New insights on the pathophysiology of inspiratory flow limitation during sleep

Ventilatory Burden as a Measure of Obstructive Sleep Apnea Severity Is Predictive of Cardiovascular and All-Cause Mortality

Rationale: The apnea-hypopnea index (AHI), used for the diagnosis of obstructive sleep apnea, captures only the frequency of respiratory events and has demonstrable limitations. Objectives: We propose a novel automated measure, termed "ventilatory burden" (VB), that represents the proportion of overnight breaths with less than 50% normalized amplitude, and we show its ability to overcome limitations of AHI. Methods: Data from two epidemiological cohorts (EPISONO [Sao Paolo Epidemiological Study] and SHHS [Sleep Heart Health Study]) and two retrospective clinical cohorts (DAYFUN; New York University Center for Brain Health) were used in this study to 1) derive the normative range of VB, 2) assess the relationship between degree of upper airway obstruction and VB, and 3) assess the relationship between VB and all-cause and cardiovascular disease (CVD) mortality with and without hypoxic burden that was derived using an in-house automated algorithm. Measurements and Main Results: The 95th percentiles of VB in asymptomatic healthy subjects across the EPISONO and the DAYFUN cohorts were 25.2% and 26.7%, respectively (median [interquartile range], VBEPISONO, 5.5 [3.5-9.7]%; VBDAYFUN, 9.8 [6.4-15.6]%). VB was associated with the degree of upper airway obstruction in a dose-response manner (VBuntreated, 31.6 [27.1]%; VBtreated, 7.2 [4.7]%; VBsuboptimally treated, 17.6 [18.7]%; VBoff-treatment, 41.6 [18.1]%) and exhibited low night-to-night variability (intraclass correlation coefficient [2,1], 0.89). VB was predictive of all-cause and CVD mortality in the SHHS cohort before and after adjusting for covariates including hypoxic burden. Although AHI was predictive of all-cause mortality, it was not associated with CVD mortality in the SHHS cohort. Conclusions: Automated VB can effectively assess obstructive sleep apnea severity, is predictive of all-cause and CVD mortality, and may be a viable alternative to the AHI.

Quantifying the magnitude of pharyngeal obstruction during sleep using airflow shape

Rationale and objectives

Non-invasive quantification of the severity of pharyngeal airflow obstruction would enable recognition of obstructiveversuscentral manifestation of sleep apnoea, and identification of symptomatic individuals with severe airflow obstruction despite a low apnoea–hypopnoea index (AHI). Here we provide a novel method that uses simple airflow-versus-time (“shape”) features from individual breaths on an overnight sleep study to automatically and non-invasively quantify the severity of airflow obstruction without oesophageal catheterisation.

Methods

41 individuals with suspected/diagnosed obstructive sleep apnoea (AHI range 0–91 events·h−1) underwent overnight polysomnography with gold-standard measures of airflow (oronasal pneumotach: “flow”) and ventilatory drive (calibrated intraoesophageal diaphragm electromyogram: “drive”). Obstruction severity was defined as a continuous variable (flow:drive ratio). Multivariable regression used airflow shape features (inspiratory/expiratory timing, flatness, scooping, fluttering) to estimate flow:drive ratio in 136 264 breaths (performance based on leave-one-patient-out cross-validation). Analysis was repeated using simultaneous nasal pressure recordings in a subset (n=17).

Results

Gold-standard obstruction severity (flow:drive ratio) varied widely across individuals independently of AHI. A multivariable model (25 features) estimated obstruction severity breath-by-breath (R2=0.58versusgold-standard, p<0.00001; mean absolute error 22%) and the median obstruction severity across individual patients (R2=0.69, p<0.00001; error 10%). Similar performance was achieved using nasal pressure.

Conclusions

The severity of pharyngeal obstruction can be quantified non-invasively using readily available airflow shape information. Our work overcomes a major hurdle necessary for the recognition and phenotyping of patients with obstructive sleep disordered breathing.

Prolonged partial obstruction during sleep is a NREM phenomenon

OBJECTIVE

Prolonged partial obstruction (PPO) is a common finding in sleep studies. Although not verified, it seems to emerge in deep sleep. We study the effect of PPO on sleep architecture or sleep electroencephalography (EEG) frequency.

METHODS

Fifteen OSA patients, 15 PPO + OSA patients and 15 healthy subjects underwent a polysomnography. PPO was detected from Emfit mattress signal. Visual sleep parameters and median NREM sleep frequency of the EEG channels were evaluated.

RESULTS

The amount of deep sleep (N3) did not differ between the PPO + OSA and control groups (medians 11.8% and 13.8%). PPO + OSA-patients' N3 consisted mostly of PPO. PPO + OSA patients had lighter sleep than healthy controls in three brain areas (Fp2-A1, C4-A1, O1-A2, p-values < 0.05).

CONCLUSION

PPO evolved in NREM sleep and especially in N3 indicating that upper airway obstruction does not always ameliorate in deep sleep but changes the type. Even if PPO + OSA-patients had N3, their NREM sleep was lighter in three EEG locations. This might reflect impaired recovery function of sleep.

Weighted Polynomial Approximation for Automated Detection of Inspiratory Flow Limitation

Inspiratory flow limitation (IFL) is a critical symptom of sleep breathing disorders. A characteristic flattened flow-time curve indicates the presence of highest resistance flow limitation. This study involved investigating a real-time algorithm for detecting IFL during sleep. Three categories of inspiratory flow shape were collected from previous studies for use as a development set. Of these, 16 cases were labeled as non-IFL and 78 as IFL which were further categorized into minor level (20 cases) and severe level (58 cases) of obstruction. In this study, algorithms using polynomial functions were proposed for extracting the features of IFL. Methods using first- to third-order polynomial approximations were applied to calculate the fitting curve to obtain the mean absolute error. The proposed algorithm is described by the weighted third-order (w.3rd-order) polynomial function. For validation, a total of 1,093 inspiratory breaths were acquired as a test set. The accuracy levels of the classifications produced by the presented feature detection methods were analyzed, and the performance levels were compared using a misclassification cobweb. According to the results, the algorithm using the w.3rd-order polynomial approximation achieved an accuracy of 94.14% for IFL classification. We concluded that this algorithm achieved effective automatic IFL detection during sleep.

Annual review of selected scientific literature: Report of the committee on scientific investigation of the American Academy of Restorative Dentistry

STATEMENT OF PROBLEM

It is clear the contemporary dentist is confronted with a blizzard of information regarding materials and techniques from journal articles, advertisements, newsletters, the internet, and continuing education events. While some of that information is sound and helpful, much of it is misleading at best.

PURPOSE

This review identifies and discusses the most important scientific findings regarding outcomes of dental treatment to assist the practitioner in making evidence-based choices. This review was conducted to assist the busy dentist in keeping abreast of the latest scientific information regarding the clinical practice of dentistry.

MATERIAL AND METHODS

Each of the authors, who are considered experts in their disciplines, was asked to peruse the scientific literature published in 2015 in their discipline and review the articles for important information that may have an impact on treatment decisions. Comments on experimental methodology, statistical evaluation, and overall validity of the conclusions are included in many of the reviews.

RESULTS

The reviews are not meant to stand alone but are intended to inform the interested reader about what has been discovered in the past year. The readers are then invited to go to the source if they wish more detail.

CONCLUSIONS

Analysis of the scientific literature published in 2015 is divided into 7 sections, dental materials, periodontics, prosthodontics, occlusion and temporomandibular disorders, sleep-disordered breathing, cariology, and implant dentistry.