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

Traversing the Fuzzy Valley: Problems caused by reliance on default simulation and parameter identification programs for discontinuous models

invited, 6-pagesThe Levenberg-Marquardt parameter identification method is often used in tandem with numerical Runge-Kutta model simulation to find optimal model parameter values to match measured data. However, these methods can potentially find erroneous parameter values. The problem is exacerbated when discontinuous models are analyzed. A highly parameterized respiratory mechanics model defines a pressure-volume response to a low flow experiment in an acute respiratory distress syndrome patient. Levenberg-Marquardt parameter identification is used with various starting values and either a typical numerical integration model simulation or a novel error-stepping method. Model parameter values from the error-stepping method were consistently located close to the error minima (median deviation: 0.4%). In contrast, model values from numerical integration were erratic and distinct from the error minima (median deviation: 1.4%). The comparative failure of Runge-Kutta model simulation was due to the method’s poor handling of model discontinuities and the resultant lack of smoothness in the error surface. As the Leven-berg-Marquardt identification system is an error gradient decent method, it depends on accurate measurement of the model-to-measured data error surface. Hence, the method failed to converge accurately due to poorly defined error surfaces. When the error surface is imprecisely identified, the parameter identification process can produce sub-optimal results. Particular care must be used when gradient decent methods are used in conjunction with numerical integration model simulation methods and discontinuous models

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

Effect of various Neurally adjusted ventilatory assist (NAVA) gains on the relationship between diaphragmatic activity (Eadi max) and tidal volume (Vt)

ESICM 2011 programme is available in notes http://www.springerlink.com/content/m0xth64u3885w270/</a

Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

Monitoring patient-specific respiratory mechanics can be used to guide mechanical ventilation (MV) therapy in critically ill patients. However, many patients can exhibit spontaneous breathing (SB) efforts during ventilator supported breaths, altering airway pressure waveforms and hindering model-based (or other) identification of the true, underlying respiratory mechanics necessary to guide MV. This study aims to accurately assess respiratory mechanics for breathing cycles masked by SB efforts. A cumulative pressure reconstruction method is used to ameliorate SB by identifying SB affected waveforms and reconstructing unaffected pressure waveforms for respiratory mechanics identification using a single-compartment model. Performance is compared to conventional identification without reconstruction, where identified values from reconstructed waveforms should be less variable. Results are validated with 9485 breaths affected by SB, including periods of muscle paralysis that eliminates SB, as a validation test set where reconstruction should have no effect. In this analysis, the patients are their own control, with versus without reconstruction, as assessed by breath-to-breath variation using the non-parametric coefficient of variation (CV) of respiratory mechanics. Pressure reconstruction successfully estimates more consistent respiratory mechanics. CV of estimated respiratory elastance is reduced up to 78% compared to conventional identification (p < 0.05). Pressure reconstruction is comparable (p > 0.05) to conventional identification during paralysis, and generally performs better as paralysis weakens, validating the algorithm’s purpose. Pressure reconstruction provides less-affected pressure waveforms, ameliorating the effect of SB, resulting in more accurate respiratory mechanics identification. Thus providing the opportunity to use respiratory mechanics to guide mechanical ventilation without additional muscle relaxants, simplifying clinical care and reducing risk

A Polynomial Model of Patient-specific Breathing Effort During Controlled Mechanical Ventilation

Patient breathing efforts occurring during controlled ventilation causes perturbations in pressure data, which cause erroneous parameter estimation in conventional models of respiratory mechanics. A polynomial model of patient effort can be used to capture breath-specific effort and underlying lung condition. An iterative multiple linear regression is used to identify the model in clinical volume controlled data. The polynomial model has lower fitting error and more stable estimates of respiratory elastance and resistance in the presence of patient effort than the conventional single compartment model. However, the polynomial model can converge to poor parameter estimation when patient efforts occur very early in the breath, or for long duration. The model of patient effort can provide clinical benefits by providing accurate respiratory mechanics estimation and monitoring of breath-to-breath patient effort, which can be used by clinicians to guide treatment

