Asynchrony Index Research Articles

Induction of subject-ventilator asynchrony by variation of respiratory parameters in a lung injury model in pigs

BackgroundSubject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated.MethodsAlterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%.ResultsIE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours.ConclusionsThe method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony.

Ventilator performances for non-invasive ventilation: a bench study

IntroductionA wide range of recent ventilators, dedicated or not, is available for non-invasive ventilation (NIV) in respiratory or intensive care units (ICU). We conducted a bench study to compare their...

Exploring variable observational time windows for patient–ventilator asynchrony during mechanical ventilation treatment

Background and Objective:Patient–ventilator asynchrony (PVA) is prevalent in mechanical ventilation (MV) for critically ill patients and has been associated with adverse patient outcomes. However, studies investigating the associations between PVA and patient outcomes employ differing time windows for PVA evaluation. In this study, machine learning methods are used to quantify the prevalence and magnitude of asynchrony at different time windows, as well as its temporal trends. The study aims to identify the optimal time window for assessing the temporal changes in the asynchrony index (AI) and magnitude of asynchrony (Masyn,avg). Methods:This study uses Convolutional Neural Networks (CNN) and Convolutional Autoencoders (CAE) to detect incidences of PVA and quantify its severity in 30 MV respiratory failure patients with 2722 h of respiratory data. The frequency of PVA and the breath-average magnitude were determined over different time periods, t, where t=0.5, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 45, 60 min and throughout MV. The AI for the patients was determined using the CNN model. Given an asynchronous breath, the CAEs were used to reconstruct asynchrony-free waveforms. The Masyn,avg was quantified as the difference between the two waveforms. The change in AI (ΔAI) and the change in Masyn,avg (ΔMasyn,avg) for all time windows, t were also calculated for each patient. Results:The median [interquartile range] overall AI and Masyn,avg for the patient cohort are 24.8 [12.9–46.1]% and 37.2 [33.4–45.3]% respectively. Analysis of the patient cohort also shows significant intra-patient variability in AI and Masyn,avg, while the inter-patient variation in AI is greater as compared to Masyn,avg. The cohort mean ΔAI and ΔMasyn,avg exhibit a converging trend with a minima at t=5 min and with values of 5.32 ± 2.37% and 2.80 ± 1.03%, respectively. A time window of t=5 min was preferred for AI and Masyn,avg evaluation as it can capture the granular changes in asynchrony while also being representative of longer temporal trends, thus preventing excessive variations in patient AI and Masyn,avg. Conclusion:Overall, this study provides new insight into both the short- and long-term trends of PVA in MV patients. By understanding these patterns, healthcare providers can enhance the monitoring of MV, leading to more informed and timely intervention. Ultimately, this could lead to improved patient care and outcomes.

NIV-NAVA versus non-invasive respiratory support in preterm neonates: a meta-analysis of randomized controlled trials.

To analyze the clinical and physiological outcomes of NIV-NAVA in preterm infants compared with other non-invasive respiratory support. We conducted a meta-analysis of RCTs and randomized crossover studies comparing NIV-NAVA to other non-invasive strategies in preterm neonates. NIV-NAVA was superior to other non-invasive support in maximum EAdi (MD - 0.66 µV; 95% CI - 1.17 to -0.15; p = 0.01), asynchrony index (MD - 49.8%; 95% CI - 63.1 to -36.5; p < 0.01), and peak inspiratory pressure (MD - 2.2 cmH2O; 95% CI - 2.7 to -1.7; p < 0.01). However, there were no significant differences in the incidences of intubation (RR 0.91; 95% CI 0.56-1.48; p = 0.71), reintubation (RR 0.72; 95% CI 0.45-1.16; p = 0.18), or bronchopulmonary dysplasia (RR 0.77; 95% CI 0.37-1.60; p = 0.48). NIV-NAVA was associated with improvements in maximum Edi, asynchrony index, and peak inspiratory pressure relative to other non-invasive respiratory strategies, without significant differences in clinical outcomes between groups.

