Virtual patients for mechanical ventilation in the intensive care unit

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE

Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort.

METHODS

Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison.

RESULTS

Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN.

CONCLUSION

The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Model-driven engineering for digital twins: a graph model-based patient simulation application

Introduction

Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector.

Methods

This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation.

Results

A short case study is presented to demonstrate the viability of the proposed simulation architecture.

Discussion

The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.

Mechanical and morphological characterization of the emphysematous lung tissue

Irreversible alveolar airspace enlargement is the main characteristic of pulmonary emphysema, which has been extensively studied using animal models. While the alterations in lung mechanics associated with these morphological changes have been documented in the literature, the study of the mechanical behavior of parenchymal tissue from emphysematous lungs has been poorly investigated. In this work, we characterize the mechanical and morphological properties of lung tissue in elastase-induced emphysema rat models under varying severity conditions. We analyze the non-linear tissue behavior using suitable hyperelastic constitutive models that enable to compare different non-linear responses in terms of hyperelastic material parameters. We further analyze the effect of the elastase dose on alveolar morphology and tissue material parameters and study their connection with respiratory-system mechanical parameters. Our results show that while the lung mechanical function is not significantly influenced by the elastase treatment, the tissue mechanical behavior and alveolar morphology are markedly affected by it. We further show a strong association between alveolar enlargement and tissue softening, not evidenced by respiratory-system compliance. Our findings highlight the importance of understanding tissue mechanics in emphysematous lungs, as changes in tissue properties could detect the early stages of emphysema remodeling. STATEMENT OF SIGNIFICANCE: Gas exchange is vital for life and strongly relies on the mechanical function of the lungs. Pulmonary emphysema is a prevalent respiratory disease where alveolar walls are damaged, causing alveolar enlargement that induces harmful changes in the mechanical response of the lungs. In this work, we study how the mechanical properties of lung tissue change during emphysema. Our results from animal models show that tissue properties are more sensitive to alveolar enlargement due to emphysema than other mechanical properties that describe the function of the whole respiratory system.

Effect of propolis mouthwash on the incidence of ventilator-associated pneumonia in intensive care unit patients: a comparative randomized triple-blind clinical trial

OBJECTIVES

Ventilator-associated pneumonia (VAP) increases the length of hospitalization and mortality rate. This study aimed to determine the effect of propolis mouthwash on the incidence of VAP in intensive care unit (ICU) patients.

MATERIALS AND METHODS

Triple-blind, comparative randomized, controlled clinical trial was conducted over one year, with 110 ICU patients at Imam-Hossein and Bahar hospitals (Shahroud) and Kowsar Hospital (Semnan) in Iran. The intervention group used 15 cc of 0.06% propolis mouthwash solution twice daily at 8 AM and 4 PM for seven days. The control group used 15 cc of 0.2% chlorhexidine mouthwash at the same times and duration. Data were collected using a demographic questionnaire, APACHE II, Beck Oral Assessment Scale, and Modified Clinical Pulmonary Infection Score (MCPIS).

RESULTS

There was no significant difference in demographic information, disease severity, and oral health between the two groups before and after intervention (P > 0.05). The incidence of VAP in the intervention group compared to the control group was 10.9% vs. 30.9% on the third day (P = 0.0166, 95% CI: 0.53-0.83 and RR = 0.35), 23.6% vs. 43.6% on the fifth day (P = 0.0325 and 95% CI: 0.31-0.95 and RR = 0.54), and 25.5% vs. 47.3% on the seventh day (P = 0.0224, 95% CI: 0.32-0.92, and RR = 0.54). The Mann-Whitney indicated the incidence of VAP was significantly lower in the intervention group on the third, fifth, and seventh days.

CONCLUSION

Propolis mouthwash can be considered as an alternative to chlorhexidine mouthwash for ICU patients.

CLINICAL RELEVANCE

Propolis mouthwash serves as a simple, economical intervention to potentially reduce incidence of VAP.

TRIAL REGISTRATION

(IRCT20110427006318N12, date 02.04.2019).

Digital twins for health: a scoping review

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

Pulmonary response prediction through personalized basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings.

METHODS

This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps.

RESULTS

The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95.

CONCLUSIONS

The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.

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.

Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses

BACKGROUND

Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop.

METHODS

Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.

RESULTS

The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.

CONCLUSIONS

The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

Virtual patient with temporal evolution for mechanical ventilation trial studies: A stochastic model approach

BACKGROUND AND OBJECTIVE

Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, time-varying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model.

METHODS

A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.

RESULTS

A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.

