Pulmonary gas exchange evaluated by machine learning: a computer simulation

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Morgan, TJ
Langley, AN
Barrett, RDC
Anstey, CM
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Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q. From these inputs the model produced paired arterial blood gases, first with the inspired O2 fraction (FiO2) adjusted to arterial saturation (SaO2) = 0.90, and second with FiO2 increased by 0.1. ‘Stacked regressor’ ML ensembles were trained/validated on 90% of this dataset. The remainder with shunt, log SD, and mean ‘held back’ formed the test-set. ‘Two-Point’ ML estimates of shunt, log SD and mean utilized data from both FiO2 settings. ‘Single-Point’ estimates used only data from SaO2 = 0.90. From 3454 test gas exchange scenarios, two-point shunt, log SD and mean estimates produced linear regression models versus true values with slopes ~ 1.00, intercepts ~ 0.00 and R2 ~ 1.00. Kernel density and Bland–Altman plots confirmed close agreement. Single-point estimates were less accurate: R2 = 0.77–0.89, slope = 0.991–0.993, intercept = 0.009–0.334. ML applications using blood gas, indirect calorimetry, and cardiac output data can quantify pulmonary gas exchange in terms describing a 20 compartment V/Q model of pulmonary blood flow. High fidelity reports require data from two FiO2 settings.

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Journal of Clinical Monitoring and Computing
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© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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Machine learning
Computational physiology
Clinical sciences
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Morgan, TJ; Langley, AN; Barrett, RDC; Anstey, CM, Pulmonary gas exchange evaluated by machine learning: a computer simulation, Journal of Clinical Monitoring and Computing, 2022, 37 (1), pp. 201-210