Single-FiO₂ lung modelling with machine learning: a computer simulation incorporating volumetric capnography.

ABSTRACT: We investigated whether machine learning (ML) analysis of ICU monitoring data incorporating volumetric capnography measurements of mean alveolar PCO₂ can partition venous admixture (VenAd) into its shunt and low V/Q components without manipulating the inspired oxygen fraction (FiO₂). From a 21-compartment ventilation / perfusion (V/Q) model of pulmonary blood flow we generated blood gas and mean alveolar PCO₂ data in simulated scenarios with shunt values from 7.3% to 36.5% and a range of FiO₂ settings, indirect calorimetry and cardiac output measurements and acid- base and hemoglobin oxygen affinity conditions. A 'deep learning' ML application, trained and validated solely on single FiO₂ bedside monitoring data from 14,736 scenarios, t

SUBMITTER: Morgan TJ 

PROVIDER: S-EPMC10066977 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature