Bedside waveforms interpretation as a tool to identify patient-ventilator asynchronies

Lung and Diaphragm Protection During Mechanical Ventilation in Patients with Acute Respiratory Distress Syndrome

Patients with acute respiratory distress syndrome often require mechanical ventilation to maintain adequate gas exchange and to reduce the workload of the respiratory muscles. Although lifesaving, positive pressure mechanical ventilation can potentially injure the lungs and diaphragm, further worsening patient outcomes. While the effect of mechanical ventilation on the risk of developing lung injury is widely appreciated, its potentially deleterious effects on the diaphragm have only recently come to be considered by the broader intensive care unit community. Importantly, both ventilator-induced lung injury and ventilator-induced diaphragm dysfunction are associated with worse patient-centered outcomes.

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

BACKGROUND

Subject-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.

METHODS

Alterations 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%.

RESULTS

IE 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.

CONCLUSIONS

The 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.

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection

BACKGROUND AND OBJECTIVE

Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data.

METHODS

In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy.

RESULTS

VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01).

CONCLUSIONS

Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

Reverse triggering ? a novel or previously missed phenomenon?

BACKGROUND

Reverse triggering (RT) was described in 2013 as a form of patient-ventilator asynchrony, where patient's respiratory effort follows mechanical insufflation. Diagnosis requires esophageal pressure (Pes) or diaphragmatic electrical activity (EAdi), but RT can also be diagnosed using standard ventilator waveforms.

HYPOTHESIS

We wondered (1) how frequently RT would be present but undetected in the figures from literature, especially before 2013; (2) whether it would be more prevalent in the era of small tidal volumes after 2000.

METHODS

We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, from 1950 to 2017, with key words related to asynchrony to identify papers with figures including ventilator waveforms expected to display RT if present. Experts labelled waveforms. 'Definite' RT was identified when Pes or EAdi were in the tracing, and 'possible' RT when only flow and pressure waveforms were present. Expert assessment was compared to the author's descriptions of waveforms.

RESULTS

We found 65 appropriate papers published from 1977 to now, containing 181 ventilator waveforms. 21 cases of 'possible' RT and 25 cases of 'definite' RT were identified by the experts. 18.8% of waveforms prior to 2013 had evidence of RT. Most cases were published after 2000 (1 before vs. 45 after, p = 0.03). 54% of RT cases were attributed to different phenomena. A few cases of identified RT were already described prior to 2013 using different terminology (earliest in 1997). While RT cases attributed to different phenomena decreased after 2013, 60% of 'possible' RT remained missed.

CONCLUSION

RT has been present in the literature as early as 1997, but most cases were found after the introduction of low tidal volume ventilation in 2000. Following 2013, the number of undetected cases decreased, but RT are still commonly missed. Reverse Triggering, A Missed Phenomenon in the Literature. Critical Care Canada Forum 2019 Abstracts. Can J Anesth/J Can Anesth 67 (Suppl 1), 1-162 (2020). https://doi-org.myaccess.library.utoronto.ca/ https://doi.org/10.1007/s12630-019-01552-z .

Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model

BACKGROUND

Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging.

OBJECTIVE

We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths.

METHODS

A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed.

RESULTS

A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records.

CONCLUSION

This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data.

Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

BACKGROUND

Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths.

METHODS

Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (ΔPes), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation.

RESULTS

6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a ΔPes > 10 cmH2O and 37.2% a ΔPes > 15 cmH2O.

CONCLUSIONS

Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate patient-ventilator interaction.

Driving pressure of respiratory system and lung stress in mechanically ventilated patients with active breathing

BACKGROUND

During control mechanical ventilation (CMV), the driving pressure of the respiratory system (ΔPrs) serves as a surrogate of transpulmonary driving pressure (ΔPlung). Expiratory muscle activity that decreases end-expiratory lung volume may impair the validity of ΔPrs to reflect ΔPlung. This prospective observational study in patients with acute respiratory distress syndrome (ARDS) ventilated with proportional assist ventilation (PAV+), aimed to investigate: (1) the prevalence of elevated ΔPlung, (2) the ΔPrs-ΔPlung relationship, and (3) whether dynamic transpulmonary pressure (Plungsw) and effort indices (transdiaphragmatic and respiratory muscle pressure swings) remain within safe limits.

METHODS

Thirty-one patients instrumented with esophageal and gastric catheters (n = 22) were switched from CMV to PAV+ and respiratory variables were recorded, over a maximum of 24 h. To decrease the contribution of random breaths with irregular characteristics, a 7-breath moving average technique was applied. In each patient, measurements were also analyzed per deciles of increasing lung elastance (Elung). Patients were divided into Group A, if end-inspiratory transpulmonary pressure (PLEI) increased as Elung increased, and Group B, which showed a decrease or no change in PLEI with Elung increase.

RESULTS

In 44,836 occluded breaths, ΔPlung ≥ 12 cmH2O was infrequently observed [0.0% (0.0-16.9%) of measurements]. End-expiratory lung volume decrease, due to active expiration, was associated with underestimation of ΔPlung by ΔPrs, as suggested by a negative linear relationship between transpulmonary pressure at end-expiration (PLEE) and ΔPlung/ΔPrs. Group A included 17 and Group B 14 patients. As Elung increased, ΔPlung increased mainly due to PLEI increase in Group A, and PLEE decrease in Group B. Although ΔPrs had an area receiver operating characteristic curve (AUC) of 0.87 (95% confidence intervals 0.82-0.92, P < 0.001) for ΔPlung ≥ 12 cmH2O, this was due exclusively to Group A [0.91 (0.86-0.95), P < 0.001]. In Group B, ΔPrs showed no predictive capacity for detecting ΔPlung ≥ 12 cmH2O [0.65 (0.52-0.78), P > 0.05]. Most of the time Plungsw and effort indices remained within safe range.

CONCLUSION

In patients with ARDS ventilated with PAV+, injurious tidal lung stress and effort were infrequent. In the presence of expiratory muscle activity, ΔPrs underestimated ΔPlung. This phenomenon limits the usefulness of ΔPrs as a surrogate of tidal lung stress, regardless of the mode of support.

Automated evaluation of typical patient-ventilator asynchronies based on lung hysteretic responses

BACKGROUND

Patient-ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure-volume (PV) loop.

METHODS

Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors.

RESULTS

The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment.

CONCLUSIONS

The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.

Ventilators, Settings, Autotitration Algorithms

The choice of a ventilator model for a single patient is usually based on parameters such as size (portability), presence or absence of battery and ventilatory modes. However, there are many details within each ventilator model about triggering, pressurisation or autotitration algorithms that may go unnoticed, but may be important or may justify some drawbacks that may occur during their use in individual patients. This review is intended to emphasize these differences. Guidance is also provided on the operation of autotitration algorithms, in which the ventilator is able to take decisions based on a measured or estimated parameter. It is important to know how they work and their potential sources of error. Current evidence on their use is also provided.

Impact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study)

BACKGROUND

Patient-ventilator asynchronies are usually detected by visual inspection of ventilator waveforms but with low sensitivity, even when performed by experts in the field. Recently, estimation of the inspiratory muscle pressure (P_mus) waveforms through artificial intelligence algorithm has been proposed (Magnamed®, São Paulo, Brazil). We hypothesized that the display of these waveforms could help healthcare providers identify patient-ventilator asynchronies.

METHODS

A prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated P_mus waveform would improve the correct identification of asynchronies in simulated clinical scenarios. The primary outcome was the mean asynchrony detection rate (sensitivity). Physicians and respiratory therapists who work in intensive care units were randomized to control or intervention group. In both groups, participants analyzed pressure and flow waveforms of 49 different scenarios elaborated using the ASL-5000 lung simulator. In the intervention group the estimated P_mus waveform was displayed in addition to pressure and flow waveforms.

RESULTS

A total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the P_mus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p < 0.001). This effect remained when stratifying asynchronies by type.

CONCLUSIONS

We showed that the display of the P_mus waveform improved the ability of healthcare professionals to recognize patient-ventilator asynchronies by visual inspection of ventilator tracings. These findings require clinical validation.

TRIAL REGISTRATION

ClinicalTrials.gov: NTC05144607. Retrospectively registered 3 December 2021.

Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data

BACKGROUND AND OBJECTIVE

Mechanical ventilation is a lifesaving treatment for critically ill patients in an Intensive Care Unit (ICU) or during surgery. However, one potential harm of mechanical ventilation is related to patient-ventilator asynchrony (PVA). PVA can cause discomfort to the patient, damage to the lungs, and an increase in the length of stay in the ICU and on the ventilator. Therefore, automated detection algorithms are being developed to detect and classify PVAs, with the goal of optimizing mechanical ventilation. However, the development of these algorithms often requires large labeled datasets; these are generally difficult to obtain, as their collection and labeling is a time-consuming and labor-intensive task, which needs to be performed by clinical experts.

METHODS

In this work, we aimed to develop a computer algorithm for the automatic detection and classification of PVA. The algorithm employs a neural network for the detection of the breath of the patient. The development of the algorithm was aided by simulations from a recently published model of the patient-ventilator interaction.

RESULTS

The proposed method was effective, providing an algorithm with reliable detection and classification results of over 90% accuracy. Besides presenting a detection and classification algorithm for a variety of PVAs, here we show that using simulated data in combination with clinical data increases the variability in the training dataset, leading to a gain in performance and generalizability.

CONCLUSIONS

In the future, these algorithms can be utilized to gain a better understanding of the clinical impact of PVAs and help clinicians to better monitor their ventilation strategies.

Neural Network-Enabled Identification of Weak Inspiratory Efforts during Pressure Support Ventilation Using Ventilator Waveforms

During pressure support ventilation (PSV), excessive assist results in weak inspiratory efforts and promotes diaphragm atrophy and delayed weaning. The aim of this study was to develop a classifier using a neural network to identify weak inspiratory efforts during PSV, based on the ventilator waveforms. Recordings of flow, airway, esophageal and gastric pressures from critically ill patients were used to create an annotated dataset, using data from 37 patients at 2-5 different levels of support, computing the inspiratory time and effort for every breath. The complete dataset was randomly split, and data from 22 patients (45,650 breaths) were used to develop the model. Using a One-Dimensional Convolutional Neural Network, a predictive model was developed to characterize the inspiratory effort of each breath as weak or not, using a threshold of 50 cmH₂O*s/min. The following results were produced by implementing the model on data from 15 different patients (31,343 breaths). The model predicted weak inspiratory efforts with a sensitivity of 88%, specificity of 72%, positive predictive value of 40%, and negative predictive value of 96%. These results provide a 'proof-of-concept' for the ability of such a neural-network based predictive model to facilitate the implementation of personalized assisted ventilation.

How I Teach Auto-PEEP: Applying the Physiology of Expiration

Teaching complex topics in mechanical ventilation can prove challenging for clinical educators, both at the bedside and in the classroom setting. Some of these topics, such as the topic of auto-positive end-expiratory pressure (auto-PEEP), consist of complicated physiological principles that can be difficult to convey in an organized and intuitive manner. In this entry of "How I Teach," we provide an approach to teaching the concept of auto-PEEP to senior residents and fellows working in the intensive care unit. We offer a framework for educators to effectively present the concepts of auto-PEEP to learners, either at the bedside or in the classroom setting, by summarizing key concepts and including concrete examples of the educational techniques we use. This framework includes specific content we emphasize, how to present this content using a variety of educational resources, assessing learner understanding, and how to modify the topic on the basis of location, time, or resource constraints.

Asynchronies during invasive mechanical ventilation: narrative review and update

Invasive mechanical ventilation is a frequent therapy in critically ill patients in critical care units. To achieve favorable outcomes, patient and ventilator interaction must be adequate. However, many clinical situations could attempt against this principle and generate a mismatch between these two actors. These asynchronies can lead the patient to worst outcomes; because of that is vital to recognize and treat these entities as soon as possible. Early detection and recognition of the different asynchronies could favor the reduction of the days of mechanical ventilation, the days of hospital stay, and in intensive care and improve clinical results.