Automated characterization of patient–ventilator interaction using surface electromyography

BackgroundCharacterizing patient–ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient.MethodsIn this study, we recorded surface electromyography (sEMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{sEMG}$$\\end{document}) signals from the diaphragm and intercostal muscles and esophageal pressure (Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document}) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document}, blinded toward each other, the sEMG\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{sEMG}$$\\end{document} signals, and the algorithmic results. We also classified patient–ventilator interaction and calculated asynchrony indices with manually detected inspirations in Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document} as a reference for automated asynchrony classification and asynchrony index calculation.ResultsSpontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document} demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document}. The average delay of automatically detected inspiratory onset to the Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document} reference was -\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of 0.06±0.13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.06\\pm 0.13$$\\end{document} to the Pes\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{\ extrm{es}}$$\\end{document}-based reference.ConclusionsOur study demonstrates the feasibility of automating the quantification of patient–ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient–ventilator interaction.

Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks.

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. An observational study was conducted in 23 babies randomly selected from 170 neonates who were ventilated with SIPPV-VG, SIMV-VG or PSV-VG mode for at least 12 h. 500 breaths were randomly selected and manually annotated from each recording to train convolutional neural network (CNN) models for PVI classification. The average asynchrony index (AI) over all recordings was 52.5%. The most frequently occurring PVIs included expiratory work (median: 28.4%, interquartile range: 23.2-40.2%), late cycling (7.6%, 2.8-10.2%), failed triggering (4.6%, 1.2-6.2%) and late triggering (4.4%, 2.8-7.4%). Approximately 25% of breaths with a PVI had two or more PVIs occurring simultaneously. Binary CNN classifiers were developed for PVIs affecting ≥1% of all breaths (n = 7) and they achieved F1 scores of >0.9 on the test set except for early triggering where it was 0.809. PVIs occur frequently in neonates undergoing conventional mechanical ventilation with a significant proportion of breaths containing multiple PVIs. We have developed computational models for seven different PVIs to facilitate automated detection and further evaluation of their clinical significance in neonates. The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. By adapting a recent taxonomy of PVI definitions in adults, we have manually annotated neonatal ventilator waveforms to determine prevalence and co-occurrence of neonatal PVIs. We have also developed binary deep learning classifiers for common PVIs to facilitate their automatic detection and quantification.

Neurally Adjusted Ventilatory Assist Versus Pressure Support Ventilation: A Comprehensive Review.

Mechanical ventilation serves as crucial life support for critically ill patients. Although it is life-saving prolonged ventilation carries risks and complications like barotrauma, Ventilator-associated pneumonia, sepsis, and many others. Optimizing patient-ventilator interactions and facilitating early weaning is necessary for improved intensive care unit (ICU) outcomes. Traditionally Pressure support ventilation (PSV) mode is widely used for weaning patients who are intubated and mechanically ventilated. Neurally adjusted ventilatory assist (NAVA) mode of the ventilator is an emerging ventilator mode that delivers pressure depending on the patient's respiratory drive, which in turn prevents over-inflation and improves the patient's ventilator interactions. Our article revises and compares the effectiveness of NAVA compared to PSV ventilation under different contexts. Overall we conclude that NAVA level of ventilation can be safely administered in a patient with acute respiratory failure, provided diaphragmatic paralysis is not considered. NAVA improves asynchrony index, wean-off time, and sleep quality and is associated with increased ventilator-free days. These results are based on small-scale studies with low power, and further studies are warranted in large-scale cohorts with more diverse populations to confirm these results.

Are all ventilators for NIV performing the same? A bench analysis

Global pandemic due to COVID-19 has increased the interest for ventilators´ use worldwide. New devices have been developed and older ones have undergone a renewed interest, but we lack robust evidence about performance of each ventilator to match appropriate device to a given patient and care environment. The aim of this bench study was to investigate the performance of six devices for noninvasive ventilation, and to compare them in terms of volume delivered, trigger response, pressurization capacity and synchronization in volume assisted controlled and pressure support ventilation. All ventilators were tested under thirty-six experimental conditions by using the lung model ASL5000® (IngMar Medical, Pittsburgh, PA). Two leak levels, two muscle inspiratory efforts and three mechanical patterns were combined for simulation. Trigger function was assessed by measurement of trigger-delay time. Pressurization capacity was evaluated as area under the pressure–time curve over the first 500 ms after inspiratory effort onset. Synchronization was evaluated by the asynchrony index and by incidence and type of asynchronies in each condition. All ventilators showed a good performance, even if pressurization capacity was worse than expected. Leak level did not affect their function. Differences were found during low muscle effort and obstructive pattern. In general, Philips Trilogy Evo/EV300 and Hamilton C3 showed the best results. NIV devices successfully compensate air leaks but still underperform with low muscle effort and obstructive lungs. Clinicians´ must have a clear understanding of the goals of NIV both for devices´ choice and set main parameters to achieve therapy success.