Model-based approach to estimate Dfrc in the ICU using measured lung dynamics

6-pagesAcute Respiratory Distress Syndrome (ARDS) is characterized by inflammation, filling of the lung with fluid and collapsed lung unit. Mechanical ventilation (MV) is used to treat ARDS/ALI using positive end expiratory pressure (PEEP) to recruit and retain lung units, thus increasing pulmonary volume and dynamic functional residual capacity (dFRC) at the end of expiration. However, simple methods to measure dFRC at the bedside currently do not exist and other methods are invasive and impractical to carry out on a regular basis. Stress-strain theory is used to estimate ΔdFRC, which represents the extra pulmonary volume due to PEEP, utilizing readily available patient data from a single breath. The model uses commonly controlled or measured parameters (lung compliance, plateau airway pressure, PV data) to identify a parameter ß_1 as a function of PEEP and tidal volume. A median ß_1 value is calculated for each PEEP level over a cohort and is hypothesised as a constant throughout the population for the particular PEEP. Estimated ΔdFRC values are then compared to measured values to assess accuracy of the model. ΔdFRC was calculated for 9 patients and compared to the measured values. The median percentage error was 40.29% [IQR: 14.20-55.39] for PEEP = 5cmH2O, 31.12% [IQR: 10.53-192.71] for PEEP = 10cmH2O, 20.8% [IQR: 7.51-81.06] for PEEP = 15cmH2O, 15.44% [IQR: 11.92-36.18] for PEEP = 20cmH2O, 19.7% [IQR: 4.79-20.76] for PEEP = 25cmH2O and 11.78% [IQR: 2.99-27.5] for PEEP = 30cmH2O. Linear regression between estimated and measured ΔdFRC produced R2 = 0.862. The model-based approach offers a simple and non-invasive method which does not require interruption of MV to estimate dFRC. The clinical accuracy of the model is limited but was able to track the impact of changes in PEEP and tidal volume on dFRC, on a breath-by-breath basis for each PEEP

PEEP in mechanically ventilated patients: a clinical proof of concept

The optimal level of positive end-expiratory pressure (PEEP) is widely debated in ARDS. Current methods of selecting PEEP do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical utility of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, and assess the disease state and response over time. Ten patients with ALI or ARDS were given incremental PEEP (increments: 5cmH2O; maximum PIP: 45cmH2O) under volume controlled ventilation. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Data were fitted to a recruitment model. PEEP was optimised based on model-based: 1) threshold opening pressures (TOP); 2) threshold closing pressures (TCP); and 3) net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance over time. Median model fitting error for inflation and deflation was 2.8% and 1.02%. All patients experienced auto-PEEP. Model-based optimal PEEP had a range of [5, 27], [10, 25] and [10, 30]cmH2O for the TOP, TCP and net recruitment metrics and was greater than clinically selected. Model-identified patient-specific compliance changed over time with patient condition, and was associated with a significant change in model-selected PEEP, indicating the model’s utility in tracking disease status. The model-based method presented in this paper provides a unique, non-invasive method to optimise patient-specific PEEP and assess disease state over time

A Novel Visualisation System for ICU Nursing Effort Per-Patient

Patient and nurse interaction in the Intensive Care Unit (ICU) influences patient recovery, care and outcome [1-3]. In particular, it is imperative to build up a nurse-to-patient ratio system for ICU. However, most of evaluation systems are patient oriented, focusing on assessing the patient condition. The assumption made in these evaluation systems is that the sicker the patient, the higher nursing care provision is required. These systems are broad and may not easily differentiate patients needing more or less care. There is no standard method to consistently quantify patient and bedside nurse interaction. This paper presents a new Nurse Motion Tracking System (NMTS), which is developed to track and evaluate nursing motion at the patient bedside, aimed to quantify the time nurses spend on nursing activities

Structural identifiability and practical applicability of an alveolar recruitment model for ARDS patients

"(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Mathematical models of respiratory mechanics can offer substantial insight into patient state and pulmonary dynamics that are not directly measurable. Thus, they offer significant potential to evaluate and guide patient-specific lung protective ventilator strategies for Acute Respiratory Distress Syndrome (ARDS) patients. To assure bedside-applicability, the model has to be computationally efficient and identifiable from the available data, while also capturing dominant dynamics observed in ARDS patients. In this work, a recruitment model is enhanced by considering alveolar distension and implemented in a time-continuous respiratory mechanics model. A hierarchical gradient decent approach is used to fit the model to low-flow test responses of 12 ARDS patients. The reported parameter values were physiologically plausible and capable of reproducing the measured pressure responses with high accuracy. Structural identifiability of the model is proven, but a practical identifiability analysis of the results shows a lack of convexity on the error-surface. Covariance analyses reveal limited influence of particular model parameters during parameter identification indicating that successful parameter identification is currently not assured in all test sets. Overall, the presented model is physiologically and clinically relevant, captures ARDS dynamics, and uses clinically descriptive parameters. The patient-specific models show its ability to capture pulmonary dynamics directly relevant to patient condition and clinical guidance. These characteristics can currently not be directly measured or established without such a validated model