Advances in Ventilator Management for Patients with Acute Respiratory Distress Syndrome

The ventilatory care of patients with acute respiratory distress syndrome (ARDS) is evolving as our understanding of physiologic mechanisms of respiratory failure improves. Despite several decades of research, the mortality rate for ARDS remains high. Over the years, we continue to expand strategies to identify and mitigate ventilator-induced lung injury. This now includes a greater understanding of the benefits and harms associated with spontaneous breathing.

Patient-Ventilator Synchrony

Patient-ventilator asynchrony develops when the ventilator output does not match the efforts of the patient and contributes to excess work of breathing, lung injury, and mortality. Asynchronies are categorized as trigger (breath initiation), flow (delivery of the breath), and cycle (transition from inspiration to expiration). Clinicians should be skilled at ventilator waveform analysis to detect patient-ventilator asynchronies and make informed ventilator adjustments. Ventilator overdrive suppresses respiratory drive and reduces asynchrony, while other adjustments specific to the asynchrony are also useful.

Lung and diaphragm protective ventilation: a synthesis of recent data

INTRODUCTION

: To adhere to the Hippocratic Oath, to "first, do no harm", we need to make every effort to minimize the adverse effects of mechanical ventilation. Our understanding of the mechanisms of ventilator-induced lung injury (VILI) and ventilator-induced diaphragm dysfunction (VIDD) has increased in recent years. Research focuses now on methods to monitor lung stress and inhomogeneity and targets we should aim for when setting the ventilator. In parallel, efforts to promote early assisted ventilation to prevent VIDD have revealed new challenges, such as titrating inspiratory effort and synchronizing the mechanical with the patients' spontaneous breaths, while at the same time adhering to lung-protective targets.

AREAS COVERED

This is a narrative review of the key mechanisms contributing to VILI and VIDD and the methods currently available to evaluate and mitigate the risk of lung and diaphragm injury.

EXPERT OPINION

Implementing lung and diaphragm protective ventilation requires individualizing the ventilator settings, and this can only be accomplished by exploiting in everyday clinical practice the tools available to monitor lung stress and inhomogeneity, inspiratory effort, and patient-ventilator interaction.

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

Background

Whether 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.

Methods

In 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.

Results

Out 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.

Conclusions

Ventilator waveforms can be used alone to reliably assess patient’s spontaneous activity and patient–ventilator interaction provided that a systematic method is adopted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03895-4.

Patient-Ventilator Synchronization During Non-invasive Ventilation: A Pilot Study of an Automated Analysis System

Background: Patient-ventilator synchronization during non-invasive ventilation (NIV) can be assessed by visual inspection of flow and pressure waveforms but it remains time consuming and there is a large inter-rater variability, even among expert physicians. SyncSmart™ software developed by Breas Medical (Mölnycke, Sweden) provides an automatic detection and scoring of patient-ventilator asynchrony to help physicians in their daily clinical practice. This study was designed to assess performance of the automatic scoring by the SyncSmart software using expert clinicians as a reference in patient with chronic respiratory failure receiving NIV.

Methods: From nine patients, 20 min data sets were analyzed automatically by SyncSmart software and reviewed by nine expert physicians who were asked to score auto-triggering (AT), double-triggering (DT), and ineffective efforts (IE). The study procedure was similar to the one commonly used for validating the automatic sleep scoring technique. For each patient, the asynchrony index was computed by automatic scoring and each expert, respectively. Considering successively each expert scoring as a reference, sensitivity, specificity, positive predictive value (PPV), κ-coefficients, and agreement were calculated.

Results: The asynchrony index assessed by SynSmart was not significantly different from the one assessed by the experts (18.9 ± 17.7 vs. 12.8 ± 9.4, p = 0.19). When compared to an expert, the sensitivity and specificity provided by SyncSmart for DT, AT, and IE were significantly greater than those provided by an expert when compared to another expert.

Conclusions:SyncSmart software is able to score asynchrony events within the inter-rater variability. When the breathing frequency is not too high (<24), it therefore provides a reliable assessment of patient-ventilator asynchrony; AT is over detected otherwise.

Gas conditioning during helmet noninvasive ventilation: effect on comfort, gas exchange, inspiratory effort, transpulmonary pressure and patient-ventilator interaction

Background

There is growing interest towards the use of helmet noninvasive ventilation (NIV) for the management of acute hypoxemic respiratory failure. Gas conditioning through heat and moisture exchangers (HME) or heated humidifiers (HHs) is needed during facemask NIV to provide a minimum level of humidity in the inspired gas (15 mg H₂O/L). The optimal gas conditioning strategy during helmet NIV remains to be established.

Methods

Twenty patients with acute hypoxemic respiratory failure (PaO₂/FiO₂ < 300 mmHg) underwent consecutive 1-h periods of helmet NIV (PEEP 12 cmH₂O, pressure support 12 cmH₂O) with four humidification settings, applied in a random order: double-tube circuit with HHs and temperature set at 34 °C (HH34) and 37 °C (HH37); Y-piece circuit with HME; double-tube circuit with no humidification (NoH). Temperature and humidity of inhaled gas were measured through a capacitive hygrometer. Arterial blood gases, discomfort and dyspnea through visual analog scales (VAS), esophageal pressure swings (ΔP_ES) and simplified pressure–time product (PTP_ES), dynamic transpulmonary driving pressure (ΔP_L) and asynchrony index were measured in each step.

Results

Median [IqR] absolute humidity, temperature and VAS discomfort were significantly lower during NoH vs. HME, HH34 and HH37: absolute humidity (mgH₂O/L) 16 [12–19] vs. 28 [23–31] vs. 28 [24–31] vs. 33 [29–38], p < 0.001; temperature (°C) 29 [28–30] vs. 30 [29–31] vs. 31 [29–32] vs 32. [31–33], p < 0.001; VAS discomfort 4 [2–6] vs. 6 [2–7] vs. 7 [4–8] vs. 8 [4–10], p = 0.03. VAS discomfort increased with higher absolute humidity (p < 0.01) and temperature (p = 0.007). Higher VAS discomfort was associated with increased VAS dyspnea (p = 0.001). Arterial blood gases, respiratory rate, ΔP_ES, PTP_ES and ΔP_L were similar in all conditions. Overall asynchrony index was similar in all steps, but autotriggering rate was lower during NoH and HME (p = 0.03).