Lumped variable representing the integrative effects of climate and underlying surface system: Interpreting Budyko model parameter from earth system science perspective

Parametric Budyko models express the ratio of annual actual evapotranspiration (ET) to annual precipitation (P) as a function of dryness index, i.e., annual potential evapotranspiration (ET0)/P (denoted as DI) and model parameter at a catchment. Generally the model parameter is assumed to reflect factors besides DI, especially the underlying surface features. However, recent studies have revealed its relations with DI, which forces us to further examine its connotation. We revisited the derivation of three parametric Budyko equations and found that model parameter was essentially a lumped variable representing the integrative effects of climate and underlying surface system. Then taking 15 typical catchments in the Chinese Loess Plateau as the study area, we explored the controls of Budyko-Fu model parameter (w) and their differences in temporal (between-years) and spatial (between-catchments) processes. As expected, w was affected by both climate and underlying surface factors. Specifically, DI, climate seasonality and asynchrony index (SAI) and Normalized Difference Vegetation Index (NDVI) influenced w in both temporal and spatial processes; besides, catchment relief ratio (CR) and relative infiltration capacity (ks/ir) also affected w in spatial process. SAI was strongly correlated with DI and its close relationship with w essentially conveyed DI's influence. Considering the inherent connection between DI and the inverse w, DI was not included in the empirical formula for w. SAI and NDVI remained independent in temporal process and the empirical formula for w with these two factors modeled annual ET well (MAE = 15.03 mm). In spatial process, SAI, NDVI, CR, and ks/ir were interconnected and by using any one the spatial variation of ET was captured adequately (MAE = 11.16, 11.74, 11.27, and 10.43 mm, respectively). Our results demonstrate theoretically and empirically the importance of viewing Budyko model parameter from earth system science perspective and focusing on the differences of its controls in spatiotemporal processes.

Establishment and Application of a Patient-Ventilator Asynchrony Remote Network Platform for ICU Mechanical Ventilation: A Retrospective Study

In the process of mechanical ventilation, the problem of patient-ventilator asynchrony (PVA) is faced. This study proposes a self-developed remote mechanical ventilation visualization network system to solve the PVA problem. The algorithm model proposed in this study builds a remote network platform and achieves good results in the identification of ineffective triggering and double triggering abnormalities in mechanical ventilation. The algorithm has a sensitivity recognition rate of 79.89% and a specificity of 94.37%. The sensitivity recognition rate of the trigger anomaly algorithm was as high as 67.17%, and the specificity was 99.92%. The asynchrony index was defined to monitor the patient's PVA. The system analyzes real-time transmission of respiratory data, identifies double triggering, ineffective triggering, and other anomalies through the constructed algorithm model, and outputs abnormal alarms, data analysis reports, and data visualizations to assist or guide physicians in handling abnormalities, which is expected to improve patients' breathing conditions and prognosis.

A randomized controlled trial comparing non-invasive ventilation delivered using neurally adjusted ventilator assist (NAVA) or adaptive support ventilation (ASV) in patients with acute exacerbation of chronic obstructive pulmonary disease

