The Intelligent Ventilator (INVENT) project: the role of mathematical models in translating physiological knowledge into clinical practice

Non-Invasive Assessment of Abdominal/Diaphragmatic and Thoracic/Intercostal Spontaneous Breathing Contributions

(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing effort. Thus, it has the potential, for example, to help differentiate between respiratory disease and dysfunctional breathing, which otherwise can present with similar symptoms such as breath rate. Current automatic methods of measuring chest expansion are invasive, intrusive, and/or difficult to conduct in conjunction with pulmonary function testing (spontaneous breathing pressure and flow measurements). (2) Methods: A tape measure and rotary encoder band system developed by the authors was used to directly measure changes in thoracic and abdominal circumferences without the calibration required for analogous strain-gauge-based or image processing solutions. (3) Results: Using scaling factors from the literature allowed for the conversion of thoracic and abdominal motion to lung volume, combining motion measurements correlated to flow-based measured tidal volume (normalised by subject weight) with R2 = 0.79 in data from 29 healthy adult subjects during panting, normal, and deep breathing at 0 cmH2O (ZEEP), 4 cmH2O, and 8 cmH2O PEEP (positive end-expiratory pressure). However, the correlation for individual subjects is substantially higher, indicating size and other physiological differences should be accounted for in scaling. The pattern of abdominal and chest expansion was captured, allowing for the analysis of muscular recruitment patterns over different breathing modes and the differentiation of active and passive modes. (4) Conclusions: The method and measuring device(s) enable the validation of patient-specific lung mechanics models and accurately elucidate diaphragmatic-driven volume changes due to intercostal/chest-wall muscular recruitment and elastic recoil.

Respiratory pressure and split flow data collection device with rapid occlusion attachment

Respiratory model-based methods require datasets containing enough dynamics to ensure model identifiability for development and validation. Rapid expiratory occlusion has been used to identify elastance and resistance within a single breath. Currently accepted practice for rapid expiratory occlusion involves a 100 ms occlusion of the expiratory pathway. This article presents a low-cost modular rapid shutter attachment to enable identification of passive respiratory mechanics. Shuttering faster than 100 ms creates rapid expiratory occlusion without the added dynamics of muscular response to shutter closure, by eliminating perceived expiratory blockage via high shutter speed. The shutter attachment fits onto a non-invasive venturi-based flow meter with separated inspiratory and expiratory pathways, established using one-way valves. Overall, these elements allow comprehensive collection of respiratory pressure and flow datasets with relatively very rapid expiratory occlusion.

Airflow and dynamic circumference of abdomen and thorax for adults at varied continuous positive airway pressure ventilation settings and breath rates

Continuous positive airway pressure (CPAP) ventilation is a commonly prescribed respiratory therapy providing positive end-expiratory pressure (PEEP) to assist breathing and prevent airway collapse. Setting PEEP is highly debated and it is thus primarily titrated based on symptoms of excessive or insufficient support. However, titration periods are clinician intensive and can result in barotrauma or under-oxygenation during the process. Developing model-based methods to more efficiently personalise CPAP therapy based on patient-specific response requires clinical data of lung/CPAP interactions. To this end, a trial was conducted to establish a dataset of healthy subjects lung/CPAP interaction. Pressure, flow, and tidal volume were recorded alongside secondary measures of dynamic chest and abdominal circumference, to better validate model outcomes and assess breathing modes, muscular recruitment, and effort. N = 30 subjects (15 male; 15 female) were included. Self-reported asthmatics and smokers/vapers were included, offering a preliminary assessment of any potential differences in response to CPAP from lung stiffness changes in these scenarios. Additional demographics associated with lung function (sex, age, height, and weight) were also recorded.

Computational physiological models for individualised mechanical ventilation: a systematic literature review focussing on quality, availability, and clinical readiness

BACKGROUND

Individualised optimisation of mechanical ventilation (MV) remains cumbersome in modern intensive care medicine. Computerised, model-based support systems could help in tailoring MV settings to the complex interactions between MV and the individual patient's pathophysiology. Therefore, we critically appraised the current literature on computational physiological models (CPMs) for individualised MV in the ICU with a focus on quality, availability, and clinical readiness.

METHODS

A systematic literature search was conducted on 13 February 2023 in MEDLINE ALL, Embase, Scopus and Web of Science to identify original research articles describing CPMs for individualised MV in the ICU. The modelled physiological phenomena, clinical applications, and level of readiness were extracted. The quality of model design reporting and validation was assessed based on American Society of Mechanical Engineers (ASME) standards.

