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

Model-based Method in Assessing Breathing Effort in Mechanically Ventilated Patients in Malaysian ICU Hospital

Patients with Acute Respiratory Distress Syndrome (ARDS) required mechanical ventilation (MV) for breathing support. However, some MV patients encountered spontaneous breathing (SB) efforts while fully sedated which can obscure the true underlying respiratory mechanics of these patients. Thus, pressure reconstruction method is required to reconstruct the missing pressure and calculate the breathing effort that produced by the patients without additional clinical protocols or invasive procedure. In this paper, results of spontaneous breathing effort in Malaysian critically-ill patients adopting the developed pressure reconstruction model are presented. By using the pressure reconstruction model, the SB affected pressure waveform is reconstructed to approximate true respiratory mechanics and quantifies the SB effort. The SB breathing efforts were computed and compared with the results from Christchurch Hospital, New Zealand. The substitute measure of SB effort can be indicated from the difference between the reconstructed and unreconstructed pressure. Results shows that all patients from both cohorts exhibited SB effort with the highest SB effort at 11.48% for Malaysian patient. Overall, the well-developed non-invasive pressure reconstruction method is able to measure the SB effort produced by Malayisan MV patients that help the clinicians in selecting the optimal MV setting. This first non-invasive guidance in selecting the optimal setting of MV in Malaysia is potentially reduced the ICU cost and improve the MV management in Malaysian hospital

Patient-ventilator interaction using autoencoder derived magnitude of asynchrony breathing

The occurrence of asynchronous breathing (AB) is prevalent during mechanical ventilation (MV) treatment. Despite studies being carried out to elucidate the impact of AB on MV patients, the asynchrony index, a metric to describe the patient-ventilator interaction, may not be sufficient to quantify the severity of each AB fully in MV patients. This research investigates the feasibility of using a machine learning-derived metric, the ventilator interaction index, to describe a patient’s interaction with a mechanical ventilator. VI is derived using the magnitude of a breath’s asynchrony to measure how well patient is interacting with the ventilator. 1,188 hours of hourly and for 13 MV patients were computed using a convolution neural network and an autoencoder. Pearson’s correlation analysis between patients’ and versus their levels of partial pressure oxygen (PaO2) and partial pressure of carbon dioxide (PaCO2) was carried out. In this patient cohort, the patients’ median is 38.4% [Interquartile range (IQR): 25.9-48.8], and the median is 86.0% [IQR: 76.5-91.7]. Results show that high AI does not necessarily predispose to low. This difference suggests that every AB poses a different magnitude of asynchrony that may affect patient’s PaO2 and PaCO2. Quantifying hourly along with during MV could be beneficial in explicating the aetiology of AB

A Nonlinear Hysteretic Model for Automated Prediction of Lung Mechanics during Mechanical Ventilation

Mechanical ventilation (MV) is core intensive care unit (ICU) therapy during the Covid-19 pandemic. Optimising MV care to a specific patient with respiratory failure is difficult due to inter- and intra- patient variability in lung mechanics and condition. The ability to accurately predict patient-specific lung response to a change in MV settings would enable semi-automated care and significantly improve the efficiency of MV monitoring and care. It has particular emphasis when considering MV care required to treat Covid-19 patients, who require longer MV care, where patient-specific care can reduce the time on MV required. This study develops a nonlinear smooth hysteresis loop model (HLM) able to capture the essential lung dynamics in a patient-specific fashion from measured ventilator data, particularly for changes of compliance and infection points of the pressure-volume loop. The automated (no human input) hysteresis loop analysis (HLA) method is applied to identify HLM model parameters, enabling automated digital cloning to create a virtual patient model to accurately predict lung response at a specified positive end expiratory pressure (PEEP) level, as well as in response to the changes of PEEP. The performance of this automated digital cloning approach is assessed using clinical data from 4 patients and 8 recruitment maneuver (RM) arms. Validation results show the HLM-based hysteresis loops identified using HLA match clinical pressure volume loops very well with root-mean-square (RMS) errors less than 2% for all 8 data sets over 4 patients, validating the accuracy of the developed HLM in capturing the essential lung physiology and respiratory behaviours at different patient conditions. More importantly, the patient-specific digital clones at lower PEEP levels accurately predict lung response at higher PEEP levels with predicted peak inspiratory pressure (PIP) errors less than 2% in average. In addition, the resulted additional lung volume Vfrc obtained with PEEP changes are predicted with average absolute difference of 0.025L. The overall results validate the versatility and potential of the developed HLM for delineating changes of nonlinear lung dynamics, and its capability to create a predictive virtual patient with use of HLA for future treatment personalization and optimisation in MV therapy

Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient.

peer reviewedBACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care