PurposeNo study has compared neurally adjusted ventilator assist (NAVA) with adaptive support ventilation (ASV) during non-invasive ventilation (NIV) in subjects with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Materials and methodsIn this randomized controlled trial, we compared NAVA-NIV with ASV-NIV for delivering NIV in consecutive subjects with AECOPD. The primary outcome was NIV failure rate (invasive mechanical ventilation). The key secondary outcomes were number of NIV manipulations, asynchrony index, and 90-day mortality. ResultsWe enrolled 76 subjects (NAVA-NIV, n = 36, ASV-NIV, n = 40; 74% males) with a mean ± SD age of 61.4 ± 8.2 years. We found no difference in NIV failure rates between the two arms (NAVA-NIV vs. ASV-NIV; 8/36 [22.2%] vs. 8/40 [20%]; p = 0.83). The median physician manipulations for NIV were significantly less in the ASV-NIV arm than in the NAVA-NIV arm (2 [0.8–4] vs. 3 [2–5]; p= 0.014) during the initial 24-h. We found no difference in median asynchrony index (NAVA-NIV vs. ASV-NIV, 16.6% vs. 16.4%, p = 0.5) and 90-day mortality (22.2% vs. 17.5%, p = 0.67). ConclusionThe use of NAVA-NIV was not superior to ASV-NIV in reducing NIV failure rates in AECOPD. Both NAVA-NIV and ASV-NIV had similar asynchrony index and 90-day mortality. Trial registrywww.clinicaltrials.gov (NCT04414891).

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 (AI), 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 (VI), 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 a patient is interacting with the ventilator. 1,188 hours of hourly AI and VI for 13 MV patients were computed using a convolution neural network and an autoencoder. Pearson's correlation analysis between patients’ AI and VI 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 AI is 38.4% [Interquartile range (IQR): 25.9-48.8], and the median VI is 86.0% [IQR: 76.5-91.7]. Results show that high AI does not necessarily predispose to low VI. This difference suggests that every AB poses a different magnitude of asynchrony that may affect patient's PaO2 and PaCO2. Quantifying hourly VI along with AI during MV could be beneficial in explicating the aetiology of AB.

The Effect of Switching from Volume-Controlled to Pressure-Controlled Ventilation on Respiratory Distress and Asynchrony Index Improvement among Mechanically Ventilated Adults.

It is important to synchrony the time, intensity, and respiratory signal of the phrenic nerve between the patient and the ventilator. This study aimed to evaluate the effect of switching from volume-controlled to pressure-controlled ventilation on respiratory distress and asynchrony index improvement. In this randomized controlled clinical trial, 70 patients admitted to the intensive care unit under mechanical ventilation were included. Asynchronous evaluation was performed by examining the patient and evaluating and analyzing the graphic flow curve and ventilator pressure, which included trigger and flow asynchronous and asynchronous cycling. In the intervention group, the mode of ventilation was switched to PSIMV such that peak inspiratory pressures would be equivalent to positive end-expiratory pressure (PEEP) in the volume-controlled mode. Finally, again at 60, 75, and 90 min, information about the ventilator and the patient's symptoms, and arterial carbon dioxide levels were sent by arterial gas sample. The asynchronous index was also recorded in both groups. This study showed that the mean of variables such as height, ideal body weight, tidal volume, set rate; Sense, FiO2, PEEP did not differ significantly between the two groups. The mean of asynchrony was significantly reduced in both control group (16.51 ± 3.35-14.51 ± 2.90; P < 0.001) and intervention group (18.26 ± 6.13-13.32 ± 5.53; P < 0.001). Regardless of the type and severity of the disease, switching the ventilation mode from volume-controlled to pressure-controlled can improve patient adaptation to the ventilator, especially in cases with frequent asynchrony.

Responses of Bilevel Ventilators to Unintentional Leak: A Bench Study.

The impact of leaks has mainly been assessed in bench models using continuous leak patterns which did not reflect real-life leakage. We aimed to assess the impact of the pattern and intensity of unintentional leakage (UL) using several respiratory models. An active artificial lung (ASL 5000) was connected to three bilevel-ventilators set in pressure mode; the experiments were carried out with three lung mechanics (COPD, OHS and NMD) with and without upper airway obstruction. Triggering delay, work of breathing, pressure rise time, inspiratory pressure, tidal volume, cycling delay and the asynchrony index were measured at 0, 6, 24 and 36 L/min of UL. We generated continuous and inspiratory UL. Compared to 0 L/min of UL, triggering delays were significantly higher with 36 L/min of UL (+27 ms) and pressure rise times were longer (+71 ms). Cycling delays increased from -4 [-250-169] ms to 150 [-173-207] ms at, respectively 0 L/min and 36 L/min of UL and work of breathing increased from 0.15 [0.12-0.29] J/L to 0.19 [0.16-0.36] J/L. Inspiratory leakage pattern significantly increased triggering delays (+35 ms) and cycling delays (+263 ms) but decreased delivered pressure (-0.94 cmH2O) compared to continuous leakage pattern. Simulated upper airway obstruction significantly increased triggering delay (+199 ms), cycling delays (+371 ms), and decreased tidal volume (-407 mL) and pressure rise times (-56 ms). The pattern of leakage impacted more the device performances than the magnitude of the leakage per se. Flow limitation negatively reduced all ventilator performances.

