A minimal model of lung mechanics and model-based markers for optimizing ventilator treatment in ARDS patients

Abstract

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.

Traversing the Fuzzy Valley: Problems Caused by Reliance on Default Simulation and Parameter Identification Programs for Discontinuous Models

“…While numerical integration coupled with gradient based identification methods are frequently used successfully (Sundaresan et al 2009;Schranz et al 2011), it is important to know the limitations of such methods. In particular, this investigation has shown that numerical integration methods which handle discontinuities poorly, such as the proprietary MATLABTM methods, are likely to cause parameter identification failure when poorly applied.…”

“…This information can be used to guide therapy or aid diagnosis (Sundaresan et al 2009;Lozano et al 2008;Chase et al 2011). However, fitting a model simulation to measured data often requires complex mathematical algorithms, and success is not guaranteed.…”

Absolute Electrical Impedance Tomography (aEIT) Guided Ventilation Therapy in Critical Care Patients: Simulations and Future Trends

“…In the model, the lung unit can assume only two possible states: recruited (or open) and derecruited (or closed) [17], [20]. These two states are governed only by the threshold opening pressure (TOP), which is the critical pressure above which the lung unit pops open, and the threshold closing pressure (TCP) below which the unit collapses.…”

“…These two states are governed only by the threshold opening pressure (TOP), which is the critical pressure above which the lung unit pops open, and the threshold closing pressure (TCP) below which the unit collapses. The model uses normally distributed TOP and TCP pressures with a specific mean and standard deviation (SD), which may be adjusted to reflect the heterogeneous characteristic of alveoli under different abnormal lung conditions such as ARDS [20], [21]. Fig.…”

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

“…The linear portion of the static PV curve is where most of the data points occur on any measured dynamic PV loop. Based on the work of Sundaresan et al [23], over 90% of the data points from measured PV loops occur in the static portion of the static PV curve and are the points primarily used in this analysis. The non-linear portions at PEEP and PIP are actually high flow regions and are thus do not account for or include the majority of the data.…”