Automated Detection of Incomplete Exhalation as an Indirect Detection of Auto-PEEP on Mechanically Ventilated Adults

Auto-PEEP is auto positive end-expiratory pressure due to excessive amounts of alveolar gas produced by sustained recurrent incomplete exhalation. Incomplete exhalation occurs when the exhaled breath never reaches a flow rate of 0 L/min. The objective of this dissertation is to develop an automated detection system of auto-PEEP through incomplete exhalation as revealed by ventilator graphics for mechanically ventilated adults. Auto-PEEP can cause adverse effects if allowed to linger and if not quickly identified. An automated detection system will be instrumental in helping to quickly identify auto-PEEP. A computerized algorithm was developed to detect incomplete exhalation based on the following three parameters:1) Foi, was used to represent the value of the flow at the onset of inhalation, 2) ∆T, was used to represent the value of time difference between onset inhalation to the 0 L/min mark, and 3) slope threshold, a value set for the slope of change of flow over ∆T. Optimum parameters of the algorithm were achieved for Foi = -3 L/min, ∆T = 0.2 s, and slope threshold = 90 L-s/min. A novel data set was introduced to validate the algorithm, yielding no significant difference in true positive rates (t = 1.5, df = 12.402, p-value = 0.1408) and false positive rates (t = 1.9, df = 16.765, p-value = 0.0725) as outcomes for two-tailed t-tests comparing the novel and old data set. To determine the relationship between auto-PEEP and detection of sustained incomplete exhalation, a correlation of a linear model of the novel data set between auto-PEEP and the percentage of incomplete exhalation detection out of the existing breaths (index) was investigated. A linear model should interpret the index value that corresponds to significant auto-PEEP presence; unfortunately, no significant linear model was found between incomplete exhalation index and auto-PEEP (F1,62 = 1.67, p-value = 0.2010). However, there was a relationship between the intrinsic PEEP values and the incomplete exhalation index as functions of time. The automated detection algorithm produced by this work provides a non-invasive method of automatically detecting auto-PEEP

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

Background: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. Methods: Changes in patient-specific lung elastance over a pressure–volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. Results: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony. Conclusions: Experimental test-lung validation demonstrates the method’s reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool

Patient-ventilator interaction using autoencoder derived magnitude of asynchrony breathing

The occurrence of asynchronous breathing (AB) is prevalent during mechanical ventilation (MV) treatment. Despite studies being carried out to elucidate the impact of AB on MV patients, the asynchrony index, a metric to describe the patient-ventilator interaction, may not be sufficient to quantify the severity of each AB fully in MV patients. This research investigates the feasibility of using a machine learning-derived metric, the ventilator interaction index, to describe a patient’s interaction with a mechanical ventilator. VI is derived using the magnitude of a breath’s asynchrony to measure how well patient is interacting with the ventilator. 1,188 hours of hourly and for 13 MV patients were computed using a convolution neural network and an autoencoder. Pearson’s correlation analysis between patients’ and versus their levels of partial pressure oxygen (PaO2) and partial pressure of carbon dioxide (PaCO2) was carried out. In this patient cohort, the patients’ median is 38.4% [Interquartile range (IQR): 25.9-48.8], and the median is 86.0% [IQR: 76.5-91.7]. Results show that high AI does not necessarily predispose to low. This difference suggests that every AB poses a different magnitude of asynchrony that may affect patient’s PaO2 and PaCO2. Quantifying hourly along with during MV could be beneficial in explicating the aetiology of AB

Model-based patient matching for in-parallel pressure-controlled ventilation

Background: Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. Methods: The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. Results: The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. Conclusion: This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials

Simulation of ventilation distribution and gas transport during oscillatory ventilation

High frequency oscillatory ventilation (HFOV) relies on low tidal volumes cycled at supraphysiologic rates, producing fundamentally different mechanisms for gas transport and exchange compared to conventional mechanical ventilation. Despite the appeal of using low tidal volumes to mitigate the risks of ventilator- induced lung injury (VILI), HFOV does not improve mortality in most clinical applications. One possible explanation for this is that HFOV distributes flows throughout the lung in a non-uniform and frequency-dependent manner, especially in the presence of mechanical heterogeneity. This thesis is a systematic investigation of the relationship between carbon dioxide elimination and frequency content during oscillatory ventilation, with emphasis on the frequency- dependent effects of mechanical heterogeneity and various gas transport mechanisms. A computational model consisting of an anatomically-structured airway network was used to simulate ventilation distribution and gas exchange in a canine lung. These simulations were validated against theoretical predictions and experimental data for eucapnic oscillatory ventilation. The model was also used to assess the impact of mechanical heterogeneity on ventilation distribution and gas transport. Simulations demonstrated a critical transition at the resonant frequency, above which the ventilation patterns became spatially clustered and frequency-dependent. Finally, the model demonstrated that pairs of oscillatory frequencies could yield eucapnic conditions with less potential for VILI compared to traditional single frequency HFOV. These results illustrate the importance of frequency selection in managing the distribution of ventilation and gas transport in the heterogeneous lung, and suggest that the frequency content in oscillatory waveforms may be optimized to achieve eucapnic gas exchange using less injurious ventilation.2017-10-27T00:00:00

Novel techniques for lung volume reduction and its assessment in emphysema

Many patients with emphysema remain breathless despite optimal medical therapy. Non-pharmacological approaches to reduce the volume of hyperinflated lungs include lung volume reduction surgery (LVRS) which is effective in selected patients with upper lobe predominant emphysema and low exercise capacity. Bronchoscopic techniques to reduce lung volume are also being developed. Studies of two bronchoscopic techniques to achieve lung volume reduction (LVR) are presented in this thesis; LVR coils (LVRCs) and endobronchial autologous blood instillation. In a trial of LVRCs we demonstrate for the first time in a randomised controlled setting, that treatment with LVRCs results in statistically and clinically meaningful improvements in quality of life, lung function and exercise capacity compared with controls, and that benefits are maintained up to 12 months following treatment compared to baseline. In two pilot studies, we used autologous blood instilled endobronchially aiming to achieve lung volume reduction by inducing parenchymal scarring and fibrosis. Instilling 180-240 mls of autologous blood withdrawn from patients during the bronchoscopic procedure directly into a giant bullae resulted in significant reduction in bulla size over subsequent months in three of five patients, with associated improvements in lung function, exercise capacity and quality of life. However a randomised controlled trial of instilling 60 mls of autologous blood into three segments of one lobe in patients with heterogeneous emphysema was ineffective. In addition, I investigated the use of a novel 3-dimentional measurement system, optoelectronic plethysmography (OEP), to track abdominal and chest wall movements during respiration. This showed that successful lung volume reduction approaches were associated with significant improvements in lower rib cage paradoxical inspiratory movements after lung volume reduction. Improvements in chest wall asynchrony were larger the worse the asynchrony was at baseline, and those with larger improvements in asynchrony derived greater benefits in lung function and other clinical outcomes following LVR.Open Acces

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included