Patient-Ventilator Synchrony in Extremely Premature Neonates during Non-Invasive Neurally Adjusted Ventilatory Assist or Synchronized Intermittent Positive Airway Pressure: A Randomized Crossover Pilot Trial

Introduction: Synchronization of non-invasive ventilation is challenging in extremely premature infants. We compared patient-ventilator synchrony between non-invasive neurally adjusted ventilatory assist (NIV-NAVA) using transdiaphragmatic (Edi) catheter and synchronized intermittent positive airway pressure (SiPAP) using an abdominal trigger. Methods: This study was a monocentric, randomized, crossover trial in premature infants born before 28 weeks of gestation, aged 3 days or more, and below 32 weeks postmenstrual age. NIV-NAVA and SiPAP were applied in a random order for 2 h with analysis of data from the second hour. The primary outcome was the asynchrony index. Results: Fourteen patients were included (median [IQR] gestational age at birth 25.6 (25.3–26.4) weeks, median [IQR] birth weight 755 [680–824] g, median [IQR] postnatal age 26.5 [19.8–33.8] days). The median (IQR) asynchrony index was significantly lower in NIV-NAVA versus SiPAP (49.9% [44.1–52.6] vs. 85.8% [74.2–90.9], p < 0.001). Ineffective efforts and auto-triggering were significantly less frequent in NIV-NAVA versus SiPAP (3.0% vs. 32.0% p < 0.001 and 10.0% vs. 26.6%, p = 0.004, respectively). Double triggering was significantly less frequent in SiPAP versus NIV-NAVA (0.0% vs. 9.0%, p < 0.001). No significant difference was observed for premature cycling and late cycling. Peak Edi and swing Edi were significantly lower in NIV-NAVA as compared to SiPAP (7.7 [6.1–9.9] vs. 11.0 [6.7–14.5] μV, p = 0.006; 5.4 [4.2–7.6] vs. 7.6 [4.3–10.8] μV, p = 0.007, respectively). No significant difference was observed between NIV-NAVA and SiPAP for heart rate, respiratory rate, COMFORTneo scores, apnoea, desaturations, or bradycardias. Discussion/Conclusion: NIV-NAVA markedly improves patient-ventilator synchrony as compared to SiPAP in extremely premature infants.

Improved Understanding of How Catchment Properties Control Hydrological Partitioning Through Machine Learning

AbstractLong‐term hydrological partitioning of catchments can be well described by the Budyko framework with a parameter (e.g., Fu's equations with parameter ω). The Budyko framework considers aridity index as the dominant control on hydrological partitioning, while the parameter represents integrated influences of catchment properties. Our understanding regarding the controls of catchment properties on the parameter is still limited. In this study, two machine learning methods, that is, boosted regression tree (BRT) and CUBIST, were used to model ω. Interpretable machine learning methods were adopted for better physical understanding including feature importance, accumulated local effects (ALE), and local interpretable model‐agnostic explanations. Among the 15 properties of 443 Australian catchments, analysis of feature importance showed that root zone storage capacity (SR), vapor pressure, vegetation coverage (M), precipitation depth, climate seasonality and asynchrony index (SAI), and water use efficiency (WUE) were the six primary control factors on ω. ALE showed that ω varied nonlinearly with all factors, and varied non‐monotonically with M, SAI, and WUE. LIME showed that the importance of the six dominant factors on ω varied between regions. CUBIST was further used to build regionally varying relationships between ω and the primary factors. Continental scale ω and evapotranspiration were further mapped across Australia based on the most robust BRT‐trained parameterization scheme with a resolution of 0.05°. Instead of using the machine learning method as a black box, we employed interpretability approaches to identify the controls. Our findings not only improved the capability of the Budyko method for hydrological partitioning across Australia, but also demonstrated that the controls of catchment properties on hydrological partitioning vary in different regions.

