D. Redmond

The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.

BackgroundReal-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment.ImplementationCURE Soft is a desktop application developed in JAVA. It has two modes of operation, 1) Online real-time monitoring decision support and, 2) Offline for user education purposes, auditing, or reviewing patient care. The CURE Soft has been tested in mechanically ventilated patients with respiratory failure. The clinical protocol, software testing and use of the data were approved by the New Zealand Southern Regional Ethics Committee.Results and discussionUsing CURE Soft, patient’s respiratory mechanics response to treatment and clinical protocol were monitored. Results showed that the patient’s respiratory elastance (Stiffness) changed with the use of muscle relaxants, and responded differently to ventilator settings. This information can be used to guide mechanical ventilation therapy and titrate optimal ventilator PEEP.ConclusionCURE Soft enables real-time calculation of model-based respiratory mechanics for mechanically ventilated patients. Results showed that the system is able to provide detailed, previously unavailable information on patient-specific respiratory mechanics and response to therapy in real-time. The additional insight available to clinicians provides the potential for improved decision-making, and thus improved patient care and outcomes.

Pressure reconstruction by eliminating the demand effect of spontaneous respiration (PREDATOR) method for assessing respiratory mechanics of reverse-triggered breathing cycles

Purpose: Patient-specific respiratory mechanics can be used to guide mechanical ventilation therapy. However, even in controlled ventilation modes, underlying respiratory mechanics can be masked by spontaneous breathing efforts. The aim of this study is to accurately assess respiratory mechanics for breathing cycles affected by these spontaneous breathing efforts. Methods: A pressure reconstruction by eliminating the demand effect of spontaneous respiration (PREDATOR) method is used to reconstruct pressure profiles to assess underlying respiratory mechanics (breath specific elastance and resistance). The method is tested on both simulated and clinical data comprising n=264 breaths. Results: Using simulated data, the standard deviation of identified elastance (σ=0.168) and resistance (σ=0.053) are both significantly smaller using PREDATOR (σ=1.009 and σ=0.348) (p<;0.05 for both) compared to standard methods. Variability in identified elastance is significantly decreased in clinical data tested (p<;0.05). Median [IQR] of the robust coefficient of variation in elastance for each pressure level using PREDATOR is 0.0518 [0.0278-0.0668] compared to 0.1211 [0.0854-0.1783] of the standard algorithm. Conclusions: The PREDATOR method provides a more accurate respiratory mechanics identification in the presence of spontaneous breathing. It provides the opportunity to use respiratory mechanics to guide mechanical ventilation therapy.

Clinical Utilisation of Respiratory Elastance (CURE): Pilot Trials for the Optimisation of Mechanical Ventilation Settings for the Critically Ill

Abstract Current practice in determining Mechanical Ventilation (MV) settings is highly variable with little consensus, forcing clinicians to rely on general approaches and clinical intuition. The Clinical Utilisation of Respiratory Elastance (CURE) system was developed to aid clinical determination of important MV settings by providing real-time patient-specific lung condition information at the patient bedside. The pilot clinical trials to investigate the performance and efficacy of this system are currently being carried out in the Christchurch Hospital ICU, New Zealand. This paper presents the CURE clinical trial protocol and its initial findings from the two patients recruited to date. In particular, this paper focuses on CUREs ability to determine patient-specific responses in real time to PEEP changes and recruitment manoeuvres (RM). The results from this study demonstrate the potential for CURE Soft to improve the reliability and ease with which clinicians make decisions about MV settings in the ICU.

Real-Time Breath-to-Breath Asynchrony Event Detection using Time-Varying Respiratory Elastance Model

Abstract Asynchronous events (AE) occur during mechanical ventilation (MV) therapy when the patients breathing is not synchronised with the ventilator support. Frequent AE indicates sub-optimal ventilation therapy and may lead to further complications. Asynchrony Index (AI) gives the percentage of AEs as a percentage of total breaths, but is only assessed via manual scrutiny. Thus, there is a need to automate AE detection in real-time. A model-based approach using time-varying elastance to detect AEs is developed and retrospectively assessed in MV patients. Data from 14 mechanically ventilated respiratory failure patients, enrolled in an observational study in Christchurch Hospital, New Zealand were used to investigate the performance of the method. Patient data is sorted according to the ventilation mode used, and AI is calculated for each episode separately. The model-based approach accurately identifies AEs, and shown not to give false positive readings when compared to manual detection (gold standard). None of the ventilation modes give significantly different AI levels (P > 0.05). AI decreases when ventilation mode changes and increases overall time indicate worsen patient-ventilator interaction. The model-based method is able to successfully and accurately calculate AI. Real time use of this metric will enable patients with sub-optimal ventilator settings to be automatically identified for the first time and the settings adjusted as necessary, improving the efficacy of mechanical ventilation therapy, and providing a quantified metric to help guide MV care.

Effects of Different Models and Different Respiratory Manoeuvres in Respiratory Mechanics Estimation

The aim of mechanical ventilation (MV) is to provide sufficient breathing support for patients with respiratory failure in the intensive care unit (ICU). However, applying inappropriate ventilation parameters can result in ventilator induced lung injury. To prevent this, respiratory mechanics such as elastance and resistance can be estimated at the bedside to help guide MV parameters using respiratory mechanics models. Different models or methods provide different information and each have their own advantages and disadvantages. In this study, respiratory mechanics of 9 respiratory failure patients were estimated using the simple first order model (FOM) and viscoelastic model (VEM). These patients undergo different respiratory manoeuvres and their estimated respiratory mechanics using these models are studied and compared with a standard clinical method in estimating respiratory mechanics. The results showed that both models were able to capture patient-specific mechanics and responses. The VEM was able to provide higher correlation to the standard clinical method compared to FOM.

