Expiratory model-based method to monitor ARDS disease state

INTRODUCTION: Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection. METHODS: Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty. RESULTS: The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics. CONCLUSION: Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.Peer reviewe

Integral-Based Identification of an Inhomogeneity Model in Respiratory Mechanics

4-pagesIndividualized models of respiratory mechanics may help to reduce potential harmful effects of ventilation therapy by predicting the outcome of certain ventilator settings. The underlying models are commonly identified by iterative error-mapping methods, such as the Levenberg-Marquardt Algorithm, requiring initial estimates for the patient specific parameters. The quality of the initial estimates has a significant influence on identification efficiency and results. An iterative integral-based parameter identification method was applied to a linear 2nd order respiratory mechanics model. The method was compared to the Levenberg-Marquardt Algorithm using clinical data from 13 Acute Respiratory Distress Syndrome (ARDS) patients. The Iterative Integral-Based Method converged to the Levenberg-Marquardt solution two times faster and was independent of initial estimates. These investigations reveal that the Iterative Integral-Based Method is beneficial with respect to computing time, operator independence and robustness

Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients

The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is highly invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a CO measure without further invasiveness

Dynamic functional residual capacity can be estimated using a stress-strain approach

Invited. Available online 9 June 2010.Background Acute Respiratory Distress Syndrome (ARDS) results in collapse of alveolar units and loss of lung volume at the end of expiration. Mechanical ventilation is used to treat patients with ARDS or Acute Lung Injury (ALI), with the end objective being to increase the dynamic functional residual capacity (dFRC), and thus increasing overall functional residual capacity (FRC). Simple methods to estimate dFRC at a given positive end expiratory pressure (PEEP) level in patients with ARDS/ALI currently does not exist. Current viable methods are time-consuming and relatively invasive. Methods Previous studies have found a constant linear relationship between the global stress and strain in the lung independent of lung condition. This study utilizes the constant stress–strain ratio and an individual patient's volume responsiveness to PEEP to estimate dFRC at any level of PEEP. The estimation model identifies two global parameters to estimate a patient specific dFRC, ß and mß. The parameter ß captures physiological parameters of FRC, lung and respiratory elastance and varies depending on the PEEP level used, and mß is the gradient of ß vs. PEEP. Results dFRC was estimated at different PEEP values and compared to the measured dFRC using retrospective data from 12 different patients with different levels of lung injury. The median percentage error is 18% (IQR: 6.49) for PEEP = 5 cm H2O, 10% (IQR: 9.18) for PEEP = 7 cm H2O, 28% (IQR: 12.33) for PEEP = 10 cm H2O, 3% (IQR: 2.10) for PEEP = 12 cm H2O and 10% (IQR: 9.11) for PEEP = 15 cm H2O. The results were further validated using a cross-correlation (N = 100,000). Linear regression between the estimated and measured dFRC with a median R2 of 0.948 (IQR: 0.915, 0.968; 90% CI: 0.814, 0.984) over the N = 100,000 cross-validation tests. Conclusions The results suggest that a model based approach to estimating dFRC may be viable in a clinical scenario without any interruption to ventilation and can thus provide an alternative to measuring dFRC by disconnecting the patient from the ventilator or by using advanced ventilators. The overall results provide a means of estimating dFRC at any PEEP levels. Although reasonable clinical accuracy is limited to the linear region of the static PV curve, the model can evaluate the impact of changes in PEEP or other mechanical ventilation settings

Management of Mechanical Ventilation During Extracorporeal Membrane Oxygenation

This chapter explores the best practices of mechanical ventilation during extracorporeal membrane oxygenation (ECMO) through a detailed discussion of the physiologic theory and clinical evidence. Future areas of study and unanswered questions about mechanical ventilation during ECMO are also delineated

Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and 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 viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods

Front Lines of Thoracic Surgery

Front Lines of Thoracic Surgery collects up-to-date contributions on some of the most debated topics in today's clinical practice of cardiac, aortic, and general thoracic surgery,and anesthesia as viewed by authors personally involved in their evolution. The strong and genuine enthusiasm of the authors was clearly perceptible in all their contributions and I'm sure that will further stimulate the reader to understand their messages. Moreover, the strict adhesion of the authors' original observations and findings to the evidence base proves that facts are the best guarantee of scientific value. This is not a standard textbook where the whole discipline is organically presented, but authors' contributions are simply listed in their pertaining subclasses of Thoracic Surgery. I'm sure that this original and very promising editorial format which has and free availability at its core further increases this book's value and it will be of interest to healthcare professionals and scientists dedicated to this field

Theoretical open-loop model of respiratory mechanics in the extremely preterm infant

Non-invasive ventilation is increasingly used for respiratory support in preterm infants, and is associated with a lower risk of chronic lung disease. However, this mode is often not successful in the extremely preterm infant in part due to their markedly increased chest wall compliance that does not provide enough structure against which the forces of inhalation can generate sufficient pressure. To address the continued challenge of studying treatments in this fragile population, we developed a nonlinear lumped-parameter model of respiratory system mechanics of the extremely preterm infant that incorporates nonlinear lung and chest wall compliances and lung volume parameters tuned to this population. In particular we developed a novel empirical representation of progressive volume loss based on compensatory alveolar pressure increase resulting from collapsed alveoli. The model demonstrates increased rate of volume loss related to high chest wall compliance, and simulates laryngeal braking for elevation of end-expiratory lung volume and constant positive airway pressure (CPAP). The model predicts that low chest wall compliance (chest stiffening) in addition to laryngeal braking and CPAP enhance breathing and delay lung volume loss. These results motivate future data collection strategies and investigation into treatments for chest wall stiffening.Comment: 22 pages, 5 figure