BackgroundAcute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.MethodsA study of 10 patients diagnosed with ALI/ARDS whom underwent recruitment manoeuvre is carried out. Airway pressure and flow data are used to identify patient-specific constant lung elastance (Elung) and time-variant dynamic lung elastance (Edrs) at each PEEP level (increments of 5cmH2O), for a single compartment linear lung model using integral-based methods. Optimal PEEP is estimated using Elung versus PEEP, Edrs-Pressure curve and Edrs Area at minimum elastance (maximum compliance) and the inflection of the curves (diminishing return). Results are compared to clinically selected PEEP values. The trials and use of the data were approved by the New Zealand South Island Regional Ethics Committee.ResultsMedian absolute percentage fitting error to the data when estimating time-variant Edrs is 0.9% (IQR = 0.5-2.4) and 5.6% [IQR: 1.8-11.3] when estimating constant Elung. Both Elung and Edrs decrease with PEEP to a minimum, before rising, and indicating potential over-inflation. Median Edrs over all patients across all PEEP values was 32.2 cmH2O/l [IQR: 26.1-46.6], reflecting the heterogeneity of ALI/ARDS patients, and their response to PEEP, that complicates standard approaches to PEEP selection. All Edrs-Pressure curves have a clear inflection point before minimum Edrs, making PEEP selection straightforward. Model-based selected PEEP using the proposed metrics were higher than clinically selected values in 7/10 cases.ConclusionContinuous monitoring of the patient-specific Elung and Edrs and minimally invasive PEEP titration provide a unique, patient-specific and physiologically relevant metric to optimize PEEP selection with minimal disruption of MV therapy.

A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rate as high as 70%. Despite many recent studies on ventilator treatment of the disease, there are no well established methods to determine the optimal Positive End Expiratory Pressure (PEEP) or other critical ventilator settings for individual patients. A model of fundamental lung mechanics is developed based on capturing the recruitment status of lung units. The main objective of this research is the simplest possible model that is clinically effective in determining PEEP. The model was identified for a variety of different ventilator settings using clinical data. The fitting error was between 0.1% to 4% over the inflation limb and between 0.3% to 13% over the deflation limb at different PEEP settings. The model produces good correlation with clinical data, and is clinically applicable due to the minimal number of patient specific parameters to identify. The ability to use this identified patient specific model to optimize ventilator management is demonstrated by its ability to predict the patient specific response of PEEP changes before clinically applying them. Predictions of recruited lung volume change with change in PEEP have a median absolute error of 1.87% (IQR:0.93-4.80%; 90% CI:0.16-11.98%) for inflation and a median of 5.76% (IQR:2.71-10.50%; 90% CI:0.43-17.04%) for deflation, across all data sets and PEEP values (N = 34 predictions). This minimal model thus provides a clinically useful and simple platform for continuous patient specific monitoring of lung unit recruitment for a patient.

BackgroundThe 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.MethodsTen patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH2O 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.ResultsMedian 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.ConclusionsFor 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.

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Background

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.This paper examines combining models of diffusion resistance and lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The outputs are estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model.The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also examines how the rate of CO drop with increased PEEP can be used as a method to determine optimal PEEP, which may be used to optimise MV therapy with respect to the gas exchange achieved, as well as accounting for the impact on the cardiovascular system and its management.

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Abstract-Currently, there does not exist reliable MV treatment or protocols in critical care to treat acute respiratory diseases, and thus no proven way to optimise care to minimise the mortality, length of stay or cost. The overall approach of this research is to improve protocols by using appropriate computer models that take into account the essential lung mechanics. The aim of this research is to create an automated algorithm for tracking the boundary of individual or groups of alveoli, and to convert this into a pressure volume curve for three different types of alveoli. A technique called in vivo microscopy has been developed by Schiller et al which visualizes the inflation and deflation of individual alveoli in a surfactant deactivation model of lung injury in pigs. Three different types of alveoli were tracked using data from Schiller et al, type I, II and III. These types correspond to healthy alveoli, non-collapsing but partially diseased alveoli, and fully collapsing diseased alveoli respectively. The boundaries of all the alveoli that were tracked, compared well visually to the movies. Pressure versus Area curves were derived for both inflation and deflation, they captured the expected physiological behaviour, and were qualitatively similar to the quasi-static pressure area curves derived in Schiller et al, Quantitative differences are due to the dynamic effects of ventilation which were not investigated in Schiller et al.