Pediatrik aktif hastalarda asenkroninin basınç zaman ürününe etkisinin (PTP) değerlendirmesi

Aim: To reveal the effect of synchronous or asynchronous patients on these values by examining the esophageal pressure (Pes ), transpulmonary pressure (PL), and time-pressure-product (PTP) in active patients who are planned to be weaned from the mechanical ventilator and can trigger their own respiration.&#x0D; Material and Methods: Patients who are monitored on a mechanical ventilator and who can trigger their own respiration, and who are planned to be separated from the mechanical ventilator in the near future, were taken and an esophageal catheter was inserted, then the catheter filling volume was first optimized and then validated, and PL was started to be recorded by removing Pes from the airway pressure. With the recording devices on the ventilator, the mechanical ventilation parameters of the patients were recorded as both monitoring values and waveforms. Then, the mechanical ventilation settings were rearranged by the clinician at the bedside with reference to the esophageal waveform. Then, the waveforms of these patients have examined on the Matlab E2019a (Mathworks, USA) software, and each breath was marked as synchronous-asynchronous and the asynchrony index (AI) was calculated. Accordingly, the asynchronous and synchronous periods of the patient were defined, and the values of ∆Pes, ∆PL, PTP-Pes, PTP-PL, PTP-Pes-minutes, PTP-PL-minutes between the synchronous and asynchronous periods of the same patient were calculated on Matlab software, and synchronous and The changes in these values between asynchronous periods were analyzed with the SPSS statistical program. After examining the distribution of the variables by visual and analytical methods (Shapiro-Wilk), the statistical difference between the groups was examined.&#x0D; Results: When the synchronous and asynchronous periods of all patients were compared, the following differences were found between the synchronous and asynchronous groups in favor of the synchronous group. ∆Peso [5.98 (3.55-9.08)] cmH2O, ∆PL [5 (2.7-7.25]cmH2O, ∆PTP-Peso [3.70 (2.81-4.6)) ]cmH2O*s, ∆PTP-PL [3.36 (1.39-4.18)]cmH2O*s, ∆PTP-Peso/min [1.72 (1.02-2.67)]cmH2O*min , ∆PTP-PL/min [1.25 (0.30-3.01)]cmH2O*min.&#x0D; Conclusion: It has also been shown by statistical analyzes that the synchronization of active patients who are planned to be weaned from mechanical ventilator and who can trigger their own respiration causes a decrease in the values of ∆Peso, ∆Ptranspulmonary, PTP-Peso,PTP-Ptranspulmonary, PTP-Peso-minute, PTP-Ptranspulmonary-minute. When this information about synchronization is correlated with the literature, Pes monitoring in patients and synchronization of patients prevent mechanical ventilation-related lung damage that may occur in these patients, and thus the duration of hospital stay due to asynchrony, failure to wean from the ventilator, and the consequences that may occur. It is a method that can be useful in reducing mortality and morbidity.

Patient-Ventilator Synchrony in Neurally-Adjusted Ventilatory Assist and Variable Pressure Support Ventilation.

Neurally-adjusted ventilatory assist (NAVA) improves patient-ventilator synchrony and reduces the risk of respiratory over-assistance. Variable pressure support ventilation (PSV) is a recently introduced mode of assisted ventilation that has also shown reduction in patient-ventilator asynchronies. We hypothesized that NAVA would reduce patient-ventilator asynchronies and inspiratory effort compared to variable PSV because breathing variability was intrinsically determined by the patient and not by the ventilator. This study aimed to evaluate patient-ventilator asynchronies and inspiratory effort pressure-time product (PTP) between NAVA and variable PSV in subjects with mild ARDS. After 24 h of controlled mechanical ventilation, subjects (PaO2 /FIO2 200-300 and PEEP level < 10 cm H2O) were randomized in sequence 1:1 by using a web-based encrypted platform and assigned to NAVA or variable PSV groups. Both modes of ventilation were consecutively kept for 24 h unless there were clinical changes. The primary aim of this study was to evaluate differences in asynchrony index (AI) between variable PSV and NAVA. Our secondary aims were to evaluate the coefficient of variation (CV) of breathing patterns and inspiratory effort between the groups. Thirteen subjects were randomized in the NAVA group and 13 subjects in the variable PSV group. AI over time and minute PTP (PTPmin) were not different between NAVA and variable PSV groups (AI t0P = .52, AI t12P = .27, AI t24P = .12; and PTPmin-t0P = .60, PTPmin-t12P = .57, PTPmin-t24P = .85, respectively). CV for tidal volume (VT) and pressure support (PS) was lower in variable PSV group over time compared with NAVA group (P < .05). In this randomized controlled trial including subjects with mild ARDS, NAVA and variable PSV had comparable effects on patient-ventilator synchronies and PTP. However, variable PSV reduced the variability of VT and PS when compared with NAVA.

