Background There is on-going controversy regarding the potential for increased respiratory effort to generate patient self-inflicted lung injury (P-SILI) in spontaneously breathing patients with COVID-19 acute hypoxaemic respiratory failure. However, direct clinical evidence linking increased inspiratory effort to lung injury is scarce. We adapted a computational simulator of cardiopulmonary pathophysiology to quantify the mechanical forces that could lead to P-SILI at different levels of respiratory effort. In accordance with recent data, the simulator parameters were manually adjusted to generate a population of 10 patients that recapitulate clinical features exhibited by certain COVID-19 patients, i.e., severe hypoxaemia combined with relatively well-preserved lung mechanics, being treated with supplemental oxygen. Results Simulations were conducted at tidal volumes (VT) and respiratory rates (RR) of 7 ml/kg and 14 breaths/min (representing normal respiratory effort) and at VT/RR of 7/20, 7/30, 10/14, 10/20 and 10/30 ml/kg / breaths/min. While oxygenation improved with higher respiratory efforts, significant increases in multiple indicators of the potential for lung injury were observed at all higher VT/RR combinations tested. Pleural pressure swing increased from 12.0 ± 0.3 cmH2O at baseline to 33.8 ± 0.4 cmH2O at VT/RR of 7 ml/kg/30 breaths/min and to 46.2 ± 0.5 cmH2O at 10 ml/kg/30 breaths/min. Transpulmonary pressure swing increased from 4.7 ± 0.1 cmH2O at baseline to 17.9 ± 0.3 cmH2O at VT/RR of 7 ml/kg/30 breaths/min and to 24.2 ± 0.3 cmH2O at 10 ml/kg/30 breaths/min. Total lung strain increased from 0.29 ± 0.006 at baseline to 0.65 ± 0.016 at 10 ml/kg/30 breaths/min. Mechanical power increased from 1.6 ± 0.1 J/min at baseline to 12.9 ± 0.2 J/min at VT/RR of 7 ml/kg/30 breaths/min, and to 24.9 ± 0.3 J/min at 10 ml/kg/30 breaths/min. Driving pressure increased from 7.7 ± 0.2 cmH2O at baseline to 19.6 ± 0.2 cmH2O at VT/RR of 7 ml/kg/30 breaths/min, and to 26.9 ± 0.3 cmH2O at 10 ml/kg/30 breaths/min. Conclusions Our results suggest that the forces generated by increased inspiratory effort commonly seen in COVID-19 acute hypoxaemic respiratory failure are comparable with those that have been associated with ventilator-induced lung injury during mechanical ventilation. Respiratory efforts in these patients should be carefully monitored and controlled to minimise the risk of lung injury.

Physiological simulators which are intended for use in clinical environments face harsh expectations from medical practitioners; they must cope with significant levels of uncertainty arising from non-measurable parameters, population heterogeneity and disease heterogeneity, and their validation must provide watertight proof of their applicability and reliability in the clinical arena. This paper describes a systems engineering framework for the validation of an in silico simulation model of pulmonary physiology. We combine explicit modelling of uncertainty/variability with advanced global optimization methods to demonstrate that the model predictions never deviate from physiologically plausible values for realistic levels of parametric uncertainty. The simulation model considered here has been designed to represent a dynamic in vivo cardiopulmonary state iterating through a mass-conserving set of equations based on established physiological principles and has been developed for a direct clinical application in an intensive-care environment. The approach to uncertainty modelling is adapted from the current best practice in the field of systems and control engineering, and a range of advanced optimization methods are employed to check the robustness of the model, including sequential quadratic programming, mesh-adaptive direct search and genetic algorithms. An overview of these methods and a comparison of their reliability and computational efficiency in comparison to statistical approaches such as Monte Carlo simulation are provided. The results of our study indicate that the simulator provides robust predictions of arterial gas pressures for all realistic ranges of model parameters, and also demonstrate the general applicability of the proposed approach to model validation for physiological simulation.

BackgroundClinical trials have, so far, failed to establish clear beneficial outcomes of recruitment maneuvers (RMs) on patient mortality in acute respiratory distress syndrome (ARDS), and the effects of RMs on the cardiovascular system remain poorly understood.MethodsA computational model with highly integrated pulmonary and cardiovascular systems was configured to replicate static and dynamic cardio-pulmonary data from clinical trials. Recruitment maneuvers (RMs) were executed in 23 individual in-silico patients with varying levels of ARDS severity and initial cardiac output. Multiple clinical variables were recorded and analyzed, including arterial oxygenation, cardiac output, peripheral oxygen delivery and alveolar strain.ResultsThe maximal recruitment strategy (MRS) maneuver, which implements gradual increments of positive end expiratory pressure (PEEP) followed by PEEP titration, produced improvements in PF ratio, carbon dioxide elimination and dynamic strain in all 23 in-silico patients considered. Reduced cardiac output in the moderate and mild in silico ARDS patients produced significant drops in oxygen delivery during the RM (average decrease of 423 ml min−1 and 526 ml min−1, respectively). In the in-silico patients with severe ARDS, however, significantly improved gas-exchange led to an average increase of 89 ml min−1 in oxygen delivery during the RM, despite a simultaneous fall in cardiac output of more than 3 l min−1 on average. Post RM increases in oxygen delivery were observed only for the in silico patients with severe ARDS. In patients with high baseline cardiac outputs (>6.5 l min−1), oxygen delivery never fell below 700 ml min−1.ConclusionsOur results support the hypothesis that patients with severe ARDS and significant numbers of alveolar units available for recruitment may benefit more from RMs. Our results also indicate that a higher than normal initial cardiac output may provide protection against the potentially negative effects of high intrathoracic pressures associated with RMs on cardiac function. Results from in silico patients with mild or moderate ARDS suggest that the detrimental effects of RMs on cardiac output can potentially outweigh the positive effects of alveolar recruitment on oxygenation, resulting in overall reductions in tissue oxygen delivery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12890-017-0369-7) contains supplementary material, which is available to authorized users.

BackgroundRecent analyses of patient data in acute respiratory distress syndrome (ARDS) showed that a lower ventilator driving pressure was associated with reduced relative risk of mortality. These findings await full validation in prospective clinical trials.MethodsTo investigate the association between driving pressures and ventilator induced lung injury (VILI), we calibrated a high fidelity computational simulator of cardiopulmonary pathophysiology against a clinical dataset, capturing the responses to changes in mechanical ventilation of 25 adult ARDS patients. Each of these in silico patients was subjected to the same range of values of driving pressure and positive end expiratory pressure (PEEP) used in the previous analyses of clinical trial data. The resulting effects on several physiological variables and proposed indices of VILI were computed and compared with data relating ventilator settings with relative risk of death.ResultsThree VILI indices: dynamic strain, mechanical power and tidal recruitment, showed a strong correlation with the reported relative risk of death across all ranges of driving pressures and PEEP. Other variables, such as alveolar pressure, oxygen delivery and lung compliance, correlated poorly with the data on relative risk of death.ConclusionsOur results suggest a credible mechanistic explanation for the proposed association between driving pressure and relative risk of death. While dynamic strain and tidal recruitment are difficult to measure routinely in patients, the easily computed VILI indicator known as mechanical power also showed a strong correlation with mortality risk, highlighting its potential usefulness in designing more protective ventilation strategies for this patient group.Electronic supplementary materialThe online version of this article (10.1186/s12931-019-0990-5) contains supplementary material, which is available to authorized users.

Objectives: Mechanical power (MP) and driving pressure (∆P) have been proposed as indicators, and possibly drivers, of ventilator-induced lung injury. We tested the utility of these different measures as targets to derive maximally protective ventilator settings.Design: A high-fidelity computational simulator was matched to individual patient data and used to identify strategies that minimize ∆P, MP and a modified version of MP (MMP) that removes the direct linear, positive dependence between MP and PEEP.

Background. Positive end-expiratory pressure (PEEP) is widely used to improve oxygenation and prevent alveolar collapse in mechanically ventilated patients with the acute respiratory distress syndrome (ARDS). Although PEEP improves arterial oxygenation predictably, high-PEEP strategies have demonstrated equivocal improvements in ARDS-related mortality. The effect of PEEP on tissue oxygen delivery is poorly understood and is difficult to quantify or investigate in the clinical environment.Methods. We investigated the effects of PEEP on tissue oxygen delivery in ARDS using a new, high-fidelity, computational model with highly integrated respiratory and cardiovascular systems. The model was configured to replicate published clinical trial data on the responses of 12 individual ARDS patients to changes in PEEP. These virtual patients were subjected to increasing PEEP levels during a lung-protective ventilation strategy (0–20 cm H2O). Measured variables included arterial oxygenation, cardiac output, peripheral oxygen delivery, and alveolar strain.Results. As PEEP increased, tissue oxygen delivery decreased in all subjects (mean reduction of 25% at 20 cm H2O PEEP), despite an increase in arterial oxygen tension (mean increase 6.7 kPa at 20 cm H2O PEEP). Changes in arterial oxygenation and tissue oxygen delivery differed between subjects but showed a consistent pattern. Static and dynamic alveolar strain decreased in all patients as PEEP increased.Conclusions. Incremental PEEP in ARDS appears to protect alveoli and improve arterial oxygenation, but also appears to impair tissue oxygen delivery significantly because of reduced cardiac output. We propose that this trade-off may explain the poor improvements in mortality associated with high-PEEP ventilation strategies.

The selection of mechanical ventilator settings that ensure adequate oxygenation and carbon dioxide clearance while minimizing the risk of ventilator-associated lung injury (VALI) is a significant challenge for intensive-care clinicians. Current guidelines are largely based on previous experience combined with recommendations from a limited number of in vivo studies whose data are typically more applicable to populations than to individuals suffering from particular diseases of the lung. By combining validated computational models of pulmonary pathophysiology with global optimization algorithms, we generate in silico experiments to examine current practice and uncover optimal combinations of ventilator settings for individual patient and disease states. Formulating the problem as a multiobjective, multivariable constrained optimization problem, we compute settings of tidal volume, ventilation rate, inspiratory/expiratory ratio, positive end-expiratory pressure and inspired fraction of oxygen that optimally manage the tradeoffs between ensuring adequate oxygenation and carbon dioxide clearance and minimizing the risk of VALI for different pulmonary disease scenarios.

Background: Apnoeic oxygenation can come close to matching the oxygen demands of the apnoeic patient but does not facilitate carbon dioxide (CO 2) elimination, potentially resulting in dangerous hypercapnia. Numerous studies have shown that high-flow nasal oxygen administration prevents hypoxaemia, and appears to reduce the rate of increase of arterial CO 2 partial pressure (Pa CO2), but evidence is lacking to explain these effects. Methods: We extended a high-fidelity computational simulation of cardiopulmonary physiology to include modules allowing variable effects of: (a) cardiogenic oscillations affecting intrathoracic gas spaces, (b) gas mixing within the anatomical dead space, (c) insufflation into the trachea or above the glottis, and (d) pharyngeal pressure oscillation. We validated this model by reproducing the methods and results of five clinical studies on apnoeic oxygenation. Results: Simulated outputs best matched clinical data for model selection of parameters reflecting: (a) significant effects of cardiogenic oscillations on alveoli, both in terms of strength of the effect (4.5 cm H 2 O) and percentage of alveoli affected (60%), (b) augmented gas mixing within the anatomical dead space, and (c) pharyngeal pressure oscillations between 0 and 2 cm H 2 O at 70 Hz. Conclusions: Cardiogenic oscillations, dead space gas mixing, and micro-ventilation induced by pharyngeal pressure variations appear to be important mechanisms that combine to facilitate the clearance of CO 2 during apnoea. Evolution of high-flow oxygen insufflation devices should take advantage of these insights, potentially improving apnoeic gas exchange.