Role of surgical hyoid bone repositioning in modifying upper airway collapsibility

Background: Surgical hyoid bone repositioning procedures are being performed to treat obstructive sleep apnea (OSA), though outcomes are highly variable. This is likely due to lack of knowledge regarding the precise influence of hyoid bone position on upper airway patency. The aim of this study is to determine the effect of surgical hyoid bone repositioning on upper airway collapsibility.Methods: Seven anaesthetized, male, New Zealand White rabbits were positioned supine with head/neck position controlled. The rabbit’s upper airway was surgically isolated and hyoid bone exposed to allow manipulation of its position using a custom-made device. A sealed facemask was fitted over the rabbit’s snout, and mask/upper airway pressures were monitored. Collapsibility was quantified using upper airway closing pressure (Pclose). The hyoid bone was repositioned within the mid-sagittal plane from 0 to 5 mm (1 mm increments) in anterior, cranial, caudal, anterior-cranial (45°) and anterior-caudal (45°) directions.Results: Anterior displacement of the hyoid bone resulted in the greatest decrease in Pclose amongst all directions (p = 0.002). Pclose decreased progressively with each increment of anterior hyoid bone displacement, and down by −4.0 ± 1.3 cmH2O at 5 mm. Cranial and caudal hyoid bone displacement did not alter Pclose (p > 0.35). Anterior-cranial and anterior-caudal hyoid bone displacements decreased Pclose significantly (p < 0.004) and at similar magnitudes to the anterior direction (p > 0.68).Conclusion: Changes in upper airway collapsibility following hyoid bone repositioning are both direction and magnitude dependent. Anterior-based repositioning directions have the greatest impact on reducing upper airway collapsibility, with no effect on collapsibility by cranial and caudal directions. Findings may have implications for guiding and improving the outcomes of surgical hyoid interventions for the treatment of OSA

Snoring-related energy transmission to the carotid artery in rabbits

Epidemiological studies link habitual snoring and stroke, but mechanisms involved are poorly understood. One previously advanced hypothesis is that transmitted snoring vibration energy may promote carotid atheromatous plaque formation or rupture. To test whether vibration energy is present in carotid artery walls during snoring we developed an animal model in which we examined induced snoring (IS)-associated tissue energy levels. In six male, supine, anesthetized, spontaneously breathing New Zealand White rabbits, we surgically inserted pressure transducer-tipped catheters (Millar) to monitor tissue pressure at the carotid artery bifurcation (PCT) and within the carotid sinus lumen (PCS; artery ligated). Snoring was induced via external compression (sandbag) over the pharyngeal region. Data were analyzed using power spectral analysis for frequency bands above and below 50 Hz. For frequencies below 50 Hz, PCT energy was 2.2 (1.1–12.3) cmH₂O² [median (interquartile range)] during tidal breathing (TB) increasing to 39.0 (2.5–95.0) cmH₂O² during IS (P = 0.05, Wilcoxon's signed-rank test). For frequencies >50 Hz, PCT energy increased from 9.2 (8.3–10.4) x 10⁻⁴ cmH₂O² during TB to 172.0 (118.0–569.0) x 10⁻⁴ cmH₂O² during IS (P = 0.03). Concurrently, PCS energy was 13.4 (8.5–18.0) x 10⁻⁴ cmH₂O² during TB and 151.0 (78.2–278.8) x 10⁻⁴ cmH₂O² during IS (P < 0.03). The PCS energy was greater than PCT energy for the 100–275 Hz bandwidth. In conclusion, during IS there is increased energy around and within the carotid artery, including lower frequency amplification for PCS. These findings may have implications for carotid atherogenesis and/or plaque rupture.7 page(s

An automated and reliable method for breath detection during variable mask pressures in awake and sleeping humans

