Mechanical ventilation patients may breathe spontaneously during ventilator supported breaths, altering airway pressure waveforms and hindering identification of true, underlying respiratory mechanics. This study aims to assess and identify respiratory mechanics for breathing cycles masked by spontaneous breathing (SB) effort using a pressure reconstruction method. The performance of the method is compared to parameters identified using a single-compartment model. Data from two patients (N=6305 breaths) experiencing SB and subsequent periods of muscle paralysis without SB were used for analysis. Patients are their own control and are assessed by breath-to-breath variation using coefficient of variation (CV) of respiratory elastance. Pressure reconstruction successfully estimates more consistent respiratory mechanics during SB by reducing CV up to 78% compared to conventional identification (p<0.05). Pressure reconstruction is comparable (p>0.05) to conventional identification during paralysis, and generally performs better as paralysis weakens (p<0.05). Pressure reconstruction provides less-affected pressure waveforms, ameliorating the effect of SB, resulting in more accurate respiratory mechanics identification.

Asynchronous Events (AEs) during mechanical ventilation (MV) result in increased work of breathing and potential poor patient outcomes. Thus, it is important to automate AE detection. In this study, an AE detection method, Automated Logging of Inspiratory and Expiratory Non-synchronized breathing (ALIEN) was developed and compared between standard manual detection in 11 MV patients. A total of 5701 breaths were analyzed (median [IQR]: 500 [469-573] per patient). The Asynchrony Index (AI) was 51% [28-78]%. The AE detection yielded sensitivity of 90.3% and specificity of 88.3%. Automated AE detection methods can potentially provide clinicians with real-time information on patient-ventilator interaction.

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.

Background: Real-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.Implementation: CURE 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 discussion: Using 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.Conclusion: CURE 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.