An open-loop, physiological model based decision support system can reduce pressure support while acting to preserve respiratory muscle function

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

Transparent decision support for mechanical ventilation using visualization of clinical preferences

Background

Systems aiding in selecting the correct settings for mechanical ventilation should visualize patient information at an appropriate level of complexity, so as to reduce information overload and to make reasoning behind advice transparent. Metaphor graphics have been applied to this effect, but these have largely been used to display diagnostic and physiologic information, rather than the clinical decision at hand. This paper describes how the conflicting goals of mechanical ventilation can be visualized and applied in making decisions. Data from previous studies are analyzed to assess whether visual patterns exist which may be of use to the clinical decision maker.

Materials and methods

The structure and screen visualizations of a commercial clinical decision support system (CDSS) are described, including the visualization of the conflicting goals of mechanical ventilation represented as a hexagon. Retrospective analysis is performed on 95 patients from 2 previous clinical studies applying the CDSS, to identify repeated patterns of hexagon symbols.

Results

Visual patterns were identified describing optimal ventilation, over and under ventilation and pressure support, and over oxygenation, with these patterns identified for both control and support modes of mechanical ventilation. Numerous clinical examples are presented for these patterns illustrating their potential interpretation at the bedside.

Conclusions

Visual patterns can be identified which describe the trade-offs required in mechanical ventilation. These may have potential to reduce information overload and help in simple and rapid identification of sub-optimal settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00974-5.

Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol

Background

The acute respiratory distress syndrome (ARDS) occurs in response to a variety of insults, and mechanical ventilation is life-saving in this setting, but ventilator-induced lung injury can also contribute to the morbidity and mortality in the condition. The Beacon Caresystem is a model-based bedside decision support system using mathematical models tuned to the individual patient’s physiology to advise on appropriate ventilator settings. Personalised approaches using individual patient description may be particularly advantageous in complex patients, including those who are difficult to mechanically ventilate and wean, in particular ARDS.

Methods

We will conduct a multi-centre international randomised, controlled, allocation concealed, open, pragmatic clinical trial to compare mechanical ventilation in ARDS patients following application of the Beacon Caresystem to that of standard routine care to investigate whether use of the system results in a reduction in driving pressure across all severities and phases of ARDS.

Discussion

Despite 20 years of clinical trial data showing significant improvements in ARDS mortality through mitigation of ventilator-induced lung injury, there remains a gap in its personalised application at the bedside. Importantly, the protective effects of higher positive end-expiratory pressure (PEEP) were noted only when there were associated decreases in driving pressure. Hence, the pressures set on the ventilator should be determined by the diseased lungs’ pressure-volume relationship which is often unknown or difficult to determine. Knowledge of extent of recruitable lung could improve the ventilator driving pressure. Hence, personalised management demands the application of mechanical ventilation according to the physiological state of the diseased lung at that time. Hence, there is significant rationale for the development of point-of-care clinical decision support systems which help personalise ventilatory strategy according to the current physiology. Furthermore, the potential for the application of the Beacon Caresystem to facilitate local and remote management of large numbers of ventilated patients (as seen during this COVID-19 pandemic) could change the outcome of mechanically ventilated patients during the course of this and future pandemics.

Trial registration

ClinicalTrials.gov identifier NCT04115709. Registered on 4 October 2019, version 4.0

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05967-2.

Ventilator-Associated Lung Injury

Ventilatory support, while life saving, can also cause or aggravate lung injury through several mechanisms which are encompassed within ventilator-associated lung injury (VALI). The important realizationin the acute respiratory distress syndrome that the “baby” lung resided in non-dependent areas led to the conceptualization of “lung rest” to reduce stress and strain to exposed alveolar units. We discuss concepts and mechanisms within VALI that ultimately induce maladaptive lung responses, as well as, current and future management strategies to detect and mitigate VALI at the bedside.

Central venous pressure swing outperforms diaphragm ultrasound as a measure of inspiratory effort during pressure support ventilation in COVID-19 patients

Purpose

The COVID-19-related shortage of ICU beds magnified the need of tools to properly titrate the ventilator assistance. We investigated whether bedside-available indices such as the ultrasonographic changes in diaphragm thickening ratio (TR) and the tidal swing in central venous pressure (ΔCVP) are reliable estimates of inspiratory effort, assessed as the tidal swing in esophageal pressure (ΔPes).

Methods

Prospective, observational clinical investigation in the intensive care unit of a tertiary care Hospital. Fourteen critically-ill patients were enrolled (age 64 ± 7 years, BMI 29 ± 4 kg/m²), after 6 [3; 9] days from onset of assisted ventilation. A three-level pressure support trial was performed, at 10 (PS10), 5 (PS5) and 0 cmH₂O (PS0). In each step, the esophageal and central venous pressure tidal swing were recorded, as well as diaphragm ultrasound.

Results

The reduction of pressure support was associated with an increased respiratory rate and a reduced tidal volume, while minute ventilation was unchanged. ΔPes significantly increased with reducing support (5 [3; 8] vs. 8 [14; 13] vs. 12 [6; 16] cmH₂O, p < 0.0001), as did the diaphragm TR (9.2 ± 6.1 vs. 17.6 ± 7.2 vs. 28.0 ± 10.0%, p < 0.0001) and the ΔCVP (4 [3; 7] vs. 8 [5; 9] vs. 10 [7; 11] cmH₂O, p < 0.0001). ΔCVP was significantly associated with ΔPes (R² = 0.810, p < 0.001), as was diaphragm TR, albeit with a lower coefficient of determination (R² = 0.399, p < 0.001).

