METHOD: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows.
RESULTS: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87-25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77-8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag.
CONCLUSION: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.
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.
METHODS: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3,566 and 2,447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume.
RESULTS: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 - 1.83] cmH2O and 0.55 [0.19 - 1.20] mL/kg were obtained.
CONCLUSIONS: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.
METHODS: A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.
RESULTS: From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.
CONCLUSIONS: Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
DESIGN: This pilot study over April 2016 to September 2019 adopts a before-and-after comparison design of a lung-protective mechanical ventilation protocol. All admissions to the PICU were screened daily for fulfillment of the Pediatric Acute Lung Injury Consensus Conference criteria and included.
SETTING: Multidisciplinary PICU.
PATIENTS: Patients with pediatric acute respiratory distress syndrome.
INTERVENTIONS: Lung-protective mechanical ventilation protocol with elements on peak pressures, tidal volumes, end-expiratory pressure to FIO2 combinations, permissive hypercapnia, and permissive hypoxemia.
MEASUREMENTS AND MAIN RESULTS: Ventilator and blood gas data were collected for the first 7 days of pediatric acute respiratory distress syndrome and compared between the protocol (n = 63) and nonprotocol groups (n = 69). After implementation of the protocol, median tidal volume (6.4 mL/kg [5.4-7.8 mL/kg] vs 6.0 mL/kg [4.8-7.3 mL/kg]; p = 0.005), PaO2 (78.1 mm Hg [67.0-94.6 mm Hg] vs 74.5 mm Hg [59.2-91.1 mm Hg]; p = 0.001), and oxygen saturation (97% [95-99%] vs 96% [94-98%]; p = 0.007) were lower, and end-expiratory pressure (8 cm H2O [7-9 cm H2O] vs 8 cm H2O [8-10 cm H2O]; p = 0.002] and PaCO2 (44.9 mm Hg [38.8-53.1 mm Hg] vs 46.4 mm Hg [39.4-56.7 mm Hg]; p = 0.033) were higher, in keeping with lung protective measures. There was no difference in mortality (10/63 [15.9%] vs 18/69 [26.1%]; p = 0.152), ventilator-free days (16.0 [2.0-23.0] vs 19.0 [0.0-23.0]; p = 0.697), and PICU-free days (13.0 [0.0-21.0] vs 16.0 [0.0-22.0]; p = 0.233) between the protocol and nonprotocol groups. After adjusting for severity of illness, organ dysfunction and oxygenation index, the lung-protective mechanical ventilation protocol was associated with decreased mortality (adjusted hazard ratio, 0.37; 95% CI, 0.16-0.88).
CONCLUSIONS: In pediatric acute respiratory distress syndrome, a lung-protective mechanical ventilation protocol improved adherence to lung-protective mechanical ventilation strategies and potentially mortality.
METHODS: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn).
RESULTS AND DISCUSSION: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%.
CONCLUSION: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.
Background: Mobilizing ICU patients remains a challenge, despite its safety, feasibility and positive short-term outcomes.
Design: A cross-sectional point prevalence study.
Methods: All patients who were eligible and admitted to the adult ICUs during March 2018 were recruited. Data were analysed by using the Statistical Package for Social Sciences version 24 for Windows.
Results: The prevalence of EM practice was 65.6%. The most frequently reported avoidable and unavoidable factors inhibit mobility were deep sedation and vasopressor infusion, respectively. Level II of activity was the most common level of activity performed in ICU patients. The invasive ventilated patient had 12.53 the odds to stay in bed as compared to non-invasive ventilated patient. An average adherence rate of EM protocol was 52.5%.