[A Review on Automatic Detection Algorithm for Patient-Ventilator Asynchrony during Mechanical Ventilation]

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony (PVA) during mechanical ventilation. In the early stages, the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms. While these methods were intuitive and easy to operate, they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status. Subsequently, machine learning and deep learning technologies began to emerge and develop. These technologies automatically extract and learn data characteristics through algorithms, making PVA detection more robust and universal. Among them, logistic regression, support vector machines, random forest, hidden Markov models, convolutional autoencoders, long short-term memory networks, one-dimensional convolutional neural networks, etc., have all been successfully used for PVA recognition. Despite the significant advancements in feature extraction through deep learning methods, their demand for labelled data is high, potentially consuming significant medical resources. Therefore, the combination of reinforcement learning and self-supervised learning may be a viable solution. In addition, most algorithm validations are based on a single dataset, so the need for cross-dataset validation in the future will be an important and challenging direction for development.

The Ability of Critical Care Physicians to Identify Patient-Ventilator Asynchrony Using Waveform Analysis: A National Survey

BACKGROUND

Improved patient-ventilator asynchrony (PVA) identification using waveform analysis by critical care physicians (CCPs) may improve patient outcomes. This study aimed to assess the ability of CCPs to identify different types of PVAs using waveform analysis as well as factors related to this ability.

METHODS

We surveyed 12 university-affiliated medical ICUs (MICUs) in Tunisia. CCPs practicing in these MICUs were asked to visually identify 4 clinical cases, each corresponding to a different PVA. We collected the following characteristics regarding CCPs: scientific grade, years of experience, prior training in mechanical ventilation, prior exposure to waveform analysis, and the characteristics of the MICUs in which they practice. Respondents were categorized into 2 groups based on their ability to correctly identify PVAs (defined as the correct identification of at least 3 of the 4 PVA cases). Univariate analysis was performed to identify factors related to the correct identification of PVA.

RESULTS

Among 136 included CCPs, 72 (52.9%) responded to the present survey. The respondents comprised 59 (81.9%) residents, and 13 (18.1%) senior physicians. Further, 50 (69.4%) respondents had attended prior training in mechanical ventilation. Moreover, 21 (29.2%) of the respondents could correctly identify PVAs. Double-triggering was the most frequently identified PVA type, 43 (59.7%), followed by auto-triggering, 36 (50%); premature cycling, 28 (38.9%); and ineffective efforts, 25 (34.7%). Univariate analysis indicated that senior physicians had a better ability to correctly identify PVAs than residents (7 [53.8%] vs 14 [23.7%], P = .044).

CONCLUSIONS

The present study revealed a significant deficiency in the accurate visual identification of PVAs among CCPs in the MICUs. When compared to residents, senior physicians exhibited a notably superior aptitude for correctly recognizing PVAs.

Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony

BACKGROUND

Patient-ventilator asynchrony is common in patients undergoing mechanical ventilation. The proportion of health-care professionals capable of identifying and effectively managing different types of patient-ventilator asynchronies is limited. A few studies have developed specific training programs, but they mainly focused on improving patient-ventilator asynchrony detection without assessing the ability of health-care professionals to determine the possible causes.

METHODS

We conducted a 36-h training program focused on patient-ventilator asynchrony detection and management for health-care professionals from 20 hospitals in Latin America and Spain. The training program included 6 h of a live online lesson during which 120 patient-ventilator asynchrony cases were presented. After the 6-h training lesson, health-care professionals were required to complete a 1-h training session per day for the subsequent 30 d. A 30-question assessment tool was developed and used to assess health-care professionals before training, immediately after the 6-h training lecture, and after the 30 d of training (1-month follow-up).

RESULTS

One hundred sixteen health-care professionals participated in the study. The median (interquartile range) of the total number of correct answers in the pre-training, post-training, and 1-month follow-up were significantly different (12 [8.75-15], 18 [13.75-22], and 18.5 [14-23], respectively). The percentages of correct answers also differed significantly between the time assessments. Study participants significantly improved their performance between pre-training and post-training (P < .001). This performance was maintained after a 1-month follow-up (P = .95) for the questions related to the detection, determination of cause, and management of patient-ventilator asynchrony.