RESULTS

Out of 6,333 unique publications, 149 publications were included. CPMs emerged since the 1970s with increasing levels of readiness. A total of 131 articles (88%) modelled lung mechanics, mainly for lung-protective ventilation. Gas exchange (n = 38, 26%) and gas homeostasis (n = 36, 24%) models had mainly applications in controlling oxygenation and ventilation. Respiratory muscle function models for diaphragm-protective ventilation emerged recently (n = 3, 2%). Three randomised controlled trials were initiated, applying the Beacon and CURE Soft models for gas exchange and PEEP optimisation. Overall, model design and quality were reported unsatisfactory in 93% and 21% of the articles, respectively.

CONCLUSION

CPMs are advancing towards clinical application as an explainable tool to optimise individualised MV. To promote clinical application, dedicated standards for quality assessment and model reporting are essential. Trial registration number PROSPERO- CRD42022301715 . Registered 05 February, 2022.

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.

Model-based patient matching for in-parallel pressure-controlled ventilation

Background

Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV.

Methods

The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation.

Results

The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care.

Conclusion

This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-022-00983-y.

Quantifying ventilator unloading in CPAP ventilation

BACKGROUND

The intrinsic (muscular) patient effort driving inspiration in non-invasive ventilation modes, such as continuous positive airway pressure (CPAP) therapy, has not been identified from non-invasive data. Current CPAP settings are based on clinical judgment and assessment of symptoms of respiratory distress. Non-optimal settings, including too much positive end expiratory pressure (PEEP) can cause unintended lung injury and ventilator unloading, where patient effort drops and the CPAP device enables too much work being imposed on the injured lung. Currently, there is no non-invasive means of quantifying or identifying these effects.

METHODS

A novel model-based method of ascertaining intrinsic patient work of breathing (WOB) in CPAP is developed based on linear single compartment and 2nd order b-spline models previously used in invasive ventilation modes. Results are compared to current clinical indications, such as total Imposed WOB from the CPAP device and beak length, the latter of which is the clinical metric used to indicate alveolar overdistension. Intrinsic and Imposed WOB are compared. The hypothesis is that ventilator unloading can be assessed as a decrease in Intrinsic WOB relative to Imposed WOB, as PEEP and associated ventilator unloading rise. This hypothesis is tested using 14 subjects from a CPAP trial of several breathing rates at two PEEP levels.

RESULTS

The ratio of Intrinsic to Imposed WOB, normalised per unit tidal volume, decreased with increasing PEEP (4-7 cm H₂O), capturing the expected trend of ventilator unloading. Ventilator unloading was observed across all breathing rates. Beak length measurements showed no conclusive evidence of capturing overdistension at higher PEEP or ventilator unloading.

CONCLUSIONS

Patient Intrinsic WOB in CPAP was non-invasively quantified using model-based methods, based on pressure and flow measurements. The ratio of Intrinsic to Imposed WOB per unit tidal volume clearly and consistently showed ventilator unloading across all patients and breathing rates, with Intrinsic WOB decreasing with increasing PEEP. This trend was not observed in the current clinical metric of beak length. Non-invasively quantifying Intrinsic WOB and ventilator unloading is the critical first step to objectively optimising clinical CPAP settings, patient care, and outcomes.

Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support

Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where inter- and intra- patient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH₂O. Prediction errors for peak inspiratory volume for an increase of 16 cmH₂O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care.

Biomedical engineer's guide to the clinical aspects of intensive care mechanical ventilation

Background

Mechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO₂) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective.

Discussion

This review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions.

Conclusion

Engineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Model-based advice for mechanical ventilation: From research (INVENT) to product (Beacon Caresystem)

This paper describes the structure and functionality of a physiological model-based system for providing advice on the settings of mechanical ventilation. Use of the system is presented with examples of patients on support and control modes of mechanical ventilation.

Perpetual and Virtual Patients for Cardiorespiratory Physiological Studies

As a result of innovations in informatics over the last decades, physiologic models elaborated in the second half of the 20th century could be transformed into specific virtual patients called computational models. These models, developed initially for teaching purposes, are of great potential interest in responding to current concerns about improving patient care and safety. However, even if there are obvious advantages to using computational models in cardiorespiratory management, major concerns persist as to their reliability and their ability to recreate real patient physiologic evolution over time. Once developed, these models require complex validation and configuration phases prior to implementation in daily practice. This article focuses on the development of computational models, and reviews the methodologies to clinically validate the models including specific patient databases (perpetual patients) and the use in clinical practice including very high fidelity simulation.

