One-compartment lumped-parameter models of respiratory mechanics, representing the airflow resistance of the tracheobronchial tree with a linear or nonlinear resistor, are not able to describe the mechanical property of airways in different generations. Therefore, based on the anatomic structure of tracheobronchial tree and the mechanical property of airways in each generation, this study classified the human airways into three segments: the upper airway segment, the collapsible airway segment, and the small airway segment. Finally, a nonlinear, multi-compartment lumped-parameter model of respiratory mechanics with three airway segments was established. With the respiratory muscle effort as driving pressure, the model was used to simulate the tidal breathing of healthy adults. The results were consistent with the physiological data and the previously published results, suggesting that this model could be used for pathophysiological research of respiratory system.
ObjectiveTo evaluate the accuracy of invasive ventilator monitoring for airway resistance (Raw) and respiratory compliance (Crs), and identify factors influencing measurement precision in pressure control ventilation (PCV) mode. MethodsUtilizing an ASL5000 lung simulator, we configured airway resistance settings (3 cmH2O·L–1·s–1 and 15 cmH2O·L–1·s–1) and compliance values (15 mL/cmH2O and 50 mL/cmH2O). ASL5000 was connected to SV800 ventilator via endotracheal tubes (internal diameters 6.5 mm, 7.0 mm, 7.5 mm, and 8.0 mm) in PCV mode, inspiratory pressures was set 5 cmH2O, 10cmH2O, 15cmH2O, 20 cmH2O, yielding 352 datasets. Absolute differences (ΔRaw, ΔCrs) and percentage differences (ΔRaw%, ΔCrs%) between ventilator-monitored and simulator-setted were calculated. Correlation and multiple linear regression analyses were employed to evaluate the main and interaction effects of tube diameter, simulated airway resistance/respiratory compliance, and inspiratory pressure on ΔRaw% and ΔCrs%.ResultsIn pressure control mode, median ΔRaw was 2.0 (0.0, 4.0) cmH2O·L–1·s–1 ; median ΔCrs was 6.5 (5.0, 11.0) mL/cmH2O. Tube diameter (β=–44.32, 95%CI –50.90 to –37.74, P<0.001) and simulated resistance (β=–12.24, 95%CI –12.83 to –11.66, P<0.001) were significant negative predictors of ΔRaw%, while inspiratory pressure (β=4.88, 95%CI 4.25 to 5.50, P<0.001) and simulated compliance (β=1.10, 95%CI 0.90 to 1.30, P<0.001) were significant positive predictors. The negative association between simulated resistance and ΔRaw% was attenuated by increased simulated compliance (β=–0.10, 95%CI –0.13 to –0.07, P<0.001); the negative effect of tube diameter on ΔRaw% was less pronounced at higher inspiratory pressures (β=–1.89, 95%CI –2.99 to –0.79, P=0.001). Inspiratory pressure had no significant effect on ΔCrs% (P=0.909), Tube diameter (β=–4.30, 95%CI –5.19 to –3.40, P<0.001), simulated compliance (β=–0.18, 95%CI –0.21 to –0.16, P<0.001) were significant negative predictors of ΔCrs%; simulated resistance (β=–0.11, 95%CI –0.19 to –0.03, P=0.009) negatively affected ΔCrs% initially, but its interaction with simulated compliance was not significant (P=0.438). The interaction between tube diameter and inspiratory pressure significantly reduced ΔCrs% (β=–0.21, 95%CI –0.37 to –0.05, P=0.011). ConclusionsIn PCV mode, the SV800 ventilator achieved manufacturer-specified monitoring accuracy. ΔRaw% correlated negatively with tube diameter and simulated resistance, interaction effects between simulated resistance and compliance, interaction effects between tube diameter-inspiratory pressure, and positively with simulated compliance and inspiratory pressure. Inspiratory pressure demonstrated no impact on compliance monitoring, whereas tube diameter, simulated compliance, tube diameter interaction with inspiratory pressure reduced ΔCrs%.