Fig 1.
The approach for simulating multi-patient ventilation.
An entire system state is calculated every 2 ms. The Pulse dynamic circuit solver and transporter are leveraged to ensure sound physics-based results with conservation of energy and mass. Mechanistic interactions occur with all other Pulse physiological systems, most notably, the alveolar-capillary partial pressure gradient diffusion gas exchange with the cardiovascular system.
Fig 2.
Select outputs from an example multi-patient ventilation scenario from the initial investigation.
The outcome of this scenario was classified as negative (red) because both tidal volumes (as mL/kg) are outside the desired bounds for lung protective ventilation.
Table 1.
The results of an outcome correlation statistical analysis.
Fig 3.
The selected parameters for investigation are compared with each other to determine their dependence.
The PPMCC method is used to calculate a value between -1 (inversely correlated) and 1 (correlated). Those with low correction (close to 0) are more independent of each other and are therefore the best candidates for informed decision-making.
Fig 4.
Comparison of multi-patient ventilation simulated outcomes due to TV (plot a) and PaO2 (plot b) outcome bounds.
Each graphical dash is a full simulation. Included are univariate histogram plots for each axis using kernel density estimation to represent the distribution of all three outcomes described in two dimensions. The compliance (abscissa) has discrete values due to the chosen patient model parameter setting methodology and fluid mechanics. The OSI (ordinate) is dependent on all external settings, along with the complex interactions of internal mechanistic models. Note that while the OSI has units of mmHg (because it is a ratio of pressure divided by saturation), the interpretation is like the unitless OI value.
Fig 5.
Distribution of all simulated patient’s OSI grouped by the seven DIF settings used from mild to severe.
The OSI increases with DIF and is, therefore, useful for non-invasive clinical assessment of hypoxemic respiratory failure. The OSI increases with diffusion impairment because the SpO2, which plateaus at 100, is a proportionately larger fraction of PaO2 as diffusion impairment and shunt make PaO2 less than expected for a given FiO2.
Fig 6.
The simulations from Fig 4 were holistically taken into account to get a complete decision matrix.
Outcomes were assigned a normalized value of green = 1, yellow = 0.5, and red = 0 to encode a z-axis as colors or color gradient. The resulting three-dimensional scatter plot (plot a) was used to produce an interpolated surface using the first-order bivariate B-spline method (plot b).