CONCLUSION

VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

Modelling patient specific cardiopulmonary interactions

Mechanical ventilation is well known for having detrimental effects on the cardiovascular system, particularly when using high positive end-expiratory pressure. High positive end-expiratory pressure levels cause a decrease in stroke volume, which, under normal conditions, usually bring about a decrease in stressed blood volume. Stressed blood volume, defined as the total pressure generating volume of the cardiovascular system, has been shown to be a potential index of fluid responsiveness, making it a potentially important diagnostic tool. Generally, respiratory and haemodynamic care are provided independently of one another. However, that positive end-expiratory pressure alters both stroke volume and stressed blood volume suggests both the pulmonary and cardiovascular state should be conjointly optimised and used to guide positive end-expiratory pressure. However, the complex and patient-specific nature of cardiopulmonary interactions which occur during mechanical ventilation presents a challenge for accurate modelling of respiratory and cardiovascular interactions required to better optimise care. Previous models attempting to incorporate cardiopulmonary interactions have suffered from poor reliability at higher PEEP levels, largely due to an exaggerated effect of intrathoracic pressure on the cardiovascular system. A new parameter, alpha, is added to a previously validated cardiopulmonary model, to modulate the percentage of intrathoracic pressure applied to the vena cava and left ventricle. The new parameter aims to increase reliability under high PEEP conditions as well as provide a patient specific solution to modelling cardiopulmonary interactions. The results from the identified optimal alpha are compared to the original model to investigate how this new parameter may be used to create a more patient-specific cardiopulmonary model, which would be better suited for guidance of care in the ICU.

Low-cost structured light imaging of regional volume changes for use in assessing mechanical ventilation

BACKGROUND

Optimal setting of mechanical ventilators is critical for improving outcomes. Accurate, predictive lung mechanics models are effective in optimizing MV settings, but only at a global level as they cannot estimate regional lung volume ventilation to assess the potential of local distension or under-ventilation. This study presents a low-cost structured light system for non-contact high resolution chest motion measurement to estimate regional lung volume changes.

METHODS

The system consists of a structured light projector and two cameras. A new pattern is designed to extract motion from sub-regions of the chest surface, and an efficient feature is proposed to provide a fast and accurate correspondence matching between two views. Reconstruction of 3D surface points is based on the matched points and stereo method. Asymmetric distribution of tidal volume into left and right lungs is estimated based on reconstructed regional chest expansion. A proof-of-concept experiment using a dummy model and two test lungs connected to a ventilator to provide differential chest expansion is conducted under tidal volumes of 400 ml, 500 ml and 600 ml, with results compared to the widely-used SURF and ORB methods.

RESULTS

Compared to the SURF and ORB methods, the proposed method is more computationally efficient with ∼40% less computational time cost, and higher accuracy for dense point correspondence. Finally, the proposed method estimated the region lung volumes with the maximum error of 8 ml under 600 ml tidal volume, indicating a good accuracy.

CONCLUSIONS

Surface reconstruction results in a proof-of-concept experiment with differential chest expansion show good performance for the proposed pattern and method in extracting the key information for regional chest expansion. The proposed method is generalizable, with potential for use in other applications.

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

BACKGROUND AND OBJECTIVE

Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment.

METHODS

The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated.

RESULTS

This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol.

CONCLUSION

The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Whole-lung finite-element models for mechanical ventilation and respiratory research applications

Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.

CAREDAQ: Data acquisition device for mechanical ventilation waveform monitoring

Mechanical ventilation (MV) provides respiratory support for critically ill patients in the intensive care unit (ICU). Waveform data output by the ventilator provides valuable physiological and diagnostic information. However, existing systems do not provide full access to this information nor allow for real-time, non-invasive data collection. Therefore, large amounts of data are lost and analysis is limited to short samples of breathing cycles. This study presents a data acquisition device for acquiring and monitoring patient ventilation waveform data. Acquired data can be exported to other systems, allowing users to further analyse data and develop further clinically useful parameters. These parameters, together with other ventilatory information, can help personalise and guide MV treatment. The device is designed to be easily replicable, low-cost, and scalable according to the number of patient beds. Validation was carried out by assessing system performance and stability over prolonged periods of 7 days of continuous use. The device provides a platform for future integration of machine-learning or model-based modules, potentially allowing real-time, proactive, patient-specific MV guidance and decision support to improve the quality and productivity of care and outcomes.