CONCLUSIONS

A specific 36-h training program significantly improved the ability of health-care professionals to detect patient-ventilator asynchrony, determine the possible causes of patient-ventilator asynchrony, and properly manage different types of patient-ventilator asynchrony.

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research

Mechanically ventilated patients generate waveform data that corresponds to patient interaction with unnatural forcing. This breath information includes both patient and apparatus sources, imbuing data with broad heterogeneity resulting from ventilator settings, patient efforts, patient-ventilator dyssynchronies, injuries, and other clinical therapies. Lung-protective ventilator settings outlined in respiratory care protocols lack personalization, and the connections between clinical outcomes and injuries resulting from mechanical ventilation remain poorly understood. Intra- and inter-patient heterogeneity and the volume of data comprising lung-ventilator system (LVS) observations limit broader and longer-time analysis of such systems. This work presents a computational pipeline for resolving LVS systems by tracking the evolution of data-conditioned model parameters and ventilator information. For individuals, the method presents LVS trajectory in a manageable way through low-dimensional representation of phenotypic breath waveforms. More general phenotypes across patients are also developed by aggregating patient-personalized estimates with additional normalization. The effectiveness of this process is demonstrated through application to multi-day observational series of 35 patients, which reveals the complexity of changes in the LVS over time. Considerable variations in breath behavior independent of the ventilator are revealed, suggesting the need to incorporate care factors such as patient sedation and posture in future analysis. The pipeline also identifies structural similarity in pressure-volume (pV) loop characterizations at the cohort level. The design invites active learning to incorporate clinical practitioner expertise into various methodological stages and algorithm choices.

Ineffective Effort in Patients With Acute Brain Injury Undergoing Invasive Mechanical Ventilation

BACKGROUND

Ineffective effort (IE) is a frequent patient-ventilator asynchrony in invasive mechanical ventilation. This study aimed to investigate the incidence of IE and to explore its relationship with respiratory drive in subjects with acute brain injury undergoing invasive mechanical ventilation.

METHODS

We retrospectively analyzed a clinical database that assessed patient-ventilator asynchrony in subjects with acute brain injury. IE was identified based on airway pressure, flow, and esophageal pressure waveforms collected at 15-min intervals 4 times daily. At the end of each data set recording, airway-occlusion pressure (P0.1) was determined by the airway occlusion test. IE index was calculated to indicate the severity of IE. The incidence of IE in different types of brain injuries as well as its relationship with P0.1 was determined.

RESULTS

We analyzed 852 data sets of 71 subjects with P0.1 measured and undergoing mechanical ventilation for at least 3 d after enrollment. IE was detected in 688 (80.8%) data sets, with a median index of 2.2% (interquartile range 0.4-13.1). Severe IE (IE index ≥ 10%) was detected in 246 (28.9%) data sets. The post craniotomy for brain tumor and the stroke groups had higher median IE index and lower P0.1 compared with the traumatic brain injury group (2.6% [0.7-9.7] vs 2.7% [0.3-21] vs 1.2% [0.1-8.5], P = .002; 1.4 [1-2] cm H2O vs 1.5 [1-2.2] cm H2O vs 1.8 [1.1-2.8] cm H2O, P = .001). Low respiratory drive (P0.1 < 1.14 cm H2O) was independently associated with severe IE in the expiratory phase (IEE) even after adjusting for confounding factors by logistic regression analysis (odds ratio 5.18 [95% CI 2.69-10], P < .001).

CONCLUSIONS

IE was very common in subjects with acute brain injury. Low respiratory drive was independently associated with severe IEE.

Reverse Triggered Breath during Pressure Support Ventilation and Neurally Adjusted Ventilatory Assist at Increasing Propofol Infusion

BACKGROUND

Reverse triggered breath (RTB) has been extensively described during assisted-controlled modes of ventilation. We aimed to assess whether RTB occurs during Pressure Support Ventilation (PSV) and Neurally Adjusted Ventilatory Assist (NAVA) at varying depths of propofol sedation.

