Respiratory airway resistance monitoring in mechanically ventilated patients

Physiological models of respiratory mechanics can be used to optimise mechanical ventilator settings to improve critically ill patient outcomes. Models are generally generated via either physical measurements or analogous behaviours that can model experimental outcomes. However, models derived solely from physical measurements are infrequently applied to clinical data. This investigation assesses the efficacy of a physically derived airway branching model (ABM) to capture clinical data. The ABM is derived via classical pressure-flow equations and branching based on known anatomy. It is compared to two well accepted lumped parameter models of the respiratory system: the linear lung model (LLM) and the Dynostatic Model (DSM). The ABM significantly underestimates the total pressure drop from the trachea to the alveoli. While the LLM and DSM both recorded peak pressure drops of 17.8 cmH2O and 10.2 cmH2O, respectively, the maximum ABM modelled pressure drop was 0.66 cmH2O. This result indicates that the anatomically accurate ABM model does not incorporate all of the airway resistances that are clinically observed in critically ill patients. In particular, it is hypothesised that the primary discrepancy is in the endotracheal tube. In contrast to the lumped parameter models, the ABM was capable of defining the pressure drop in the deep bronchial paths and thus may allow further investigation of alveoli recruitment and gas exchange at that level given realistic initial pressures at the upper airways

Performance of lung recruitment model in healthy anesthetised pigs

Patients with acute respiratory failure are given mechanical ventilation (MV) for treatment and breathing support. During MV, positive end-expiratory pressure (PEEP) is applied to recruit collapsed alveoli and maximized oxygenation. However, there are no well-established methods for quantifying alveoli recruitment with PEEP increase

A Proportional-Derivative Endogenous Insulin Secretion model with an Adapted Gauss Newton Approach

Endogenous insulin (UN) secreted by pancreatic β-cells plays a leading role in glucose homeostasis. Pathological changes in UN can enable early diagnosis of metabolic dysfunction before the emergence of type 2 diabetes. The dynamic insulin sensitivity and secretion test (DISST) is a dynamic test that is able to quantify participant-specific insulin sensitivity (SI) values and UN profiles. Like most studies, the DISST uses direct inversion of C-peptide concentration measurements to quantify a UN profile which relies on the assumption that insulin and C-peptide are equimolarly secreted from β-cells. This study develops a proportional-derivative (PD) control model that defines UN as a function of glucose concentration to provide further insight and modeling capability for this prediabetic state. Results show that individuals with normal glucose tolerance (NGT) tend to have higher gain ratio compared to individuals with impaired fasting glucose (IFG) with median values of 19.11 and 2.79 min, respectively. In particular, the main difference between the UN profiles of NGT and IFG group lies within the derivative gain (), specifically in first phase secretion (U1). A higher value of is needed in response to an abrupt increase in plasma glucose level. This proposed model offers model simplicity as well as a link between insulin secretion and glucose concentration that is able to provide more information in determining each participant’s glycemic condition

Implementation of a Non-Linear Autoregressive Model with Modified Gauss-Newton Parameter Identification to Determine Pulmonary Mechanics of Respiratory Patients that are Intermittently Resisting Ventilator Flow Patterns

Modelling the respiratory system of intensive care patients can enable individualized mechanical ventilation therapy and reduce ventilator induced lung injuries. However, spontaneous breathing (SB) efforts result in asynchronous pressure waveforms that mask underlying respiratory mechanics. In this study, a nonlinear auto-regressive (NARX) model was identified using a modified Gauss-Newton (GN) approach, and demonstrated on data from one SB patient. The NARX model uses three pressure dependent basis functions to capture respiratory system elastance, and contains a single resistance coefficient and positive end expiratory pressure (PEEP) coefficient. The modified GN method exponentially reduces the contribution of large residuals on the step in the coefficients at each GN iteration. This approach allows the model to effectively ignore the anomaly in the pressure waveform due to SB efforts, while successfully describing the shape of normal breathing cycles. This method has the potential to be used in the ICU to more robustly capture patient-specific behaviour, and thus enable clinicians to select optimal ventilator settings and improve patient car

Optimal PEEP - the final solution: Model-based mechanical ventilation for intensive care

invited, 1-pag

A pressure reconstruction method for spontaneous breathing effort monitoring

Introduction: Estimating respiratory mechanics of mechanically ventilated (MV) patients is unreliable when patients exhibit spontaneous breathing (SB) efforts on top of ventilator support. This reverse triggering effect [1] results in an M-wave shaped pressure wave. A model-based method to reconstruct the affected airway pressure curve is presented to enable estimation of the true underlying respiratory mechanics of these patients. Methods: Airway pressure and flow data from 72 breaths of a pneumonia patient were used for proof of concept. A pressure wave reconstruction method ‘fills’ parts of the missing area caused by SB efforts and reverse triggering by connecting the peak pressure and end inspiration slope (Figure 1). A time-varying elastance model [2] is then used to identify underlying respiratory elastance (AUCEdrs). The area of the unreconstructed M-wave has less pressure, resulting in a lower overall AUCEdrs without reconstruction. The missing area of the airway pressure or AUCEdrs is hypothesized to be a surrogate of patient-specific inspiratory to assess the strength of SB efforts. AUCEdrs and missing area A2 are compared with/without reconstruction. Results: Median AUCEdrs and breath-specific effort using reconstruction were 24.99[IQR:22.90-25.98] cmH2O/l and 3.64 [IQR:0.00-3.87] % versus AUCEdrs of 20.87[IQR:15.24-27.48] cmH2O/l for unreconstructed M-wave data, indicating significant patient and breath specific SB effort, and the expected higher elastance (p < 0.05). Conclusions: A simple reconstruction method enables the real-time measurements respiratory system properties of a SB patient and measure the surrogate of the SB effort, that latter of which has clinical useful in deciding whether to extubate or re-sedate the patient

Patient-Ventilator Interaction using Time-varying Pulmonary Mechanics - A Retrospective Comparison between Pressure Support and Neurally Adjusted Ventilatory Assist

peer reviewe

Detecting Ventilation Asynchronies Using Time-Variant Respiratory Mechanics

Asynchronous patient-ventilator interaction risks inadequate oxygenation that can cause prolonged mechanical ventilation requirement. Currently, patient-ventilator asynchrony events cannot be automatically detected in real-time and the problem is essentially neglected. This research presents a model-based approach to automatically detect asynchrony events in real-time