<div><p>Accurate breath detection is crucial in sleep and respiratory physiology research and in several clinical settings. However, this process is technically challenging due to measurement and physiological artifacts and other factors such as variable leaks in the breathing circuit. Recently developed techniques to quantify the multiple causes of obstructive sleep apnea, require intermittent changes in airway pressure applied to a breathing mask. This presents an additional unique challenge for breath detection. Traditional algorithms often require drift correction. However, this is an empirical operation potentially prone to human error. This paper presents a new algorithm for breath detection during variable mask pressures in awake and sleeping humans based on physiological landmarks detected in the airflow or epiglottic pressure signal (Pepi). The algorithms were validated using simulated data from a mathematical model and against the standard visual detection approach in 4 healthy individuals and 6 patients with sleep apnea during variable mask pressure conditions. Using the flow signal, the algorithm correctly identified 97.6% of breaths with a mean difference±SD in the onsets of respiratory phase compared to expert visual detection of 23±89ms for inspiration and 6±56ms for expiration during wakefulness and 10±74ms for inspiration and 3±28 ms for expiration with variable mask pressures during sleep. Using the Pepi signal, the algorithm correctly identified 89% of the breaths with accuracy of 31±156ms for inspiration and 9±147ms for expiration compared to expert visual detection during variable mask pressures asleep. The algorithm had excellent performance in response to baseline drifts and noise during variable mask pressure conditions. This new algorithm can be used for accurate breath detection including during variable mask pressure conditions which represents a major advance over existing time-consuming manual approaches.</p></div

Development and validation of a computational finite element model of the rabbit upper airway : simulations of mandibular advancement and tracheal displacement

The mechanisms leading to upper airway (UA) collapse during sleep are complex and poorly understood. We have previously developed an anaesthetized rabbit model for studying UA physiology. Based on this body of physiological data, we aimed to develop and validate a two-dimensional (2D) computational finite element (FEM) of the passive rabbit UA and peripharyngeal tissues.Model geometry was reconstructed from a mid-sagittal CT image of a representative NZ White rabbit, which included major soft (tongue, soft palate, constrictor muscles), cartilaginous (epiglottis, thyroid cartilage) and bony pharyngeal tissues (mandible, hard palate, hyoid bone). Other UA muscles were modeled as linear elastic connections. Initial boundary and contact definitions were defined from anatomy and material properties derived from the literature. Model parameters were optimized to physiological data sets associated with mandibular advancement (MA) and caudal tracheal displacement (TD), including hyoid displacement, which featured with both applied loads. The model was then validated against independent data sets involving combined MA and TD. Model outputs included UA lumen geometry, peripharyngeal tissue displacement, stress and strain distributions.Simulated MA and TD resulted in UA enlargement and non-uniform increases in tissue displacement, stress and strain. Model predictions closely agreed with experimental data for individually applied MA, TD and their combination.We have developed and validated an FEM of the rabbit UA which predicts UA geometry and peripharyngeal tissue mechanical changes associated with interventions known to improve UA patency. The model has the potential to advance our understanding of UA physiology and peripharyngeal tissue mechanics.15 page(s

Effect of sampling frequency and random noise on the accuracy of the proposed algorithm to detect inspiratory (a-b) and expiratory onsets (c-d).

<p>Noise level of less than 5% in the flow signal is typical to that seen in most experimental settings.</p

Mathematical model of a flow signal that contains inflection points.

<p>Amplitude modulation, random trend and random noise were added to the model to simulate fluctuations in amplitude, baseline shift and noise, respectively. For further detail on the model refer to the text and Figure A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179030#pone.0179030.s001" target="_blank">S1 File</a>. Note: the red line shows the random trend simulating fluctuations in baseline shift of the flow signal, which consequently renders traditional volume-drift correction algorithms inaccurate. Noise level is set to 2% in this example.</p

Bias and variability of inspiratory time (Ti) (a-b) and expiratory time (Te) (c-d) when applying the proposed algorithm on flow signals of clinical datasets during transient reductions in continuous positive airway pressure during wakefulness.

<p>True values were visually scored by an expert investigator blinded to the algorithm data.</p

Key steps of the proposed breath detection algorithm.

<p>Briefly, peaks and valleys of flow (or pressure) are identified (circles and triangular points) (a), smoothing curves are fitted to raw data (b-c, upper panels) and the second derivatives of the fitted curves are calculated (b-c, lower panels). Onsets of inspiration are located at the maximum and minimum points (diamonds) of the second derivate of flow and pressure signal, respectively (b-c, lower panels). Pepi: epiglottic pressure.</p

Performance of the proposed algorithm (compared to visual expert analysis) in detecting inspiratory and expiratory onsets from flow signal and epiglottic pressure signal (Pepi).

<p>Performance of the proposed algorithm (compared to visual expert analysis) in detecting inspiratory and expiratory onsets from flow signal and epiglottic pressure signal (Pepi).</p