A Modular Patient Simulator for Evaluation of Decision Support Algorithms in Mechanically Ventilated Patients

Mechanical ventilation is a life-saving intervention, which, despite being routinely used in ICUs, poses the risk of causing further damage to the lung tissue if the ventilator is set inappropriately. Medical decision support systems may help in optimizing ventilator settings according to therapy goals given by the clinician. Before using the decision support algorithms in commercially available systems, extensive tests are necessary to ensure patient safety and correct decision making. Model-based patient simulators can assist in evaluating such decision support systems by creating different clinical scenarios. We propose a new Java based patient simulator that implements various models of respiratory mechanics, gas exchange and cardiovascular dynamics to form a complex patient model. The implemented models interact with one another to allow simulation of the ventilators influence on various physiological processes. Model simulations are running in real-time and simulation results can be extracted via multiple interfaces. Each of the implemented models has been validated to exhibit physiologically correct behavior. Results of the combined model system also showed to be physiologically plausible.

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort.

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

Iterative Interpolative Pressure Reconstruction for Improved Respiratory Mechanics Estimation During Asynchronous Volume Controlled Ventilation

Asynchronous events (AEs) during mechanical ventilation (MV) breathing support can lead to poor respiratory mechanics estimation, as the patient’s attempts to breath affects the measured airway pressure and flow. An algorithm that allows improved model-based estimation of respiratory system elastance, E rs during asynchronous volume-controlled MV was developed. This method reconstructs a pseudo airway pressure waveform for each breath, that is similar to a breath that was unaffected by asynchronous efforts. The reconstructed waveforms can be used to estimate true respiratory system mechanics. To test the proposed algorithm, 10 retrospective airway pressure and flow datasets were obtained from 6 MV patients. Each dataset contains 475-500 breaths. Of the 9/10 datasets which contained AEs, 8 experienced a decrease in E rs mean absolute deviation (MAD) and the 5th-95th range (Range90) after pressure reconstruction. The median [maximum (max), minimum (min)] decrease in Range90 divided by median elastance, was 51.3% (67.4%, -16.7%). Additionally, the median elastance for reconstructed breaths in these datasets moved closer to the true, non-asynchronous, elastance value. The median elastance change was 48.7% closer towards the true value, with a maximum shift of 93.4%. The one dataset which did not experience an improvement was found to have a varying pressure amplitude indicative of external factors affecting the MV treatment, rather than a deficiency in the pressure reconstruction. The algorithm demonstrates the ability to consistently enhance elastance estimation in MV patients.

Time-Varying Respiratory Elastance for Spontaneously Breathing Patients

Abstract Respiratory mechanics models can be used to optimise patient-specific mechanical ventilation (MV), but are limited to fully sedated MV patients who are not spontaneously breathing. This research presents a non-invasive model-based method to determine respiratory mechanics of spontaneously breathing MV patients. Patient-specific respiratory mechanics of 22 spontaneously breathing patients are described using a single compartment lung model with time-varying elastance ( E drs ). The normalised E drs trajectories and the area under the curves ( AUCE drs ) are calculated using clinical data from 22 patients ventilated using pressure support (PS) and neurally adjusted ventilatory assist (NAVA). E drs trajectories are also compared between ventilation modes. E drs for PS ventilation were significantly higher compared to NAVA (p E drs trajectories were more variable during NAVA than PS (p AUCE drs > 25 cmH 2 Os/l. The AUCE drs is a surrogate of elastance, and thus can be used as a respiratory failure severity indicator. This non-invasive model-based approach captures unique dynamic respiratory mechanics for spontaneously breathing patients during PS and NAVA. The model is fully general and is applicable to both fully controlled and partially assisted MV modes, with the resulting potential to standardise treatment for all MV patients.

Effective sample size estimation for a mechanical ventilation trial through Monte-Carlo simulation: Length of mechanical ventilation and Ventilator Free Days

Randomised control trials have sought to seek to improve mechanical ventilation treatment. However, few trials to date have shown clinical significance. It is hypothesised that aside from effective treatment, the outcome metrics and sample sizes of the trial also affect the significance, and thus impact trial design. In this study, a Monte-Carlo simulation method was developed and used to investigate several outcome metrics of ventilation treatment, including 1) length of mechanical ventilation (LoMV); 2) Ventilator Free Days (VFD); and 3) LoMV-28, a combination of the other metrics. As these metrics have highly skewed distributions, it also investigated the impact of imposing clinically relevant exclusion criteria on study power to enable better design for significance. Data from invasively ventilated patients from a single intensive care unit were used in this analysis to demonstrate the method. Use of LoMV as an outcome metric required 160 patients/arm to reach 80% power with a clinically expected intervention difference of 25% LoMV if clinically relevant exclusion criteria were applied to the cohort, but 400 patients/arm if they were not. However, only 130 patients/arm would be required for the same statistical significance at the same intervention difference if VFD was used. A Monte-Carlo simulation approach using local cohort data combined with objective patient selection criteria can yield better design of ventilation studies to desired power and significance, with fewer patients per arm than traditional trial design methods, which in turn reduces patient risk. Outcome metrics, such as VFD, should be used when a difference in mortality is also expected between the two cohorts. Finally, the non-parametric approach taken is readily generalisable to a range of trial types where outcome data is similarly skewed.