Conclusions

During 1-h sessions of helmet NIV in patients with hypoxemic respiratory failure, a double-tube circuit with no humidification allowed adequate conditioning of inspired gas, optimized comfort and improved patient–ventilator interaction. Use of HHs or HME in this setting resulted in increased discomfort due to excessive heat and humidity in the interface, which was associated with more intense dyspnea.

Trail Registration Registered on clinicaltrials.gov (NCT02875379) on August 23rd, 2016.

Reliability of plateau pressure during patient-triggered assisted ventilation. Analysis of a multicentre database

PURPOSE

An inspiratory hold during patient-triggered assisted ventilation potentially allows to measure driving pressure and inspiratory effort. However, muscular activity can make this measurement unreliable. We aim to define the criteria for inspiratory holds reliability during patient-triggered breaths.

MATERIAL AND METHODS

Flow, airway and esophageal pressure recordings during patient-triggered breaths from a multicentre observational study (BEARDS, NCT03447288) were evaluated by six independent raters, to determine plateau pressure readability. Features of "readable" and "unreadable" holds were compared. Muscle pressure estimate from the hold was validated against other measures of inspiratory effort.

RESULTS

Ninety-two percent of the recordings were consistently judged as readable or unreadable by at least four raters. Plateau measurement showed a high consistency among raters. A short time from airway peak to plateau pressure and a stable and longer plateau characterized readable holds. Unreadable plateaus were associated with higher indexes of inspiratory effort. Muscular pressure computed from the hold showed a strong correlation with independent indexes of inspiratory effort.

CONCLUSION

The definition of objective parameters of plateau reliability during assisted-breath provides the clinician with a tool to target a safer assisted-ventilation and to detect the presence of high inspiratory effort.

Proportional assist ventilation relieves clinically significant dyspnea in critically ill ventilated patients

INTRODUCTION

Dyspnea is common and often severe symptom in mechanically ventilated patients. Proportional assist ventilation (PAV) is an assist ventilatory mode that adjusts the level of assistance to the activity of respiratory muscles. We hypothesized that PAV reduce dyspnea compared to pressure support ventilation (PSV).

PATIENTS AND METHODS

Mechanically ventilated patients with clinically significant dyspnea were included. Dyspnea intensity was assessed by the Dyspnea-Visual Analog Scale (D-VAS) and the Intensive Care-Respiratory Distress Observation Scale (IC-RDOS) at inclusion (PSV-Baseline), after personalization of ventilator settings in order to minimize dyspnea (PSV-Personalization), and after switch to PAV. Respiratory drive was assessed by record of electromyographic activity of inspiratory muscles, the proportion of asynchrony was analyzed.

RESULTS

Thirty-four patients were included (73% males, median age of 66 [57-77] years). The D-VAS score was lower with PSV-Personalization (37 mm [20‒55]) and PAV (31 mm [14‒45]) than with PSV-Baseline (62 mm [28‒76]) (p < 0.05). The IC-RDOS score was lower with PAV (4.2 [2.4‒4.7]) and PSV-Personalization (4.4 [2.4‒4.9]) than with PSV-Baseline (4.8 [4.1‒6.5]) (p < 0.05). The electromyographic activity of parasternal intercostal muscles was lower with PAV and PSV-Personalization than with PSV-Baseline. The asynchrony index was lower with PAV (0% [0‒0.55]) than with PSV-Baseline and PSV-Personalization (0.68% [0‒2.28] and 0.60% [0.31‒1.41], respectively) (p < 0.05).

CONCLUSION

In mechanically ventilated patients exhibiting clinically significant dyspnea with PSV, personalization of PSV settings and PAV results in not different decreased dyspnea and activity of muscles to a similar degree, even though PAV was able to reduce asynchrony more effectively.

Trends in Pediatric Patient-Ventilator Asynchrony During Invasive Mechanical Ventilation

OBJECTIVES

To explore the level and time course of patient-ventilator asynchrony in mechanically ventilated children and the effects on duration of mechanical ventilation, PICU stay, and Comfort Behavior Score as indicator for patient comfort.

DESIGN

Secondary analysis of physiology data from mechanically ventilated children.

SETTING

Mixed medical-surgical tertiary PICU in a university hospital.

PATIENTS

Mechanically ventilated children 0-18 years old were eligible for inclusion. Excluded were patients who were unable to initiate and maintain spontaneous breathing from any cause.

MEASUREMENTS AND MAIN RESULTS

Twenty-nine patients were studied with a total duration of 109 days. Twenty-two study days (20%) were excluded because patients were on neuromuscular blockade or high-frequency oscillatory ventilation, yielding 87 days (80%) for analysis. Patient-ventilator asynchrony was detected through analysis of daily recorded ventilator airway pressure, flow, and volume versus time scalars. Approximately one of every three breaths was asynchronous. The percentage of asynchronous breaths significantly increased over time, with the highest prevalence on the day of extubation. There was no correlation with the Comfort Behavior score. The percentage of asynchronous breaths during the first 24 hours was inversely correlated with the duration of mechanical ventilation. Patients with severe patient-ventilator asynchrony (asynchrony index > 10% or > 75th percentile of the calculated asynchrony index) did not have a prolonged duration of ventilation.

CONCLUSIONS

The level of patient-ventilator asynchrony increased over time was not related to patient discomfort and inversely related to the duration of mechanical ventilation.