METHODS

This is a retrospective analysis of a prospective crossover randomized controlled trial conducted in an Intensive Care Unit (ICU) of a university hospital. Fourteen intubated patients for acute respiratory failure received six trials of 25 minutes randomly applying PSV and NAVA at three different propofol infusions: awake, light, and deep sedation. We assessed the occurrence of RTBs at each protocol step. The incidence level of RTBs was determined through the RTB index, which was calculated by dividing RTBs by the total number of breaths triggered and not triggered.

RESULTS

RTBs occurred during both PSV and NAVA. The RTB index was greater during PSV than during NAVA at mild (1.5 [0.0; 5.3]% vs. 0.6 [0.0; 1.1]%) and deep (5.9 [0.7; 9.0]% vs. 1.7 [0.9; 3.5]%) sedation.

CONCLUSIONS

RTB occurs in patients undergoing assisted mechanical ventilation. The level of propofol sedation and the mode of ventilation may influence the incidence of RTBs.

An investigation on the respiratory mechanics of mechanically ventilated patients during spontaneous breathing trials with enhanced low-level pressure support ventilation

INTRODUCTION

Low-level pressure support ventilation (PSV) is most commonly adopted in spontaneous breathing trials (SBTs), and some have proposed setting the positive end-expiratory pressure (PEEP) to 0 cmH₂ O in order to shorten the observation time of SBTs. This study aims to investigate the effects of two PSV protocols on the patients' respiratory mechanics.

MATERIAL AND METHOD

A prospective randomized self-controlled crossover design was adopted in this study, which involved enrolling 30 difficult-to-wean patients who were admitted to the intensive care unit of the First Affiliated Hospital of Guangzhou Medical University between July 2019 and September 2021. Patients were subjected to the S group (pressure support: 8 cmH₂ O, PEEP: 5 cmH₂ O) and S1 group (PS: 8 cmH₂ O, PEEP: 0 cmH₂ O) for 30 min in a random order, and respiratory mechanics indices were dynamically monitored via a four-lumen multi-functional catheter with an integrated gastric tube. Among the 30 enrolled patients, 27 were successfully weaned.

RESULT

The S group showed higher airway pressure (Paw), intragastric pressure (Pga) and airway pressure-time product (PTP) than the S1 group. The S group also showed a shorter inspiratory trigger delay, (93.80 ± 47.85) versus (137.33 ± 85.66) ms (P = 0.004); and fewer abnormal triggers, (0.97 ± 2.65) versus (2.67 ± 4.48) (P = 0.042) compared with the S1 group. Stratification based on the causes of mechanical ventilation revealed that under the S1 protocol, patients with chronic obstructive pulmonary disease (COPD) had a longer inspiratory trigger delay compared to both post-thoracic surgery (PTS) patients and patients with acute respiratory distress syndrome. Despite providing greater respiratory support, S group led to significant reductions in inspiratory trigger delay and less abnormal triggers compared to S1 group, especially among patients with chronic obstructive pulmonary disease.

CONCLUSION

These findings suggest that the zero PEEP group was more likely to induce a higher number of patient-ventilator asynchronies in difficult-to-wean patients.

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

BACKGROUND

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.

METHOD

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.

RESULT

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%.

CONCLUSIONS

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.

Neurally Adjusted Ventilatory Assist vs. Conventional Mechanical Ventilation in Adults and Children With Acute Respiratory Failure: A Systematic Review and Meta-Analysis

Background

Patient-ventilator asynchrony is a common problem in mechanical ventilation (MV), resulting in increased complications of MV. Despite there being some pieces of evidence for the efficacy of improving the synchronization of neurally adjusted ventilatory assist (NAVA), controversy over its physiological and clinical outcomes remain. Herein, we conducted a systematic review and meta-analysis to determine the relative impact of NAVA or conventional mechanical ventilation (CMV) modes on the important outcomes of adults and children with acute respiratory failure (ARF).

Methods

Qualified studies were searched in PubMed, EMBASE, Medline, Web of Science, Cochrane Library, and additional quality evaluations up to October 5, 2021. The primary outcome was asynchrony index (AI); secondary outcomes contained the duration of MV, intensive care unit (ICU) mortality, the incidence rate of ventilator-associated pneumonia, pH, and Partial Pressure of Carbon Dioxide in Arterial Blood (PaCO2). A statistical heterogeneity for the outcomes was assessed using the I ² test. A data analysis of outcomes using odds ratio (OR) for ICU mortality and ventilator-associated pneumonia incidence and mean difference (MD) for AI, duration of MV, pH, and PaCO2, with 95% confidence interval (CI), was expressed.