Analysis of the method for ventilation heterogeneity assessment using the Otis model and forced oscillations

Increased heterogeneity of the lung disturbs pulmonary gas exchange. During bronchoconstriction, inflammation of lung parenchyma or acute respiratory distress syndrome, inhomogeneous lung ventilation can become bimodal and increase the risk of ventilator-induced lung injury during mechanical ventilation. A simple index sensitive to ventilation heterogeneity would be very useful in clinical practice. In the case of bimodal ventilation, the index (H) can be defined as the ratio between the longer and shorter time constant characterising regions of contrary mechanical properties. These time constants can be derived from the Otis model fitted to input impedance (Zin) measured using forced oscillations. In this paper we systematically investigated properties of the aforementioned approach. The research included both numerical simulations and real experiments with a dual-lung simulator. Firstly, a computational model mimicking the physical simulator was derived and then used as a forward model to generate synthetic flow and pressure signals. These data were used to calculate the input impedance and then the Otis inverse model was fitted to Zin by means of the Levenberg-Marquardt (LM) algorithm. Finally, the obtained estimates of model parameters were used to compute H. The analysis of the above procedure was performed in the frame of Monte Carlo simulations. For each selected value of H, forward simulations with randomly chosen lung parameters were repeated 1000 times. Resulting signals were superimposed by additive Gaussian noise. The estimated values of H properly indicated the increasing level of simulated inhomogeneity, however with underestimation and variation increasing with H. The main factor responsible for the growing estimation bias was the fixed starting vector required by the LM algorithm. Introduction of a correction formula perfectly reduced this systematic error. The experimental results with the dual-lung simulator confirmed potential of the proposed procedure to properly deduce the lung heterogeneity level. We conclude that the heterogeneity index H can be used to assess bimodal ventilation imbalances in cases when this phenomenon dominates lung properties, however future analyses, including the impact of lung tissue viscoelasticity and distributed airway or tissue inhomogeneity on H estimates, as well as studies in the time domain, are advisable.

Measuring gas exchange with step changes in inspired oxygen: an analysis of the assumption of oxygen steady state in patients suffering from COPD

Bedside estimation of pulmonary gas exchange efficiency may be possible from step changes in FIO2 and subsequent measurement of arterial oxygenation at steady state conditions. However, a steady state may not be achieved quickly after a change in FIO2, especially in patients with lung disease such as COPD, rendering this approach cumbersome. This paper investigates whether breath by breath measurement of respiratory gas and arterial oxygen levels as FIO2 is changed can be used as a much more rapid alternative to collecting data from steady state conditions for measuring pulmonary gas exchange efficiency. Fourteen patients with COPD were studied using 4-5 step changes in FIO2 in the range of 0.15-0.35. Values of expired respiratory gas and arterial oxygenation were used to calculate and compare the parameters of a mathematical model of pulmonary gas exchange in two cases: from data at steady state; and from breath by breath data prior to achievement of a steady state. For each patient, the breath by breath data were corrected for the delay in arterial oxygen saturation changes following each change in FIO2. Calculated model parameters were shown to be similar for the two data sets, with Bland-Altman bias and limits of agreement of -0.4 and -3.0 to 2.2 % for calculation of pulmonary shunt and 0.17 and -0.47 to 0.81 kPa for alveolar to end-capillary PO2, a measure of oxygen abnormality due to shunting plus regions of low [Formula: see text] A/[Formula: see text] ratio. This study shows that steady state oxygen levels may not be necessary when estimating pulmonary gas exchange using changes in FIO2. As such this technique may be applicable in patients with lung disease such as COPD.

Closed loop mechanical ventilation

Mechanical ventilation is a sophisticated technique with very narrow therapeutic ranges i.e. highly efficient and able to keep alive the most severe patients, but with considerable side effects and unwanted complications if not properly and timely used. Computerized protocols, closed loop systems, decision support, all terms which need to be defined, may help making mechanical ventilation safer and more efficient. The present paper will provide a short overview on technical and engineering considerations regarding closed loop controlled ventilation as well as tangible clinical evidences supporting the previous statement.