Neurally-Adjusted Ventilatory Assist Versus Pressure Support Ventilation During Noninvasive Ventilation.

Noninvasive ventilation (NIV) is increasingly used during ventilatory support. Neurally-adjusted ventilatory assist (NAVA) is a mode of mechanical ventilation that can improve patient-ventilator interaction. We conducted a meta-analysis to compare patient-ventilator interaction and clinical outcomes between NAVA and pressure support ventilation (PSV) in adult subjects during NIV. The PubMed, Cochrane Library, Web of Science, OpenGrey, and Embase databases were searched for appropriate clinical trials comparing NIV-NAVA with NIV-PSV for adult subjects. Comparisons of asynchrony index (AI), types of asynchrony, and clinical outcomes were pooled. Fifteen studies were included involving 615 subjects. AI was significantly lower in NAVA than PSV group (mean difference [MD] -14.70 [95% CI -23.20 to -6.19], P < .001). Subgroup analysis grouped by exacerbation of COPD or non-COPD showed that the AI of NAVA was lower than PSV in COPD exacerbation (MD -14.56 [95% CI -21.04 to -8.09], P < .001) and non-COPD (MD -3.02 [95% CI -4.44 to -1.61], P < .001). Severe asynchrony was significantly lower in NAVA than in PSV (odds ratio 0.06 [95% CI 0.03-0.11], P < .001). Inspiratory trigger delay in NAVA was significantly lower than PSV (MD -129.60 [95% CI -148.43 to -110.78], P < .001). NAVA had longer ICU length of stay than PSV (MD 1.22 [95% CI 0.44-2.00], P = .002). Level of discomfort was significantly higher in NAVA group than PSV group (MD 0.62 [95% CI 0.02-1.21], P = .040). NAVA has advantages in ventilator-patient interaction compared to PSV in NIV. Further research is needed in order to estimate effects on clinical outcomes.

Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method

BackgroundWhether respiratory efforts and their timing can be reliably detected during pressure support ventilation using standard ventilator waveforms is unclear. This would give the opportunity to assess and improve patient–ventilator interaction without the need of special equipment.MethodsIn 16 patients under invasive pressure support ventilation, flow and pressure waveforms were obtained from proximal sensors and analyzed by three trained physicians and one resident to assess patient’s spontaneous activity. A systematic method (the waveform method) based on explicit rules was adopted. Esophageal pressure tracings were analyzed independently and used as reference. Breaths were classified as assisted or auto-triggered, double-triggered or ineffective. For assisted breaths, trigger delay, early and late cycling (minor asynchronies) were diagnosed. The percentage of breaths with major asynchronies (asynchrony index) and total asynchrony time were computed.ResultsOut of 4426 analyzed breaths, 94.1% (70.4–99.4) were assisted, 0.0% (0.0–0.2) auto-triggered and 5.8% (0.4–29.6) ineffective. Asynchrony index was 5.9% (0.6–29.6). Total asynchrony time represented 22.4% (16.3–30.1) of recording time and was mainly due to minor asynchronies. Applying the waveform method resulted in an inter-operator agreement of 0.99 (0.98–0.99); 99.5% of efforts were detected on waveforms and agreement with the reference in detecting major asynchronies was 0.99 (0.98–0.99). Timing of respiratory efforts was accurately detected on waveforms: AUC for trigger delay, cycling delay and early cycling was 0.865 (0.853–0.876), 0.903 (0.892–0.914) and 0.983 (0.970–0.991), respectively.ConclusionsVentilator waveforms can be used alone to reliably assess patient’s spontaneous activity and patient–ventilator interaction provided that a systematic method is adopted.