Algebraic approximation of the distributed model for the pressure drop in the respiratory airways

The complexity of the human respiratory system causes that one of the main methods of analyzing the dynamic pulmonary phenomena and interpreting experimental results are simulations of its computational models. Among the most compound elements of these models, apart from the bronchial tree structure, is the phenomenon of flow limitation in flexible bronchi, which causes them to collapse with increasing flow, thus their properties, such as resistance, compliance and inertance, are highly nonlinear and time-varying. Commonly, this phenomenon is ignored, or a distributed model for the airway pressure drop is applied, simulated with a modified numerical solver of this differential equation (ODE). The disadvantages of this solution are the problems with taking into account the inherent singularity of the model and the long computation time due to iterative nature of the ODE procedure. The aim of the work was to derive an algebraic approximation of this distributed model, suitable for implementation in continuous dynamic models, to validate it by comparing the results of simulations with the respiratory system model including approximate and original (ODE solver) numerical procedures, as well as to evaluate possible acceleration of calculations. All simulations, including spontaneous breathing, mechanical ventilation with the optimal ventilatory waveform and forced expiration, proved that algebraic approximation yielded results negligibly differing from the ODE solution, and shortened the computation time by an order. The proposed approach is an attractive alternative in the case of computer implementations of pulmonary models, where simulations of flows and pressures in the complex respiratory system are of primary importance.

Non-invasive over-distension measurements: data driven vs model-based

Clinical measurements offer bedside monitoring aiming to minimise unintended over-distension, but have limitations and cannot be predicted for changes in mechanical ventilation (MV) settings and are only available in certain MV modes. This study introduces a non-invasive, real-time over-distension measurement, which is robust, predictable, and more intuitive than current methods. The proposed over-distension measurement, denoted as OD, is compared with the clinically proven stress index (SI). Correlation is analysed via R² and Spearman r_s. The OD safe range corresponding to the unit-less SI safe range (0.95-1.05) is calibrated by sensitivity and specificity test. Validation is fulfilled with 19 acute respiratory distress syndrome (ARDS) patients data (196 cases), including assessment across ARDS severity. Overall correlation between OD and SI yielded R² = 0.76 and Spearman r_s = 0.89. Correlation is higher considering only moderate and severe ARDS patients. Calibration of OD to SI yields a safe range defined: 0 ≤ OD ≤ 0.8 cmH₂O. The proposed OD offers an efficient, general, real-time measurement of patient-specific lung mechanics, which is more intuitive and robust than SI. OD eliminates the limitations of SI in MV mode and its less intuitive lung status value. Finally, OD can be accurately predicted for new ventilator settings via its foundation in a validated predictive personalized lung mechanics model. Therefore, OD offers potential clinical value over current clinical methods.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

BACKGROUND

Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.

METHODS

We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.

RESULTS

Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.

CONCLUSIONS

We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

BACKGROUND

Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics.

METHODS

Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony.

RESULTS

Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony.

CONCLUSIONS

Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.

Stochastic integrated model-based protocol for volume-controlled ventilation setting

BACKGROUND AND OBJECTIVE

Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration.

METHODS

A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.

RESULTS

From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.

CONCLUSIONS

Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.

Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients

BACKGROUND AND OBJECTIVE

Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses.

METHODS

This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.

RESULTS

The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH₂O and 0.55 [0.19 - 1.20] mL/kg were obtained.

CONCLUSIONS

Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.

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.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Over-distension prediction via hysteresis loop analysis and patient-specific basis functions in a virtual patient model

BACKGROUND AND OBJECTIVE

Recruitment maneuvers (RMs) with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveolar collapse. However, a suboptimal PEEP could induce undesired injury in lungs by insufficient or excessive breath support. Thus, a predictive model for patient response under PEEP changes could improve clinical care and lower risks.

METHODS

This research adds novel elements to a virtual patient model to identify and predict patient-specific lung distension to optimise and personalise care. Model validity and accuracy are validated using data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0-12cmH₂O), yielding 623 prediction cases. Predictions were made up to ΔPEEP = 12cmH₂O ahead covering 6x2cmH₂O PEEP steps.

RESULTS

Using the proposed lung distension model, 90% of absolute peak inspiratory pressure (PIP) prediction errors compared to clinical measurement are within 3.95cmH₂O, compared with 4.76cmH₂O without this distension term. Comparing model-predicted and clinically measured distension had high correlation increasing to R² = 0.93-0.95 if maximum ΔPEEP ≤ 6cmH₂O. Predicted dynamic functional residual capacity (V_frc) changes as PEEP rises yield 0.013L median prediction error for both prediction groups and overall R² of 0.84.

CONCLUSIONS

Overall results demonstrate nonlinear distension mechanics are accurately captured in virtual lung mechanics patients for mechanical ventilation, for the first time. This result can minimise the risk of lung injury by predicting its potential occurrence of distension before changing ventilator settings. The overall outcomes significantly extend and more fully validate this virtual mechanical ventilation patient model.

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

BACKGROUND

Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.

METHODS

Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH₂O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.

RESULTS

Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH₂O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH₂O, with range -7.9 - -1.9 cmH₂O. Per-patient E and R were 16.4 [13.6-21.8] cmH₂O/L and 9.2 [6.4-13.1] cmH₂O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R²=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.

CONCLUSIONS

The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.