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

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 (Ers) 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 Ers 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 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers 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

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

Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders

10.1016/j.cmpb.2021.106601Computer Methods and Programs in Biomedicine215106601-10660

Stochasticity of the respiratory mechanics during mechanical ventilation treatment

Stochastic models have been used to predict dynamic intra-patient respiratory system elastance (Ers) in mechanically ventilated (MV) patients. However, existing Ers stochastic models were developed using small cohorts, potentially showing bias and overestimation during prediction. Thus, there is a need to improve the stochastic model's performance. This research investigates the effect of the kernel density estimator (KDE) parameter tuned with a constant, c on the performance of a 30-min interval Ers stochastic model. Thirteen variations of a stochastic model were developed using varying KDE parameters. Model bias and overestimation were evaluated by the percentage of actual data captured within the 25th – 75th and 5th – 95th percentile lines (Pass50 and Pass90). The optimum range of c was chosen to tune the KDE parameter and minimise the temporal variations of model-predicted 25th – 75th and 5th – 95th percentile values of Ers (ΔRange50 and ΔRange90) in an independent retrospective clinical cohort of 14 patients. In this cohort, the values of ΔRange50 and ΔRange90 exhibit a converging behaviour, resulting in a cohort-optimised value of c = 0.4. Compared to c = 1.0 (benchmark study model), c = 0.4 significantly reduces model overestimation by up to 25.08% in the 25th – 75th percentile values of Ers. Overall, c = 0.3–1.0 presents as a generalised range of optimum c values, considering the trade-off between data overfitting and model overestimation. Optimisation of the KDE parameter enables more accurate and robust Ers stochastic models in cases of limited training data availability

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, timevarying 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

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 (res- piratory 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