Quantifying ventilator unloading in CPAP ventilation

Pulmonary function testing dataset of pressure and flow, dynamic circumference, heart rate, and aeration monitoring

Respiratory data was collected from 20 subjects, with an even sex distribution, in the low-risk clinical unit at the University of Canterbury. Ethical consent for this trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2023/30/LR-PS). Respiratory data were collected, for each subject, over three tests consisting of: 1) increasing set PEEP from a starting point of ZEEP using a CPAP machine; 2) test 1 repeated with two simulated apnoea's (breath holds) at each set PEEP; and 3) three forced expiratory manoeuvres at ZEEP. Data were collected using a custom pressure and flow sensor device, ECG, PPG, Garmin HRM Dual heartrate belt, and a Dräeger PulmoVista 500 Electrical Impedance Tomography (EIT) machine. Subject demographic data was also collected prior to the trial, in a questionnaire, with measurement equipment available. These data aim to inform the development of pulmonary mechanics models and titration algorithms.

Respiratory monitoring dataset, with rapid expiratory occlusions, over increasing positive airway pressure ventilation

Resting breathing data was collected from 80 smokers, vapers, asthmatics, and otherwise healthy people in the low-risk clinical unit at the University of Canterbury. Subjects were asked to breathe normally through a full-face mask connected to a Fisher and Paykel Healthcare SleepStyle SPSCAA CPAP device. PEEP (Positive End-Expiratory Pressure) support was increased from 4 to 12 cmH2O in 0.5 cmH2O increments. Data was also collected during resting breathing at ZEEP (0 cmH2O) before and after the PEEP trial. The trial was conducted under University of Canterbury Human Research Ethics Committee consent (Ref: HREC 2023/04/LR-PS). Data was collected by and Dräeger PulmoVista 500 EIT machine and a custom Venturi-based pressure and flow sensor device connected in series with the CPAP and full-face mask. The outlined dataset includes pressure, flow, volume, dynamic circumference (thoracic and abdominal, and cross-sectional aeration. Subject demographic data was self-reported using a questionnaire given prior to the trial.

Simulated obstructive respiratory disease dataset over increasing positive end-expiratory pressure

The breathing dataset presented is collected from 20 healthy individuals at the University of Canterbury using a device to simulate the pressure and flow profiles of obstructive pulmonary disease. Specifically, the expiratory non-linear resistance, which generates the characteristic expiratory pressure-flow loop lobe seen in obstructive disease. Ethical consent for the trial was granted by the University of Canterbury Human Research Ethics Committee (Ref: HREC 2022/26/LR). Data was collected using an open-source data collection device connected to a Fisher and Paykel Healthcare SleepStyle SPSCAA CPAP. The trial was conducted at CPAP PEEP levels of 4 and 8 cmH2O, as well as at ZEEP (0 cmH2O) with no CPAP attached. The simulation device was a modular device connected to the expiratory pathway, consisting of a free volume diversion and fixed high resistance outlet. Three simulation levels were selected for testing, achieved by changing the size of the elastic free volume. The intended use of this dataset is for the initial validation and development of respiratory pulmonary mechanics models, using data collected from healthy people with simulated disease prior to clinical testing.

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.

Development of a non-invasive ventilator for emergency and beyond

The SARS-CoV-2 pandemic led to the development and implementation of emergency ventilators owing to the shortage of ventilators globally. Using invasive ventilators for patient intubation has medical experts concerned about increasing mortality. Early intervention with oxygen and respiratory therapy reduces the need for intubation, increases survival rates, and reduces the stress of critical care ventilators in hospitals. This study explores the capabilities of an easy-to-build and accessible non-invasive ventilator during an emergency and the practical implementation of the ventilator beyond the scope of the emergency. The proposed system consists of a high-pressure turbine integrated with a microcontroller and pressure and flow sensors assembled in a portable design. The non-invasive pressure support system is tested with a single-chamber high-precision lung simulator capable of simulating multiple lung diseases. The system is operated in a spontaneous pressure support mode as a Bi-level Ventilator for varying degrees of pressure level and lung conditions. The proposed study implements two most commonly adapted non-invasive patient circuits, i.e., single passive limb leak circuit and single limb active circuit. Both circuits are tested with and without leakage compensation. Two clinically accepted ventilation modes, i.e., pressure support and volume-assured pressure support ventilation, are presented. The results demonstrate the feasibility of using this type of device for non-invasive respiratory support and highlight the need for further testing to assess its safety and effectiveness in various clinical settings.

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.

An identifiable model of lung mechanics to diagnose and monitor COPD

BACKGROUND

Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV₁)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD.

METHODS

Healthy and COPD pressure (P)[cmH₂O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R_1,insp) and linear (R_1,exp)/nonlinear (R₂Φ) expiratory resistance with disease severity and over the course of expiration.

RESULTS

R_1,insp increases from 2.1 to 7.3 cmH₂O/L/s, R_1,exp increases from 2.4 to 10.0 cmH₂O/L/s with COPD severity. Nonlinear R₂Φ increases (mean R₂Φ: 2.5 cmH₂O/L/s (healthy) to 24.4 cmH₂O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases.

CONCLUSION

Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.

Predicting mechanically ventilated patients future respiratory system elastance - A stochastic modelling approach

BACKGROUND AND OBJECTIVE

Respiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment.

METHODS

Two stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (E_rs) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves.

RESULTS

Clinical validation shows all three models captured more than 98% (median) of future E_rs data within the 5th - 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < E_rs (cmH₂O/L) < 85, suggesting similar predictive capabilities within this clinically relevant E_rs range.

CONCLUSION

The new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.

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.

CPAP pressure and flow data at 2 positive pressure levels and multiple controlled breathing rates from a trial of 30 adults

OBJECTIVES

A unique dataset of airway flow/pressure from healthy subjects on Continuous Positive Airway Pressure (CPAP) ventilation was collected. This data can be used to develop or validate models of pulmonary mechanics, and/or to develop methods to identify patient-specific parameters which cannot be measured non-invasively, during CPAP therapy. These models and values, particularly if available breath-to-breath in real-time, could assist clinicians in the prescription or optimisation of CPAP therapy, including optimising PEEP settings.

DATA DESCRIPTION

Data was obtained from 30 subjects for model-based identification of patient-specific lung mechanics using a specially designed venturi sensor system comprising an array of differential and gauge pressure sensors. Relevant medical information was collected using a questionnaire, including: sex; age; weight; height; smoking history; and history of asthma. Subjects were tasked with breathing at five different rates (including passive), matched to an online pacing sound and video, at two different levels of PEEP (4 and 7 cmH₂O) for between 50 and 180 s. Each data set comprises ~ 17 breaths of data, including rest periods between breathing rates and CPAP levels.