Conclusions

In patients with COVID-19-associated respiratory failure undergoing assisted mechanical ventilation, ΔCVP is a better estimate of inspiratory effort than diaphragm ultrasound.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10877-021-00674-4.

Monitoring diaphragm function in the ICU

PURPOSE OF REVIEW

To review the clinical problem of diaphragm function in critically ill patients and describes recent advances in bedside monitoring of diaphragm function.

RECENT FINDINGS

Diaphragm weakness, a consequence of diaphragm dysfunction and atrophy, is common in the ICU and associated with serious clinical consequences. The use of ultrasound to assess diaphragm structure (thickness, thickening) and mobility (caudal displacement) appears to be feasible and reproducible, but no large-scale 'real-life' study is available. Diaphragm ultrasound can also be used to evaluate diaphragm muscle stiffness by means of shear-wave elastography and strain by means of speckle tracking, both of which are correlated with diaphragm function in healthy. Electrical activity of the diaphragm is correlated with diaphragm function during brief airway occlusion, but the repeatability of these measurements exhibits high within-subject variability.

SUMMARY

Mechanical ventilation is involved in the pathogenesis of diaphragm dysfunction, which is associated with severe adverse events. Although ultrasound and diaphragm electrical activity could facilitate monitoring of diaphragm function to deliver diaphragm-protective ventilation, no guidelines concerning the use of these modalities have yet been published. The weaning process, assessment of patient-ventilator synchrony and evaluation of diaphragm function may be the most clinically relevant indications for these techniques.

Lung- and Diaphragm-Protective Ventilation

Mechanical ventilation can cause acute diaphragm atrophy and injury, and this is associated with poor clinical outcomes. Although the importance and impact of lung-protective ventilation is widely appreciated and well established, the concept of diaphragm-protective ventilation has recently emerged as a potential complementary therapeutic strategy. This Perspective, developed from discussions at a meeting of international experts convened by PLUG (the Pleural Pressure Working Group) of the European Society of Intensive Care Medicine, outlines a conceptual framework for an integrated lung- and diaphragm-protective approach to mechanical ventilation on the basis of growing evidence about mechanisms of injury. We propose targets for diaphragm protection based on respiratory effort and patient-ventilator synchrony. The potential for conflict between diaphragm protection and lung protection under certain conditions is discussed; we emphasize that when conflicts arise, lung protection must be prioritized over diaphragm protection. Monitoring respiratory effort is essential to concomitantly protect both the diaphragm and the lung during mechanical ventilation. To implement lung- and diaphragm-protective ventilation, new approaches to monitoring, to setting the ventilator, and to titrating sedation will be required. Adjunctive interventions, including extracorporeal life support techniques, phrenic nerve stimulation, and clinical decision-support systems, may also play an important role in selected patients in the future. Evaluating the clinical impact of this new paradigm will be challenging, owing to the complexity of the intervention. The concept of lung- and diaphragm-protective ventilation presents a new opportunity to potentially improve clinical outcomes for critically ill patients.

Intensive Care Weaning (iCareWean) protocol on weaning from mechanical ventilation: a single-blinded multicentre randomised control trial comparing an open-loop decision support system and routine care, in the general intensive care unit

INTRODUCTION

Automated systems for ventilator management to date have been either fully heuristic rule-based systems or based on a combination of simple physiological models and rules. These have been shown to reduce the duration of mechanical ventilation in simple to wean patients. At present, there are no published studies that evaluate the effect of systems that use detailed physiological descriptions of the individual patient.The BEACON Caresystem is a model-based decision support system that uses mathematical models of patients' physiology in combination with models of clinical preferences to provide advice on appropriate ventilator settings. An individual physiological description may be particularly advantageous in selecting the appropriate therapy for a complex, heterogeneous, intensive care unit (ICU) patient population.

METHODS AND ANALYSIS

Intenive Care weaning (iCareWean) is a single-blinded, multicentre, prospective randomised control trial evaluating management of mechanical ventilation as directed by the BEACON Caresystem compared with that of current care, in the general intensive care setting. The trial will enrol 274 participants across multiple London National Health Service ICUs. The trial will use a primary outcome of duration of mechanical ventilation until successful extubation.

ETHICS AND DISSEMINATION

Safety oversight will be under the direction of an independent committee of the study sponsor. Study approval was obtained from the regional ethics committee of the Health Research Authority (HRA), (Research Ethic Committee (REC) reference: 17/LO/0887. Integrated Research Application System (IRAS) reference: 226610. Results will be disseminated through international critical care conference/symposium and publication in peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov under NCT03249623. This research is registered with the National Institute for Health Research under CPMS ID: 34831.

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid–base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient’s transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO₂ respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

A Pilot Bench Study of Decision Support for Proportional Assist Ventilation

The purpose was to develop a bench setup for testing a decision support system (DSS) for proportional assist ventilation (PAV). The test setup was based on a patient simulator connected to a mechanical ventilator with the DSS measurement sensors connected to the respiratory circuit. A test case was developed with parameters of lung mechanics reflecting a patient with mild acute respiratory distress syndrome. Five experiments were performed starting at different levels of percentage support (%Supp) and continuing until the DSS advised to remain at current settings. Final advice ranged from %Supp of 50-70%, indicating some dependence of baseline level, but with resulting patient effort estimates indicating that this may not be clinically important. Further studies are required of test cases reflecting different patient types and in patients.