COVID-19 ARDS: Points to Be Considered in Mechanical Ventilation and Weaning

The COVID-19 disease can cause hypoxemic respiratory failure due to ARDS, requiring invasive mechanical ventilation. Although early studies reported that COVID-19-associated ARDS has distinctive features from ARDS of other causes, recent observational studies have demonstrated that ARDS related to COVID-19 shares common clinical characteristics and respiratory system mechanics with ARDS of other origins. Therefore, mechanical ventilation in these patients should be based on strategies aiming to mitigate ventilator-induced lung injury. Assisted mechanical ventilation should be applied early in the course of mechanical ventilation by considering evaluation and minimizing factors associated with patient-inflicted lung injury. Extracorporeal membrane oxygenation should be considered in selected patients with refractory hypoxia not responding to conventional ventilation strategies. This review highlights the current and evolving practice in managing mechanically ventilated patients with ARDS related to COVID-19.

Refractory ineffective triggering during pressure support ventilation: effect of proportional assist ventilation with load-adjustable gain factors

Background

Ineffective triggering is frequent during pressure support ventilation (PSV) and may persist despite ventilator adjustment, leading to refractory asynchrony. We aimed to assess the effect of proportional assist ventilation with load-adjustable gain factors (PAV+) on the occurrence of refractory ineffective triggering.

Design

Observational assessment followed by prospective cross-over physiological study.

Setting

Academic medical ICU.

Patients

Ineffective triggering was detected during PSV by a twice-daily inspection of the ventilator’s screen. The impact of pressure support level (PSL) adjustments on the occurrence of asynchrony was recorded. Patients experiencing refractory ineffective triggering, defined as persisting asynchrony at the lowest tolerated PSL, were included in the physiological study.

Interventions

Physiological study: Flow, airway, and esophageal pressures were continuously recorded during 10 min under PSV with the lowest tolerated PSL, and then under PAV+ with the gain adjusted to target a muscle pressure between 5 and 10 cmH₂O.

Measurements

Primary endpoint was the comparison of asynchrony index between PSV and PAV+ after PSL and gain adjustments.

Results

Among 36 patients identified having ineffective triggering under PSV, 21 (58%) exhibited refractory ineffective triggering. The lowest tolerated PSL was higher in patients with refractory asynchrony as compared to patients with non-refractory ineffective triggering. Twelve out of the 21 patients with refractory ineffective triggering were included in the physiological study. The median lowest tolerated PSL was 17 cmH₂O [12–18] with a PEEP of 7 cmH₂O [5–8] and FiO₂ of 40% [39–42]. The median gain during PAV+ was 73% [65–80]. The asynchrony index was significantly lower during PAV+ than PSV (2.7% [1.0–5.4] vs. 22.7% [10.3–40.1], p < 0.001) and consistently decreased in every patient with PAV+. Esophageal pressure–time product (PTPes) did not significantly differ between the two modes (107 cmH₂O/s/min [79–131] under PSV vs. 149 cmH₂O/s/min [129–170] under PAV+, p = 0.092), but the proportion of PTPes lost in ineffective triggering was significantly lower with PAV+ (2 cmH₂O/s/min [1–6] vs. 8 cmH₂O/s/min [3–30], p = 0.012).

Conclusions

Among patients with ineffective triggering under PSV, PSL adjustment failed to eliminate asynchrony in 58% of them (21 of 36 patients). In these patients with refractory ineffective triggering, switching from PSV to PAV+ significantly reduced or even suppressed the incidence of asynchrony.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00935-0.

Patient-Ventilator Dyssynchrony in Critically Ill Patients

Patient–ventilator dyssynchrony is a mismatch between the patient’s respiratory efforts and mechanical ventilator delivery. Dyssynchrony can occur at any phase throughout the respiratory cycle. There are different types of dyssynchrony with different mechanisms and different potential management: trigger dyssynchrony (ineffective efforts, autotriggering, and double triggering); flow dyssynchrony, which happens during the inspiratory phase; and cycling dyssynchrony (premature cycling and delayed cycling). Dyssynchrony has been associated with patient outcomes. Thus, it is important to recognize and address these dyssynchronies at the bedside. Patient–ventilator dyssynchrony can be detected by carefully scrutinizing the airway pressure–time and flow–time waveforms displayed on the ventilator screens along with assessing the patient’s comfort. Clinicians need to know how to depict these dyssynchronies at the bedside. This review aims to define the different types of dyssynchrony and then discuss the evidence for their relationship with patient outcomes and address their potential management.

Etiology, incidence, and outcomes of patient-ventilator asynchrony in critically-ill patients undergoing invasive mechanical ventilation

Patient-ventilator asynchrony (PVA) is commonly encountered during mechanical ventilation of critically ill patients. Estimates of PVA incidence vary widely. Type, risk factors, and consequences of PVA remain unclear. We aimed to measure the incidence and identify types of PVA, characterize risk factors for development, and explore the relationship between PVA and outcome among critically ill, mechanically ventilated adult patients admitted to medical, surgical, and medical-surgical intensive care units in a large academic institution staffed with varying provider training background. A single center, retrospective cohort study of all adult critically ill patients undergoing invasive mechanical ventilation for ≥ 12 h. A total of 676 patients who underwent 696 episodes of mechanical ventilation were included. Overall PVA occurred in 170 (24%) episodes. Double triggering 92(13%) was most common, followed by flow starvation 73(10%). A history of smoking, and pneumonia, sepsis, or ARDS were risk factors for overall PVA and double triggering (all P < 0.05). Compared with volume targeted ventilation, pressure targeted ventilation decreased the occurrence of events (all P < 0.01). During volume controlled synchronized intermittent mandatory ventilation and pressure targeted ventilation, ventilator settings were associated with the incidence of overall PVA. The number of overall PVA, as well as double triggering and flow starvation specifically, were associated with worse outcomes and fewer hospital-free days (all P < 0.01). Double triggering and flow starvation are the most common PVA among critically ill, mechanically ventilated patients. Overall incidence as well as double triggering and flow starvation PVA specifically, portend worse outcome.

Flow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation

Background

The evaluation of patient effort is pivotal during pressure support ventilation, but a non-invasive, continuous, quantitative method to assess patient inspiratory effort is still lacking. We hypothesized that the concavity of the inspiratory flow-time waveform could be useful to estimate patient’s inspiratory effort. The purpose of this study was to assess whether the shape of the inspiratory flow, as quantified by a numeric indicator, could be associated with inspiratory effort during pressure support ventilation.

Methods

Twenty-four patients in pressure support ventilation were enrolled. A mathematical relationship describing the decay pattern of the inspiratory flow profile was developed. The parameter hypothesized to estimate effort was named Flow Index. Esophageal pressure, airway pressure, airflow, and volume waveforms were recorded at three support levels (maximum, minimum and baseline). The association between Flow Index and reference measures of patient effort (pressure time product and pressure generated by respiratory muscles) was evaluated using linear mixed effects models adjusted for tidal volume, respiratory rate and respiratory rate/tidal volume.

Results

Flow Index was different at the three pressure support levels and all group comparisons were statistically significant. In all tested models, Flow Index was independently associated with patient effort (p < 0.001). Flow Index prediction of inspiratory effort agreed with esophageal pressure-based methods.

Conclusions

Flow Index is associated with patient inspiratory effort during pressure support ventilation, and may provide potentially useful information for setting inspiratory support and monitoring patient-ventilator interactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03624-3.

An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation

BACKGROUND AND OBJECTIVE

Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic.

METHODS

We proposed an interpretable one-dimensional convolutional neural network (1DCNN) to detect four most manifestation types of PVA (double triggering, ineffective efforts during expiration, premature cycling and delayed cycling) under pressure control ventilation mode and pressure support ventilation mode. A global average pooling (GAP) layer was incorporated with the 1DCNN model to highlight the sections of the respiratory waveform the model focused on when making a classification. Dilation convolution and batch normalization were introduced to the 1DCNN model for compensating the reduction of performance caused by the GAP layer.

RESULTS

The proposed interpretable 1DCNN exhibited comparable performance with the state-of-the-art deep learning model in PVA detection. The F1 scores for the detection of four types of PVA under pressure control ventilation and pressure support ventilation modes were greater than 0.96. The critical sections of the waveform used to detect PVA were highlighted, and found to be well consistent with the understanding of the respective type of PVA by experts.

CONCLUSIONS

The findings suggest that the proposed 1DCNN can help detect PVA, and enhance the interpretability of the classification process to help clinicians better understand the results obtained from deep learning technology.

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, "black box" nature of algorithms and concerns about "alarm fatigue" should be addressed for more mainstream adoption of these models.

Managing patient-ventilator asynchrony with a twice-daily screening protocol: A retrospective cohort study

PURPOSE

Severe patient-ventilator asynchrony (PVA) might be associated with prolonged mechanical ventilation and mortality. It is unknown if systematic screening and application of conventional methods for PVA management can modify these outcomes. We therefore constructed a twice-daily bedside PVA screening and management protocol and investigated its effect on patient outcomes.

MATERIALS AND METHODS

A retrospective cohort study of patients who were intubated in the emergency department and directly admitted to the medical intensive care unit (ICU). In phase 1 (6 months; August 2016 to January 2017), patients received usual care comprising lung protective ventilation and moderate analgesia/sedation. In phase 2 (6 months; February 2017 to July 2017), patients were additionally managed with a PVA protocol on ICU admission and twice daily (7 am, 7 pm).

RESULTS

A total of 280 patients (160 in phase 1, 120 in phase 2) were studied (age = 64.5 ± 21.4 years, 107 women [38.2%], Acute Physiology and Chronic Health Evaluation II score = 27.1 ± 8.5, 271 [96.8%] on volume assist-control ventilation initially). Phase 2 patients had lower hospital mortality than phase 1 patients (20.0% versus 34.4%, respectively, P = 0.011), even after adjustment for age and Acute Physiology and Chronic Health Evaluation II scores (odds ratio = 0.46, 95% confidence interval = 0.25-0.84).

CONCLUSIONS

Application of a bedside PVA protocol for mechanically ventilated patients on ICU admission and twice daily was associated with decreased hospital mortality. There was however no association with sedation-free days or mechanical ventilation-free days through day 28 or length of hospital stay.

Automated detection and quantification of reverse triggering effort under mechanical ventilation

BACKGROUND

Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT.

METHODS

We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts.

RESULTS

Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH₂0, with a median of 8.7 cmH₂0. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths.

CONCLUSION

An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH₂O with important variability between and within patients.

TRIAL REGISTRATION

BEARDS, NCT03447288.

Esophageal Manometry

The estimation of pleural pressure with esophageal manometry has been used for decades, and it has been a fertile area of physiology research in healthy subject as well as during mechanical ventilation in patients with lung injury. However, its scarce adoption in clinical practice takes its roots from the (false) ideas that it requires expertise with years of training, that the values obtained are not reliable due to technical challenges or discrepant methods of calculation, and that measurement of esophageal pressure has not proved to benefit patient outcomes. Despites these criticisms, esophageal manometry could contribute to better monitoring, optimization, and personalization of mechanical ventilation from the acute initial phase to the weaning period. This review aims to provide a comprehensive but comprehensible guide addressing the technical aspects of esophageal catheter use, its application in different clinical situations and conditions, and an update on the state of the art with recent studies on this topic and on remaining questions and ways for improvement.

The Basics of Ventilator Waveforms

Purpose of Review

Knowledge of ventilator waveforms is important for clinicians working with children requiring mechanical ventilation. This review covers the basics of how to interpret and use data from ventilator waveforms in the pediatric intensive care unit.

Recent Findings

Patient-ventilator asynchrony (PVA) is a common finding in pediatric patients and observed in approximately one-third of ventilator breaths. PVA is associated with worse outcomes including increased length of mechanical ventilation, increased length of stay, and increased mortality. Identification of PVA is possible with a thorough knowledge of ventilator waveforms.

Summary

Ventilator waveforms are graphical descriptions of how a breath is delivered to a patient. These include three scalars (flow versus time, volume versus time, and pressure versus time) and two loops (pressure-volume and flow-volume). Thorough understanding of both scalars and loops, and their characteristic appearances, is essential to being able to evaluate a patient’s respiratory mechanics and interaction with the ventilator.

Patient-ventilator asynchrony in acute brain-injured patients: a prospective observational study

Background

Patient–ventilator asynchrony is common in mechanically ventilated patients and may be related to adverse outcomes. Few studies have reported the occurrence of asynchrony in brain-injured patients. We aimed to investigate the prevalence, type and severity of patient–ventilator asynchrony in mechanically ventilated patients with brain injury.

Methods

This prospective observational study enrolled acute brain-injured patients undergoing mechanical ventilation. Esophageal pressure monitoring was established after enrollment. Flow, airway pressure, and esophageal pressure–time waveforms were recorded for a 15-min interval, four times daily for 3 days, for visually detecting asynchrony by offline analysis. At the end of each dataset recording, the respiratory drive was determined by the airway occlusion maneuver. The asynchrony index was calculated to represent the severity. The relationship between the prevalence and the severity of asynchrony with ventilatory modes and settings, respiratory drive, and analgesia and sedation were determined. Association of severe patient–ventilator asynchrony, which was defined as an asynchrony index  ≥ 10%, with clinical outcomes was analyzed.

Results

In 100 enrolled patients, a total of 1076 15-min waveform datasets covering 330,292 breaths were collected, in which 70,156 (38%) asynchronous breaths were detected. Asynchrony occurred in 96% of patients with the median (interquartile range) asynchrony index of 12.4% (4.3%–26.4%). The most prevalent type was ineffective triggering. No significant difference was found in either prevalence or asynchrony index among different classifications of brain injury (p > 0.05). The prevalence of asynchrony was significantly lower during pressure control/assist ventilation than during other ventilatory modes (p < 0.05). Compared to the datasets without asynchrony, the airway occlusion pressure was significantly lower in datasets with ineffective triggering (p < 0.001). The asynchrony index was significantly higher during the combined use of opioids and sedatives (p < 0.001). Significantly longer duration of ventilation and hospital length of stay after the inclusion were found in patients with severe ineffective triggering (p < 0.05).

Conclusions

Patient–ventilator asynchrony is common in brain-injured patients. The most prevalent type is ineffective triggering and its severity is likely related to a long duration of ventilation and hospital stay. Prevalence and severity of asynchrony are associated with ventilatory modes, respiratory drive and analgesia/sedation strategy, suggesting treatment adjustment in this particular population.

Trial registration The study has been registered on 4 July 2017 in ClinicalTrials.gov (NCT03212482) (https://clinicaltrials.gov/ct2/show/NCT03212482).

Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review

Mortality associated with the acute respiratory distress syndrome remains unacceptably high due in part to ventilator-induced lung injury (VILI). Ventilator dyssynchrony is defined as the inappropriate timing and delivery of a mechanical breath in response to patient effort and may cause VILI. Such deleterious patient–ventilator interactions have recently been termed patient self-inflicted lung injury. This narrative review outlines the detection and frequency of several different types of ventilator dyssynchrony, delineates the different mechanisms by which ventilator dyssynchrony may propagate VILI, and reviews the potential clinical impact of ventilator dyssynchrony. Until recently, identifying ventilator dyssynchrony required the manual interpretation of ventilator pressure and flow waveforms. However, computerized interpretation of ventilator waive forms can detect ventilator dyssynchrony with an area under the receiver operating curve of >0.80. Using such algorithms, ventilator dyssynchrony occurs in 3%–34% of all breaths, depending on the patient population. Moreover, two types of ventilator dyssynchrony, double-triggered and flow-limited breaths, are associated with the more frequent delivery of large tidal volumes >10 mL/kg when compared with synchronous breaths (54% [95% confidence interval (CI), 47%–61%] and 11% [95% CI, 7%–15%]) compared with 0.9% (95% CI, 0.0%–1.9%), suggesting a role in propagating VILI. Finally, a recent study associated frequent dyssynchrony-defined as >10% of all breaths-with an increase in hospital mortality (67 vs. 23%, P = 0.04). However, the clinical significance of ventilator dyssynchrony remains an area of active investigation and more research is needed to guide optimal ventilator dyssynchrony management.

A machine learning method for automatic detection and classification of patient-ventilator asynchrony

Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in the intensive care unit. In current clinical practice PVA is still detected using visual inspection of the air pressure, flow, and volume curves, which is time-consuming and sensitive to subjective interpretation. Correct detection of the patient respiratory efforts is needed to properly asses the type of asynchrony. Therefore, we propose a method for automatic detection of the patient respiratory efforts using a one-dimensional convolution neural network. The proposed method was able to detect patient efforts with a sensitivity and precision of 98.6% and 97.3% for the inspiratory efforts, and 97.7% and 97.2% for the expiratory efforts. Besides allowing detection of PVA, combining the estimated timestamps of patient's inspiratory and expiratory efforts with the timings of the mechanical ventilator further allows for classification of the asynchrony type. In the future, the proposed method could support clinical decision making by informing clinicians on the quality of ventilation and providing actionable feedback for properly adjusting the ventilator settings.

Patient-Ventilator Asynchrony in Critical Care Settings: National Outcomes of Ventilator Waveform Analysis

BACKGROUND

Patient-ventilator asynchrony (PVA) is a prevalent and often underrecognized problem in mechanically ventilated patients. Ventilator waveform analysis is a noninvasive and reliable means of detecting PVAs, but the use of this tool has not been broadly studied.

METHODS

Our observational analysis leveraged a validated evaluation tool to assess the ability of critical care practitioners (CCPs) to detect different PVA types as presented in three videos. This tool consisted of three videos of common PVAs (i.e., double-triggering, auto-triggering, and ineffective triggering). Data were collected via an evaluation sheet distributed to 39 hospitals among the various CCPs, including respiratory therapists (RTs), nurses, and physicians.

