Modelling mixing within the dead space of the lung improves predictions of functional residual capacity

Simulation-based optimisation to quantify heterogeneity of specific ventilation and perfusion in the lung by the Inspired Sinewave Test

The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive 'gas-distribution' type of respiratory test capable of measuring the cardiopulmonary parameters. We developed a simulation-based optimisation for the IST, with a simulation of a realistic heterogeneous lung, namely a lognormal distribution of spatial ventilation and perfusion. We tested this method in datasets from 13 anaesthetised pigs (pre and post-lung injury) and 104 human subjects (32 healthy and 72 COPD subjects). The 72 COPD subjects were classified into four COPD phenotypes based on 'GOLD' classification. This method allowed IST to identify and quantify heterogeneity of both ventilation and perfusion, permitting diagnostic distinction between health and disease states. In healthy volunteers, we show a linear relationship between the ventilatory heterogeneity versus age ([Formula: see text]). In a mechanically ventilated pig, IST ventilatory heterogeneity in noninjured and injured lungs was significantly different (p < 0.0001). Additionally, measured indices could accurately identify patients with COPD (area under the receiver operating characteristic curve is 0.76, p < 0.0001). The IST also could distinguish different phenotypes of COPD with 73% agreement with spirometry.

A tidal lung simulation to quantify lung heterogeneity with the Inspired Sinewave Test

We have created a lung simulation to quantify lung heterogeneity from the results of the inspired sinewave test (IST). The IST is a lung function test that is non-invasive, non-ionising and does not require patients' cooperation. A tidal lung simulation is developed to assess this test and also a method is proposed to calculate lung heterogeneity from IST results. A sensitivity analysis based on the Morris method and linear regression were applied to verify and to validate the simulation. Additionally, simulated emphysema and pulmonary embolism conditions were created using the simulation to assess the ability of the IST to identify these conditions. Experimental data from five pigs (pre-injured vs injured) were used for validation. This paper contributes to the development of the IST. Firstly, our sensitivity analysis reveals that the IST is highly accurate with an underestimation of about 5% of the simulated values. Sensitivity analysis suggested that both instability in tidal volume and extreme expiratory flow coefficients during the test cause random errors in the IST results. Secondly, the ratios of IST results obtained at two tracer gas oscillation frequencies can identify lung heterogeneity (ELV₆₀/ELV₁₈₀ and Qp₆₀/Qp₁₈₀). There was dissimilarity between simulated emphysema and pulmonary embolism (p < 0.0001). In the animal model, the control group had ELV₆₀/ELV₁₈₀ = 0.58 compared with 0.39 in injured animals (p < 0.0001).

Lung heterogeneity and deadspace volume in acute respiratory distress syndrome animals using the inspired sinewave test

The acute respiratory distress syndrome is associated with a high rate of morbidity and mortality, as patients undergoing mechanical ventilation are at risk of ventilator-induced lung injuries.

OBJECTIVE

To measure the lung heterogeneity and deadspace volume to find safer ventilator strategies. Then, the ventilator settings could offer homogeneous ventilation and theoretically equalise and reduce tidal strain/stress in the lung parenchyma.

APPROACH

The Inspired Sinewave Test (IST) is a non-invasive lung measurement tool, which does not require patients' cooperation. The IST can measure the effective lung volume, pulmonary blood flow and deadspace volume. We developed a computational simulation of the cardiopulmonary system to allow lung heterogeneity to be quantified using data solely derived from the IST. Then, the method to quantify lung heterogeneity using two IST tracer gas frequencies (180s and 60s) was introduced and experimented in the simulation lungs and in animal models. Thirteen anaesthetised pigs were studied with the IST, both before and after experimental lung injury (saline-lavage ARDS model). The deadspace volume is compared between IST and the SF_{6} Washout method.

RESULTS

The IST could measure the lung heterogeneity using two frequencies tracer gases. Furthermore, the value of IST ventilation heterogeneity in ARDS lungs were higher than in control lungs at PEEP 10cmH_{2}O (AuC=0.85, p<0.001). Deadspace volume values measured by the IST has a strong relationship with the measured values of the SF_{6} (9mL bias and limits of agreement from -79mL to 57mL in control animals).

SIGNIFICANCE

the potential impact of the IST technique in the identification of ventilation and perfusion heterogeneity during ventilator support.

A virtual patient model for mechanical ventilation

BACKGROUND AND OBJECTIVES

Mechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies.

METHODS

This study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms.

RESULTS

Results show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH₂O.

CONCLUSIONS

The results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV.