Utility of Driving Pressure and Mechanical Power to Guide Protective Ventilator Settings in Two Cohorts of Adult and Pediatric Patients With Acute Respiratory Distress Syndrome: A Computational Investigation

Digital Twins of Acute Hypoxemic Respiratory Failure Patients Suggest a Mechanistic Basis for Success and Failure of Noninvasive Ventilation

OBJECTIVES

To clarify the mechanistic basis for the success or failure of noninvasive ventilation (NIV) in acute hypoxemic respiratory failure (AHRF).

DESIGN

We created digital twins based on mechanistic computational models of individual patients with AHRF.

SETTING

Interdisciplinary Collaboration in Systems Medicine Research Network.

SUBJECTS

We used individual patient data from 30 moderate-to-severe AHRF patients who had failed high-flow nasal cannula (HFNC) therapy and subsequently underwent a trial of NIV.

INTERVENTIONS

Using the digital twins, we evaluated lung mechanics, quantified the separate contributions of external support and patient respiratory effort to lung injury indices, and investigated their relative impact on NIV success or failure.

MEASUREMENTS AND MAIN RESULTS

In digital twins of patients who successfully completed/failed NIV, after 2 hours of the trial the mean (sd) of the change in total lung stress was -10.9 (6.2)/-0.35 (3.38) cm H2O, mechanical power -13.4 (12.2)/-1.0 (5.4) J/min, and total lung strain 0.02 (0.24)/0.16 (0.30). In the digital twins, positive end-expiratory pressure (PEEP) produced by HFNC was similar to that set during NIV. In digital twins of patients who failed NIV vs. those who succeeded, intrinsic PEEP was 3.5 (0.6) vs. 2.3 (0.8) cm H2O, inspiratory pressure support was 8.3 (5.9) vs. 22.3 (7.2) cm H2O, and tidal volume was 10.9 (1.2) vs. 9.4 (1.8) mL/kg. In digital twins, successful NIV increased respiratory system compliance +25.0 (16.4) mL/cm H2O, lowered inspiratory muscle pressure -9.7 (9.6) cm H2O, and reduced the contribution of patient spontaneous breathing to total driving pressure by 57.0%.

CONCLUSIONS

In digital twins of AHRF patients, successful NIV improved lung mechanics, lowering respiratory effort and indices associated with lung injury. NIV failed in patients for whom only low levels of positive inspiratory pressure support could be applied without risking patient self-inflicted lung injury due to excessive tidal volumes.

Different ventilation intensities among various categories of patients ventilated for reasons other than ARDS--A pooled analysis of 4 observational studies

PURPOSE

We investigated driving pressure (ΔP) and mechanical power (MP) and associations with clinical outcomes in critically ill patients ventilated for reasons other than ARDS.

MATERIALS AND METHODS

Individual patient data analysis of a pooled database that included patients from four observational studies of ventilation. ΔP and MP were compared among invasively ventilated non-ARDS patients with sepsis, with pneumonia, and not having sepsis or pneumonia. The primary endpoint was ΔP; secondary endpoints included MP, ICU mortality and length of stay, and duration of ventilation.

RESULTS

This analysis included 372 (11%) sepsis patients, 944 (28%) pneumonia patients, and 2040 (61%) patients ventilated for any other reason. On day 1, median ΔP was higher in sepsis (14 [11-18] cmH2O) and pneumonia patients (14 [11-18]cmH2O), as compared to patients not having sepsis or pneumonia (13 [10-16] cmH2O) (P < 0.001). Median MP was also higher in sepsis and pneumonia patients. ΔP, as opposed to MP, was associated with ICU mortality in sepsis and pneumonia patients.

CONCLUSIONS

The intensity of ventilation differed between patients with sepsis or pneumonia and patients receiving ventilation for any other reason; ΔP was associated with higher mortality in sepsis and pneumonia patients.

REGISTRATION

This post hoc analysis was not registered; the individual studies that were merged into the used database were registered at clinicaltrials.gov: NCT01268410 (ERICC), NCT02010073 (LUNG SAFE), NCT01868321 (PRoVENT), and NCT03188770 (PRoVENT-iMiC).

High Mechanical Power and Driving Pressures are Associated With Postoperative Respiratory Failure Independent From Patients' Respiratory System Mechanics

OBJECTIVES

High mechanical power and driving pressure (ΔP) have been associated with postoperative respiratory failure (PRF) and may be important parameters guiding mechanical ventilation. However, it remains unclear whether high mechanical power and ΔP merely reflect patients with poor respiratory system mechanics at risk of PRF. We investigated the effect of mechanical power and ΔP on PRF in cohorts after exact matching by patients' baseline respiratory system compliance.

DESIGN

Hospital registry study.

SETTING

Academic hospital in New England.

PATIENTS

Adult patients undergoing general anesthesia between 2008 and 2020.

INTERVENTION

None.

MEASUREMENTS AND MAIN RESULTS

The primary exposure was high (≥ 6.7 J/min, cohort median) versus low mechanical power and the key-secondary exposure was high (≥ 15.0 cm H 2 O) versus low ΔP. The primary endpoint was PRF (reintubation or unplanned noninvasive ventilation within seven days). Among 97,555 included patients, 4,030 (4.1%) developed PRF. In adjusted analyses, high intraoperative mechanical power and ΔP were associated with higher odds of PRF (adjusted odds ratio [aOR] 1.37 [95% CI, 1.25-1.50]; p < 0.001 and aOR 1.45 [95% CI, 1.31-1.60]; p < 0.001, respectively). There was large variability in applied ventilatory parameters, dependent on the anesthesia provider. This facilitated matching of 63,612 (mechanical power cohort) and 53,260 (ΔP cohort) patients, yielding identical baseline standardized respiratory system compliance (standardized difference [SDiff] = 0.00) with distinctly different mechanical power (9.4 [2.4] vs 4.9 [1.3] J/min; SDiff = -2.33) and ΔP (19.3 [4.1] vs 11.9 [2.1] cm H 2 O; SDiff = -2.27). After matching, high mechanical power and ΔP remained associated with higher risk of PRF (aOR 1.30 [95% CI, 1.17-1.45]; p < 0.001 and aOR 1.28 [95% CI, 1.12-1.46]; p < 0.001, respectively).

CONCLUSIONS

High mechanical power and ΔP are associated with PRF independent of patient's baseline respiratory system compliance. Our findings support utilization of these parameters for titrating mechanical ventilation in the operating room and ICU.

An in-silico porcine model of phosgene-induced lung injury predicts clinically relevant benefits from application of continuous positive airway pressure up to 8 h post exposure

We present the first computational model of the pathophysiological consequences of phosgene-induced lung injury in porcine subjects. Data from experiments previously performed in several cohorts of large healthy juvenile female pigs (111 data points from 37 subjects), including individual arterial blood gas readings, respiratory rate and heart rate, were used to develop the computational model. Close matches are observed between model outputs (PaO2 and PaCO2) and the experimental data, for both terminally anaesthetised and conscious subjects. The model was applied to investigate the effectiveness of continuous positive airway pressure (CPAP) as a pre-hospital treatment method when treatment is initiated at different time points post exposure. The model predicts that clinically relevant benefits are obtained when 10 cmH2O CPAP is initiated within approximately 8 h after exposure. Supplying low-flow oxygen (40%) rather than medical air produced larger clinical benefits than applying higher CPAP pressure levels. This new model can be used as a tool for conducting investigations into ventilation strategies and pharmaceutical treatments for chemical lung injury of diverse aetiology, and for helping to refine and reduce the use of animals in future experimental studies.

Influence of the Driving Pressure on Mortality in ARDS Patients with or without Abdominal Obesity: A Retrospective Cohort Study

Objective. This study sets out to explore if the relationship between the driving pressure and hospital mortality in ARDS patients is influenced by body mass index (BMI) level or the presence of abdominal obesity. Methods. Data were extracted from an online database named “Multiparameter Intelligent Monitoring in Intensive Care III.” A total of 1556 patients were included and divided into four subgroups based on both BMI level (BMI ≥30 kg/m2 or BMI ＜30 kg/m2) and abdominal assessment. Driving pressure [i.e., the difference between plateau pressure and positive end-expiratory pressure (PEEP)] within 24 h of invasive mechanical ventilation was compared between survivors and nonsurvivors during hospitalization in each group. A logistic regression model was used for hospital mortality. Results. There were 1556 patients with mild-to-severe ARDS, 666 (42.80%) nonobese patients with nonabdominal obesity, 259 (16.65%) nonobese patients with abdominal obesity, 97 (6.23%) obese patients with nonabdominal obesity, and 534 (34.32%) obese patients with abdominal obesity. Driving pressure in nonobese patients with nonabdominal obesity was significantly lower in survivors (12.77 ± 4.53 cm H2O) than in nonsurvivors (14.26 ± 5.52 cm H2O, $p<0.01$ ). On the contrary, in the other three groups, driving pressure was not significantly different between survivors and nonsurvivors. After a logistic multivariable regression analysis, in nonobese (BMI<30 kg/m2) patients with nonabdominal obesity, the driving pressure was independently associated with increased hospital mortality (OR: 1.04, 95% CI 1.00–1.09, $p<0.05$ ) but not in the other three subgroups. Conclusion. Driving pressure is associated with increase in hospital mortality only in nonobese (BMI <30 kg/m2) patients with nonabdominal obesity.

A computational cardiopulmonary physiology simulator accurately predicts individual patient responses to changes in mechanical ventilator settings

We present new results validating the capability of a high-fidelity computational simulator to accurately predict the responses of individual patients with acute respiratory distress syndrome to changes in mechanical ventilator settings. 26 pairs of data-points comprising arterial blood gasses collected before and after changes in inspiratory pressure, PEEP, FiO₂, and I:E ratio from six mechanically ventilated patients were used for this study. Parallelized global optimization algorithms running on a high-performance computing cluster were used to match the simulator to each initial data point. Mean absolute percentage errors between the simulator predicted values of PaO₂ and PaCO₂ and the patient data after changing ventilator parameters were 10.3% and 12.6%, respectively. Decreasing the complexity of the simulator by reducing the number of independent alveolar compartments reduced the accuracy of its predictions. Clinical Relevance- These results provide further evidence that our computational simulator can accurately reproduce patient responses to mechanical ventilation, highlighting its usefulness as a clinical research tool.

Validation of at-the-bedside formulae for estimating ventilator driving pressure during airway pressure release ventilation using computer simulation

BACKGROUND

Airway pressure release ventilation (APRV) is widely available on mechanical ventilators and has been proposed as an early intervention to prevent lung injury or as a rescue therapy in the management of refractory hypoxemia. Driving pressure ([Formula: see text]) has been identified in numerous studies as a key indicator of ventilator-induced-lung-injury that needs to be carefully controlled. [Formula: see text] delivered by the ventilator in APRV is not directly measurable in dynamic conditions, and there is no "gold standard" method for its estimation.

METHODS

We used a computational simulator matched to data from 90 patients with acute respiratory distress syndrome (ARDS) to evaluate the accuracy of three "at-the-bedside" methods for estimating ventilator [Formula: see text] during APRV.

RESULTS

Levels of [Formula: see text] delivered by the ventilator in APRV were generally within safe limits, but in some cases exceeded levels specified by protective ventilation strategies. A formula based on estimating the intrinsic positive end expiratory pressure present at the end of the APRV release provided the most accurate estimates of [Formula: see text]. A second formula based on assuming that expiratory flow, volume and pressure decay mono-exponentially, and a third method that requires temporarily switching to volume-controlled ventilation, also provided accurate estimates of true [Formula: see text].

CONCLUSIONS

Levels of [Formula: see text] delivered by the ventilator during APRV can potentially exceed levels specified by standard protective ventilation strategies, highlighting the need for careful monitoring. Our results show that [Formula: see text] delivered by the ventilator during APRV can be accurately estimated at the bedside using simple formulae that are based on readily available measurements.

Influence of mechanical power and its components on mechanical ventilation in SARS-CoV-2

OBJECTIVE

To analyze the influence of mechanical power and its components on mechanical ventilation for patients infected with SARS-CoV-2; identify the values of the mechanical ventilation components and verify their correlations with each other and with the mechanical power and effects on the result of the Gattinoni-S and Giosa formulas.

METHODS

This was an observational, longitudinal, analytical and quantitative study of respirator and mechanical power parameters in patients with SARS-CoV-2.

RESULTS

The mean mechanical power was 26.9J/minute (Gattinoni-S) and 30.3 J/minute (Giosa). The driving pressure was 14.4cmH2O, the plateau pressure was 26.5cmH2O, the positive end-expiratory pressure was 12.1cmH2O, the elastance was 40.6cmH2O/L, the tidal volume was 0.36L, and the respiratory rate was 32 breaths/minute. The correlation between the Gattinoni and Giosa formulas was 0.98, with a bias of -3.4J/minute and a difference in the correlation of the resistance pressure of 0.39 (Gattinoni) and 0.24 (Giosa). Among the components, the correlations between elastance and driving pressure (0.88), positive end-expiratory pressure (-0.54) and tidal volume (-0.44) stood out.

CONCLUSION

In the analysis of mechanical ventilation for patients with SARS-CoV-2, it was found that the correlations of its components with mechanical power influenced its high momentary values and and that the correlations of its components with each other influenced their behavior throughout the study period. Because they have specific effects on the Gatinnoni-S and Giosa formulas, the mechanical ventilation components influenced their calculations and caused divergence in the mechanical power values.

Relationship between Driving Pressure and Mortality in Ventilated Patients with Heart Failure: A Cohort Study

Background

Heart failure (HF) is a leading cause of mortality and morbidity worldwide, with an increasing incidence. Invasive ventilation is considered to be essential for patients with HF. Previous studies have shown that driving pressure is associated with mortality in acute respiratory distress syndrome (ARDS). However, the relationship between driving pressure and mortality has not yet been examined in ventilated patients with HF. We assessed the association of driving pressure and mortality in patients with HF.

Methods

We conducted a retrospective cohort study of invasive ventilated adult patients with HF from the Medical Information Mart for Intensive Care-III database. We used multivariable logistic regression models, a generalized additive model, and a two-piecewise linear regression model to show the effect of the average driving pressure within 24 h of intensive care unit admission on in-hospital mortality.

Results

Six hundred and thirty-two invasive ventilated patients with HF were enrolled. Driving pressure was independently associated with in-hospital mortality (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06–1.18; P < 0.001) after adjusted potential confounders. A nonlinear relationship was found between driving pressure and in-hospital mortality, which had a threshold around 14.27 cmH₂O. The effect sizes and CIs below and above the threshold were 0.89 (0.75 to 1.05) and 1.17 (1.07 to 1.30), respectively.

Conclusions

There was a nonlinear relationship between driving pressure and mortality in patients with HF who were ventilated for more than 48 h, and this relationship was associated with increased in-hospital mortality when the driving pressure was more than 14.27 cmH₂O.

High risk of patient self-inflicted lung injury in COVID-19 with frequently encountered spontaneous breathing patterns: a computational modelling study

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.

In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications

Supplemental Digital Content is available in the text.

Objectives:

Patients with coronavirus disease 2019 acute respiratory distress syndrome appear to present with at least two distinct phenotypes: severe hypoxemia with relatively well-preserved lung compliance and lung gas volumes (type 1) and a more conventional acute respiratory distress syndrome phenotype, displaying the typical characteristics of the “baby lung” (type 2). We aimed to test plausible hypotheses regarding the pathophysiologic mechanisms underlying coronavirus disease 2019 acute respiratory distress syndrome and to evaluate the resulting implications for ventilatory management.

Design:

We adapted a high-fidelity computational simulator, previously validated in several studies of acute respiratory distress syndrome, to: 1) develop quantitative insights into the key pathophysiologic differences between the coronavirus disease 2019 acute respiratory distress syndrome and the conventional acute respiratory distress syndrome and 2) assess the impact of different positive end-expiratory pressure, Fio_2, and tidal volume settings.

Setting:

Interdisciplinary Collaboration in Systems Medicine Research Network.

Subjects:

The simulator was calibrated to represent coronavirus disease 2019 acute respiratory distress syndrome patients with both normal and elevated body mass indices undergoing invasive mechanical ventilation.

Interventions:

None.

Measurements and Main Results:

An acute respiratory distress syndrome model implementing disruption of hypoxic pulmonary vasoconstriction and vasodilation leading to hyperperfusion of collapsed lung regions failed to replicate clinical data on type 1 coronavirus disease 2019 acute respiratory distress syndrome patients. Adding mechanisms to reflect disruption of alveolar gas-exchange due to the effects of pneumonitis and heightened vascular resistance due to the emergence of microthrombi produced levels of ventilation perfusion mismatch and hypoxemia consistent with data from type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, while preserving close-to-normal lung compliance and gas volumes. Atypical responses to positive end-expiratory pressure increments between 5 and 15 cm H₂O were observed for this type 1 coronavirus disease 2019 acute respiratory distress syndrome model across a range of measures: increasing positive end-expiratory pressure resulted in reduced lung compliance and no improvement in oxygenation, whereas mechanical power, driving pressure, and plateau pressure all increased. Fio₂ settings based on acute respiratory distress syndrome network protocols at different positive end-expiratory pressure levels were insufficient to achieve adequate oxygenation. Incrementing tidal volumes from 5 to 10 mL/kg produced similar increases in multiple indicators of ventilator-induced lung injury in the type 1 coronavirus disease 2019 acute respiratory distress syndrome model to those seen in a conventional acute respiratory distress syndrome model.

Conclusions:

Our model suggests that use of standard positive end-expiratory pressure/Fio₂ tables, higher positive end-expiratory pressure strategies, and higher tidal volumes may all be potentially deleterious in type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, and that a highly personalized approach to treatment is advisable.