been suggested to influence the prognosis of specific endotypes of patients with acute coronary syndrome (ACS). We aim to identify respiratory polygraphy (RP) patterns that contribute to identifying the risk of recurrent cardiovascular events in patients with ACS. Methods: Post hoc analysis of the ISAACC study, including 723 patients admitted for a first ACS (NCT01335087) in which RP was performed. To identify specific RP patterns, a principal component analysis (PCA) was performed using six RP parameters: AHI, oxygen desaturation index, mean and minimum oxygen saturation (SaO2), average duration of events and percentage of time with SaO2 < 90%. An independent HypnoLaus population-based cohort was used to validate the RP components. Results: From the ISAACC study, PCA showed that two RP components accounted for 70% of the variance in the RP data. These components were validated in the HypnoLaus cohort, with two similar RP components that explained 71.3% of the variance in the RP data. The first component (component 1) was mainly characterized by low mean SaO2 and obstructive respiratory events with severe desaturation, and the second component (component 2) was characterized by high mean SaO2 and long-duration obstructive respiratory events without severe desaturation. In the ISAACC cohort, component 2 was associated with an increased risk of recurrent cardiovascular events in the third tertile with an adjusted hazard ratio (95% CI) of 2.44 (1.07 to 5.56; p-value = 0.03) compared to first tertile. For component 1, no significant association was found for the risk of recurrent cardiovascular events. Conclusion: A RP component, mainly characterized by intermittent hypoxemia, is associated with a high risk of recurrent cardiovascular events in patients without previous CVD who have suffered a first ACS.Instituto de Salud Carlos III (ISCIII; PI10/02763, PI10/02745, PI18/00449, and PI19/00907), co-funded by FEDER, “Una manera de hacer europa,” IRBLleida – Fundació Pifarré, CERCA Programme/Generalitat de Catalunya, SEPAR, ResMed Ltd. (Australia), Esteve-Teijin (Spain), Oxigen Salud (Spain), Associació Lleidatana de Respiratori (ALLER), and Sociedad Española de Sueño (SES). AZ is the recipient of a predoctoral fellowship “Ajuts 2021 de Promoció de la Recerca en Salut-9a edició” from IRBLleida/Diputació de Lleida. JD acknowledges receiving financial support from ISCIII (Miguel Servet 2019: CP19/00108), co-funded by the European Social Fund (ESF), “Investing in your future.” MS-d-l-T has received financial support from a “Ramón y Cajal” grant (RYC2019-027831-I) from the “Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación” co-funded by the European Social Fund (ESF)/“Investing in your future.” FB received funding from from ResMed (an Australian company that develops products related to sleep apnea), the Health Research Fund, the Spanish Ministry of Health, the Spanish Respiratory Society, the Catalonian Cardiology Society, Esteve-Teijin (Spain), Oxigen Salud (Spain), and ALLER. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication

Characteristics and Determinants of Respiratory Event-Associated Leg Movements

Abstract Study Objectives To (1) replicate the recently described distribution of respiratory event-associated leg movements (rLMs) in participants with mild-to-moderate obstructive sleep apnea syndrome (OSAS), (2) explore global and local factors associated with the presence of rLMs, and (3) investigate differences related to OSAS severity and periodic leg movements during sleep (PLMS) status. Methods We randomly selected six groups of participants without restless legs syndrome (12-15 participants in each group), stratified by apnea-hypopnea index (AHI) severity (AHI 10-20, 20-30, and 30-40) and PLMS status (PLMS index 15 per hr) from the population-based HypnoLaus study that assessed full polysomnography at home in participants aged 40 to 80 years, randomly selected from the population register of the city of Lausanne, Switzerland. Results Our results confirmed the distribution of leg movement activity at the end of respiratory events (−2.0 to +10.25 s). Mixed effects logistic regression modeling rLM-probability showed that rLMs were more frequent in participants with high-PLMS, at the end of obstructive apneas (vs. hypopneas) and in the presence of arousals at the end of the events. In participants with high-PLMS, rLM-probability decreased with time of night and was more reduced during REM sleep (vs. NREM sleep), whereas the duration of the respiratory event had a significant effect only in participants with low-PLMS. Conclusions We confirm the previously reported distribution of rLMs in participants with mild-to-moderate OSAS and our results suggest that rLMs are sensitive to both sleep-related and respiratory-related factors in a complex interaction with the PLMS status

Physical activity is associated with higher sleep efficiency in the general population: the CoLaus study

Abstract Study Objectives To evaluate the association of objective physical activity (PA) and sedentary behavior (SB) with sleep duration and quality. Methods Cross-sectional study including 2649 adults (53.5% women, 45-86 years) from the general population. Proportions of time spent in PA and SB were measured using 14 day accelerometry. Low PA and high SB statuses were defined as the lowest and highest tertile of each behavior. "Inactive,” "Weekend warrior,” and "Regularly active” weekly patterns were also defined. Sleep parameters were derived from the accelerometer and validated questionnaires. Results High PA, relative to low PA, was associated with higher sleep efficiency (76.6 vs. 73.8%, p < 0.01) and lower likelihood of evening chronotype [relative-risk ratio (RR) and 95% CI: 0.71 (0.52; 0.97)]. Similar associations were found for low SB relative to high SB. "Weekend warriors” relative to "Inactives,” had higher sleep efficiency [76.4 vs. 73.9%, p < 0.01] and lower likelihood of evening chronotype [RR: 0.63 (0.43; 0.93)]. "Regularly actives,” relative to "Inactives,” had higher sleep efficiency [76.7 vs. 73.9%, p < 0.01] and tended to have less frequently an evening chronotype [RR: 0.75 (0.54; 1.04), p = 0.09]. No associations were found for PA and SB with sleep duration, daytime sleepiness, insomnia, and risk of sleep apnea (after adjustment for body mass index). Conclusions High PA and low SB individuals, even if they do not sleep longer, have higher sleep efficiency and have less frequently an evening chronotype