Results

Eighteen eligible studies (n = 926 patients) were eventually enrolled. For the primary outcome, NAVA may reduce the AI (MD = -18.31; 95% CI, -24.38 to -12.25; p < 0.001). For the secondary outcomes, the duration of MV in the NAVA mode was 2.64 days lower than other CMVs (MD = -2.64; 95% CI, -4.88 to -0.41; P = 0.02), and NAVA may decrease the ICU mortality (OR =0.60; 95% CI, 0.42 to 0.86; P = 0.006). There was no statistically significant difference in the incidence of ventilator-associated pneumonia, pH, and PaCO2 between NAVA and other MV modes.

Conclusions

Our study suggests that NAVA ameliorates the synchronization of patient-ventilator and improves the important clinical outcomes of patients with ARF compared with CMV modes.

Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study

STUDY OBJECTIVES

In patients treated with noninvasive ventilation, sleep-related breathing changes can modify patient-ventilator interactions, which could reduce its effectiveness. The aim of this prospective observational study was to determine the impact of sleep/wake state on leaks, upper airway obstructive events, and asynchronies in patients treated by long-term noninvasive ventilation.

METHODS

Stable patients adapted to noninvasive ventilation were considered for nocturnal polysomnography. Unintentional leaks, upper airway obstructive events, and asynchronies were compared between sleep and awake periods.

RESULTS

Twenty-eight patients were enrolled. Underlying diagnoses were neuromuscular disease (n = 11), chest wall disease (n = 8), and obesity-hypoventilation (n = 9). Leaks were more frequent in sleep than in awake periods, with a median of 10% (interquartile range [IQR], 0%-75%) vs 1% (IQR, 0%-9%) of time (P < .001), respectively. During sleep, asynchronies with and without associated leak affected 27% of breaths (IQR, 16%-39%) compared with non-leak-related asynchronies that were recorded in 8% (IQR, 3%-25%) of breaths (P < .001). Asynchronies affecting more than 10% of total breaths were more frequent in sleep (25 patients, 89%) than in awake time (8 patients, 29%; P = .25). Eleven patients (39%) presented with 5 or more upper airway obstructive events without reduction in ventilatory drive per hour of sleep.

CONCLUSIONS

In patients adapted to home noninvasive ventilation, leaks, asynchronies, and upper airway obstructive events are frequent during the night and are concentrated in sleep periods. Asynchronies are often associated with leaks. These findings may have clinical implications considering that in patients with low sleep efficiency respiratory events could be underestimated if sleep is not evaluated.

CITATION

Martí S, Ferré A, Sampol G, et al. Sleep increases leaks and asynchronies during home noninvasive ventilation: a polysomnographic study. J Clin Sleep Med. 2022;18(1):225-233.

Automatic Adjustment of the Inspiratory Trigger and Cycling-Off Criteria Improved Patient-Ventilator Asynchrony During Pressure Support Ventilation

Background: Patient-ventilator asynchrony is common during pressure support ventilation (PSV) because of the constant cycling-off criteria and variation of respiratory system mechanical properties in individual patients. Automatic adjustment of inspiratory triggers and cycling-off criteria based on waveforms might be a useful tool to improve patient-ventilator asynchrony during PSV.

Method: Twenty-four patients were enrolled and were ventilated using PSV with different cycling-off criteria of 10% (PS₁₀), 30% (PS₃₀), 50% (PS₅₀), and automatic adjustment PSV (PS_AUTO). Patient-ventilator interactions were measured.

Results: The total asynchrony index (AI) and NeuroSync index were consistently lower in PS_AUTO when compared with PS₁₀, PS₃₀, and PS₅₀, (P < 0.05). The benefit of PS_AUTO in reducing the total AI was mainly because of the reduction of the micro-AI but not the macro-AI. PS_AUTO significantly improved the relative cycling-off error when compared with prefixed controlled PSV (P < 0.05). PS_AUTO significantly reduced the trigger error and inspiratory effort for the trigger when compared with a prefixed trigger. However, total inspiratory effort, breathing patterns, and respiratory drive were not different among modes.

Conclusions: When compared with fixed cycling-off criteria, an automatic adjustment system improved patient-ventilator asynchrony without changes in breathing patterns during PSV. The automatic adjustment system could be a useful tool to titrate more personalized mechanical ventilation.

Short-Term Effects of Pressure Controlled Versus Volume Controlled Noninvasive Ventilation in Subjects With Amyotrophic Lateral Sclerosis

BACKGROUND

Comparison of the effects of pressure controlled and volume controlled noninvasive ventilations (NIV) has usually been limited to the degree of improvement in blood gases. We compared sleep quality, abnormal respiratory events, and patient-ventilator asynchronies during administration of pressure controlled continuous mandatory ventilation (PC-CMV) and volume controlled continuous mandatory ventilation (VC-CMV) in subjects with amyotrophic lateral sclerosis naive to NIV after titration aimed at maximally improving nocturnal arterial blood gases.

METHODS

A crossover evaluation of PC-CMV and VC-CMV was performed in 27 subjects with amyotrophic lateral sclerosis. After baseline polysomnography, ventilators were set in random order so as to warrant similar and satisfactory oxygen saturation and transcutaneous [Formula: see text] in both NIV modalities during day and night. Soon after titration, polysomnography was repeated during administration of each type of NIV.

RESULTS

With respect to the baseline night, non-rapid eye movement 3, and rapid eye movement sleep stages increased, and the arousal index decreased during PC-CMV (P = .005, P = .02, and P = .01, PC-CMV vs VC-CMV, respectively) but not during VC-CMV. The arousal index during NIV was correlated to the peak pressure delivered by the ventilators (ρ = 0.47, P < .001). Few abnormal respiratory events were observed in both NIV modes. Patient-ventilator asynchronies were more frequent during VC-CMV (median [IQR] 20.8 [0.0 - 22.0] vs 31.8 [30.1 - 34.0] no./h, PC-CMV vs VC-CMV; P = .002). Twenty-one subjects declared that they preferred PC-CMV therapy.

CONCLUSIONS

In the short term, PC-CMV may be a preferred NIV modality to VC-CMV for patients with amyotrophic lateral sclerosis, even when both NIV modes are similarly effective in the correction of hypoventilation. Evaluation of the effectiveness of NIV should not be limited to the assessment of blood gas correction.

Double-Triggering During Noninvasive Ventilation in a Simulated Lung Model

BACKGROUND

Double-triggering is a well-recognized form of patient-ventilator asynchrony in noninvasive ventilation (NIV). This benchtop simulated lung study aimed to determine under which patient and device-specific conditions double-triggering is more prevalent, and how this influences the delivery of NIV.

METHODS

Two commonly used proprietary NIV devices were tested using a benchtop lung model. Lung compliance, airway resistance, respiratory effort, and breathing frequency were manipulated, and the frequency of double-triggering was assessed. A lung model of very low lung compliance (15 mL/cm H₂O) was then used to assess the frequency of double-triggering when breathing frequency and respiratory effort were varied, along with basic NIV settings, including inspiratory pressure and expiratory pressure. Minute ventilation and total inspiratory work (as calculated by the simulated lung model) were also correlated with frequency of double-triggering.

RESULTS

In both devices, double-triggering was observed with reduced lung compliance (P = .02 and P < .001 for the two devices, respectively). Reduced airway resistance was associated with double-triggering with the one device only (P = .02). Respiratory effort and breathing frequency were not independent predictors of double-triggering across all lung models. In the lung model of very low lung compliance, both devices showed increased double-triggering at a lower breathing frequency (P < .001 and P < .001), higher respiratory effort (P = .03 and P < .001), and greater pressure support (P = .044, P < .001). Importantly, double-triggering was associated with reduced minute ventilation (P = .007) with one device and increased inspiratory work (P < .001) with the other device.

CONCLUSIONS

Both simulated-patient and device characteristics influenced the frequency of double-triggering in NIV, resulting in meaningful consequences in a simulated lung model.

Patient-Ventilator Interaction During Noninvasive Ventilation in Subjects With Exacerbation of COPD: Effect of Support Level and Ventilator Mode

BACKGROUND

Patient-ventilator synchrony in patients with COPD is at risk during noninvasive ventilation (NIV). NIV in neurally-adjusted ventilatory assist (NAVA) mode improves synchrony compared to pressure support ventilation (PSV). The current study investigated patient-ventilator interaction at 2 levels of NAVA and PSV mode in subjects with COPD exacerbation.

METHODS

NIV was randomly applied at 2 levels (5 and 15 cm H₂O) of PSV and NAVA. Patient-ventilator interaction was evaluated by comparing airway pressure and electrical activity of the diaphragm waveforms with automated computer algorithms.

RESULTS

8 subjects were included. Trigger delay was longer in PSV high (268 ± 112 ms) than in PSV low (161 ± 118 ms, P = .043), and trigger delay during NAVA was shorter than PSV for both low support (49 ± 24 ms for NAVA, P = .035) and high support (79 ± 276 ms for NAVA, P = .003). No difference in cycling error for low and high levels of PSV (PSV low -100 ± 114 ms and PSV high 56 ± 315 ms) or NAVA (NAVA low -5 ± 18 ms, NAVA high 12 ± 36 ms) and no difference between PSV and NAVA was found.

CONCLUSIONS

Increasing PSV levels during NIV caused a progressive mismatch between neural effort and pneumatic timing. Patient-ventilator interaction during NAVA was more synchronous than during PSV, independent of inspiratory support level. (ClinicalTrials.gov registration NCT01791335.).

EPISYNC study: predictors of patient-ventilator asynchrony in a prospective cohort of patients under invasive mechanical ventilation - study protocol

INTRODUCTION

Patient-ventilator asynchrony is common during the entire period of invasive mechanical ventilation (MV) and is associated with worse clinical outcomes. However, risk factors associated with asynchrony are not completely understood. The main objectives of this study are to estimate the incidence of asynchrony during invasive MV and its association with respiratory mechanics and other baseline patient characteristics.

METHODS AND ANALYSIS

We designed a prospective cohort study of patients admitted to the intensive care unit (ICU) of a university hospital. Inclusion criteria are adult patients under invasive MV initiated for less than 72 hours, and with expectation of remaining under MV for more than 24 hours. Exclusion criteria are high flow bronchopleural fistula, inability to measure respiratory mechanics and previous tracheostomy. Baseline assessment includes clinical characteristics of patients at ICU admission, including severity of illness, reason for initiation of MV, and measurement of static mechanics of the respiratory system. We will capture ventilator waveforms during the entire MV period that will be analysed with dedicated software (Better Care, Barcelona, Spain), which automatically identifies several types of asynchrony and calculates the asynchrony index (AI). We will use a linear regression model to identify risk factors associated with AI. To assess the relationship between survival and AI we will use Kaplan-Meier curves, log rank tests and Cox regression. The calculated sample size is 103 patients. The statistical analysis will be performed by the software R Programming (www.R-project.org) and will be considered statistically significant if the p value is less than 0.05.

ETHICS AND DISSEMINATION

The study was approved by the Ethics Committee of Instituto do Coração, School of Medicine, University of São Paulo, Brazil, and informed consent was waived due to the observational nature of the study. We aim to disseminate the study findings through peer-reviewed publications and national and international conference presentations.

TRIAL REGISTRATION NUMBER

NCT02687802; Pre-results.

Autocycling During Noninvasive Positive Pressure Ventilation Producing a Prolonged Severe Apnea and Syncope

ABSTRACT

Autocycling is a form of patient-ventilator asynchrony that can occur during mechanical ventilation. In the case described, autocycling during noninvasive ventilation led to severe hyperventilation and subsequently produced a prolonged central apnea that resulted in syncope. This case represents the first description of a severe adverse event from autocycling during noninvasive ventilation.

Neurally-Adjusted Ventilatory Assist for Noninvasive Ventilation via a Helmet in Subjects With COPD Exacerbation: A Physiologic Study

BACKGROUND

In patients with COPD exacerbation, noninvasive ventilation (NIV) is strongly recommended. NIV is generally delivered by using patient triggered and flow-cycled pressure support through a face mask. A specific method to generate neurally-controlled pressure support has been shown to improve comfort and patient-ventilator interaction. In addition, the helmet interface was better tolerated by patients compared with a face mask. Herein, we compared neurally-controlled pressure support through a helmet with pressure support through a face mask with respect to subject comfort, breathing pattern, gas exchange, pressurization and triggering performance, and patient-ventilator synchrony.

METHODS

Two 30-min trials of NIV were randomly delivered to 10 subjects with COPD exacerbation redundant: (1) pressure support through a face mask with inspiratory pressure support of ≥8 cm H₂O to obtain a tidal volume of 6-8 mL/kg of ideal body weight; and (2) NAVA through a helmet, setting the neurally-adjusted ventilatory assist level at 15 cm H₂O/μV, with an upper airway pressure limit to obtain the same overall airway pressure applied during pressure support through a face mask. We assessed subject comfort, breathing frequency, respiratory drive, arterial blood gases, pressure-time product (PTP) of the first 300 ms and 500ms after initiation of subject effort, inspiratory trigger delay, and rate of asynchrony determined as the asynchrony index.

RESULTS

Median and interquartile range NAVA through a helmet improved comfort (7.0 [6.0-8.0]) compared with pressure support through a face mask (5.0 [4.7-5.2], P = .005). The breathing pattern was not different between the methods. Respiratory drive was slightly, although not significantly, reduced (P = .19) during NAVA through a helmet in comparison with pressure support through a face mask. Gas exchange was also not different between the trials. The PTP of the first 300 ms (P = .92) and PTP of the first 500 ms (P = .08) were not statistically different between trials, whereas triggering performance, patient-ventilator interaction, and synchrony were all improved by NAVA through a helmet compared with pressure support through a face mask.

CONCLUSIONS

In the subjects with COPD with exacerbation, NAVA through a helmet improved comfort, triggering performance, and patient-ventilator synchrony compared with pressure support through a face mask.

Minimizing Asynchronies in Mechanical Ventilation: Current and Future Trends

Patient-ventilator asynchrony exists when the phases of breath delivered by the ventilator do not match those of the patient. Asynchronies occur throughout mechanical ventilation and negatively affect patient comfort, duration of mechanical ventilation, length of ICU stays, and mortality. Identifying asynchronies requires careful attention to patients and their ventilator waveforms. This review discusses the different types of asynchronies, how they are generated, and their impact on patient comfort and outcome. Moreover, it discusses practical approaches for detecting, correcting, and preventing asynchronies. Current evidence suggests that the best approach to managing asynchronies is by adjusting ventilator settings. Proportional modes improve patient-ventilator coupling, resulting in greater comfort and less dyspnea, but not in improved outcomes with respect to the duration of mechanical ventilation, delirium, or cognitive impairment. Advanced computational technologies will allow smart alerts, and models based on time series of asynchronies will be able to predict and prevent asynchronies, making it possible to tailor mechanical ventilation to meet each patient's needs throughout the course of mechanical ventilation.

Neurally Adjusted Ventilatory Assist After Pediatric Cardiac Surgery: Clinical Experience and Impact on Ventilation Pressures

BACKGROUND

After pediatric cardiac surgery, ventilation with high airway pressures can be detrimental to right ventricular function and pulmonary blood flow. Neurally adjusted ventilatory assist (NAVA) improves patient-ventilator interactions, helping maintain spontaneous ventilation. This study reports our experience with the use of NAVA in children after a cardiac surgery. We hypothesize that using NAVA in this population is feasible and allows for lower ventilation pressures.

METHODS

We retrospectively studied all children ventilated with NAVA (invasively or noninvasively) after undergoing cardiac surgery between January 2013 and May 2015 in our pediatric intensive care unit. The number and duration of NAVA episodes were described. For the first period of invasive NAVA in each subject, detailed clinical and ventilator data in the 4 h before and after the start of NAVA were extracted.

RESULTS

33 postoperative courses were included in 28 subjects with a median age of 3 [interquartile range (IQR) 1-12] months. NAVA was used invasively in 27 courses for a total duration of 87 (IQR 15-334) h per course. Peak inspiratory pressures and mean airway pressures decreased significantly after the start of NAVA (mean differences of 5.8 cm H₂O (95% CI 4.1-7.5) and 2.0 cm H₂O (95% CI 1.2-2.8), respectively, P < .001 for both). There was no significant difference in vital signs or blood gas values. NAVA was used noninvasively in 14 subjects, over 79 (IQR 25-137) h.

CONCLUSIONS

NAVA could be used in pediatric subjects after cardiac surgery. The significant decrease in airway pressures observed after transition to NAVA could have a beneficial impact in this specific population, which should be investigated in future interventional studies.

Effect of ventilator mode on patient-ventilator synchrony and work of breathing in neonatal pigs

BACKGROUND

Patient-ventilator asynchrony can result in increased work of breathing (WOB) and need for increased sedation, as well as respiratory muscle fatigue and prolonged mechanical ventilation. Different ventilator modes may result in varying degrees of asynchrony and WOB.

OBJECTIVE

The objectives of this study were to assess the incidence of asynchrony and the effect of asynchrony on WOB in three modes of ventilation: pressure regulated volume control (PRVC), synchronized intermittent mandatory ventilation/volume control plus pressure support (SIMV/VC plus PS), and SIMV/PRVC plus PS.

METHODS

Ten piglets (2.1 ± 0.3 kg) were studied, each in the healthy and surfactant-depleted, lung-injured state. Piglets were sedated, intubated, and ventilated with the three modes of ventilation randomly applied. Piglets then underwent surfactant washout, after which the lungs were re-recruited, and the modes of ventilation were repeated. Airway flow and pressure waveforms were acquired via pneumotachograph. Waveforms were analyzed for patient-ventilator asynchrony and pressure time product (PTP) as an estimate of patient WOB. RESULTS SIMV/VC plus PS had the highest incidence of asynchrony. The incidence of asynchrony was less in the injured lung. PTP (cm H₂ O*S) was increased for SIMV/VC plus PS (healthy 0.10 ± 0.12; injured 0.15 ± 0.13) compared to PRVC (healthy 0.05 ± 0.05; injured 0.06 ± 0.03), (P < 0.03) in both the healthy and injured lung models.

CONCLUSIONS

Asynchrony and WOB are highest with SIMV/VC plus PS. If SIMV is utilized, SIMV/PRVC plus a PS that optimizes tidal volume may be preferable. PRVC has the least asynchrony and WOB in the injured lung.

Simulation of late inspiratory rise in airway pressure during pressure support ventilation

BACKGROUND

Late inspiratory rise in airway pressure (LIRAP, Paw/ΔT) caused by inspiratory muscle relaxation or expiratory muscle contraction is frequently seen during pressure support ventilation (PSV), although the modulating factors are unknown.

METHODS

We investigated the effects of respiratory mechanics (normal, obstructive, restrictive, or mixed), inspiratory effort (-2, -8, or -15 cm H2O), flow cycle criteria (5-40% peak inspiratory flow), and duration of inspiratory muscle relaxation (0.18-0.3 s) on LIRAP during PSV using a lung simulator and 4 types of ventilators.

RESULTS

LIRAP occurred with all lung models when inspiratory effort was medium to high and duration of inspiratory muscle relaxation was short. The normal lung model was associated with the fastest LIRAP, whereas the obstructive lung model was associated with the slowest. Unless lung mechanics were normal or mixed, LIRAP was unlikely to occur when inspiratory effort was low. Different ventilators were also associated with differences in LIRAP speed. Except for within the restrictive lung model, changes in flow cycle level did not abolish LIRAP if inspiratory effort was medium to high. Increased duration of inspiratory relaxation also led to the elimination of LIRAP. Simulation of expiratory muscle contraction revealed that LIRAP occurred only when expiratory muscle contraction occurred sometime after the beginning of inspiration.

CONCLUSIONS

Our simulation study reveals that both respiratory resistance and compliance may affect LIRAP. Except for under restrictive lung conditions, LIRAP is unlikely to be abolished by simply lowering flow cycle criteria when inspiratory effort is strong and relaxation time is rapid. LIRAP may be caused by expiratory muscle contraction when it occurs during inspiration.