RESULTS

A total of 411 CCPs were assessed; of these, only 41 (10.2%) correctly identified the three PVA types, while 92 (22.4%) correctly detected two types and 174 (42.3%) correctly detected one; 25.3% did not recognize any PVA. There were statistically significant differences between trained and untrained CCPs in terms of recognition (three PVAs, p < 0.001; two PVAs, p = 0.001). The majority of CCPs who identified one or zero PVAs were untrained, and such differences among groups were statistically significant (one PVA, p = 0.001; zero PVAs, p = 0.004). Female gender and prior training on ventilator waveforms were found to increase the odds of identifying more than two PVAs correctly, with odds ratios (ORs) (95% confidence intervals [CIs]) of 1.93 (1.07-3.49) and 5.41 (3.26-8.98), respectively. Profession, experience, and hospital characteristics were not found to correlate with increased odds of detecting PVAs; this association generally held after applying a regression model on the RT profession, with the ORs (95% CIs) of prior training (2.89 [1.28-6.51]) and female gender (2.49 [1.15-5.39]) showing the increased odds of detecting two or more PVAs.

CONCLUSION

Common PVAs detection were found low in critical care settings, with about 25% of PVA going undetected by CCPs. Female gender and prior training on ventilator graphics were the only significant predictive factors among CCPs and RTs in correctly identifying PVAs. There is an urgent need to establish teaching and training programs, policies, and guidelines vis-à-vis the early detection and management of PVAs in mechanically ventilated patients, so as to improve their outcomes.

Patient-ventilator asynchrony in conventional ventilation modes during short-term mechanical ventilation after cardiac surgery: randomized clinical trial

INTRODUCTION AND AIM

Studies regarding asynchrony in patients in the cardiac postoperative period are still only a few. The main objective of our study was to compare asynchronies incidence and its index (AI) in 3 different modes of ventilation (volume-controlled ventilation [VCV], pressure-controlled ventilation [PCV] and pressure-support ventilation [PSV]) after ICU admission for postoperative care.

METHODS

A prospective parallel randomised trialin the setting of a non-profitable hospital in Brazil. The participants were patients scheduled for cardiac surgery. Patients were randomly allocated to VCV or PCV modes of ventilation and later both groups were transitioned to PSV mode.

RESULTS

All data were recorded for 5 minutes in each of the three different phases: T1) in assisted breath, T2) initial spontaneous breath and T3) final spontaneous breath, a marking point prior to extubation. Asynchronies were detected and counted by visual inspection method by two independent investigators. Reliability, inter-rater agreement of asynchronies, asynchronies incidence, total and specific asynchrony indexes (AIt and AIspecific) and odds of AI ≥10% weighted by total asynchrony were analysed. A total of 17 patients randomly allocated to the VCV (n=9) or PCV (n=8) group completed the study. High inter-rated agreement for AIt (ICC 0.978; IC95%, 0,963-0.987) and good reliability (r=0.945; p<0.001) were found. Eighty-two % of patients presented asynchronies, although only 7% of their total breathing cycles were asynchronous. Early cycling and double triggering had the highest rates of asynchrony with no difference between groups. The highest odds of AI ≥10% were observed in VCV regardless the phase: OR 2.79 (1.36-5.73) in T1 vs T2, p=0.005; OR 2.61 (1.27-5.37) in T1 vs T3, p=0.009 and OR 4.99 (2.37-10.37) in T2 vs T3, p<0.001.

CONCLUSIONS

There was a high incidence of breathing asynchrony in postoperative cardiac patients, especially when initially ventilated in VCV. VCV group had a higher chance of AI ≥10% and this chance remained high in the following PSV phases.

Double Cycling During Mechanical Ventilation: Frequency, Mechanisms, and Physiologic Implications

OBJECTIVES

Double cycling generates larger than expected tidal volumes that contribute to lung injury. We analyzed the incidence, mechanisms, and physiologic implications of double cycling during volume- and pressure-targeted mechanical ventilation in critically ill patients.

DESIGN

Prospective, observational study.

SETTING

Three general ICUs in Spain.

PATIENTS

Sixty-seven continuously monitored adult patients undergoing volume control-continuous mandatory ventilation with constant flow, volume control-continuous mandatory ventilation with decelerated flow, or pressure control-continuous mandatory mechanical ventilation for longer than 24 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed 9,251 hours of mechanical ventilation corresponding to 9,694,573 breaths. Double cycling occurred in 0.6%. All patients had double cycling; however, the distribution of double cycling varied over time. The mean percentage (95% CI) of double cycling was higher in pressure control-continuous mandatory ventilation 0.54 (0.34-0.87) than in volume control-continuous mandatory ventilation with constant flow 0.27 (0.19-0.38) or volume control-continuous mandatory ventilation with decelerated flow 0.11 (0.06-0.20). Tidal volume in double-cycled breaths was higher in volume control-continuous mandatory ventilation with constant flow and volume control-continuous mandatory ventilation with decelerated flow than in pressure control-continuous mandatory ventilation. Double-cycled breaths were patient triggered in 65.4% and reverse triggered (diaphragmatic contraction stimulated by a previous passive ventilator breath) in 34.6% of cases; the difference was largest in volume control-continuous mandatory ventilation with decelerated flow (80.7% patient triggered and 19.3% reverse triggered). Peak pressure of the second stacked breath was highest in volume control-continuous mandatory ventilation with constant flow regardless of trigger type. Various physiologic factors, none mutually exclusive, were associated with double cycling.

CONCLUSIONS

Double cycling is uncommon but occurs in all patients. Periods without double cycling alternate with periods with clusters of double cycling. The volume of the stacked breaths can double the set tidal volume in volume control-continuous mandatory ventilation with constant flow. Gas delivery must be tailored to neuroventilatory demand because interdependent ventilator setting-related physiologic factors can contribute to double cycling. One third of double-cycled breaths were reverse triggered, suggesting that repeated respiratory muscle activation after time-initiated ventilator breaths occurs more often than expected.

Proportional modes versus pressure support ventilation: a systematic review and meta-analysis

Background

Proportional modes (proportional assist ventilation, PAV, and neurally adjusted ventilatory assist, NAVA) could improve patient–ventilator interaction and consequently may be efficient as a weaning mode. The purpose of this systematic review is to examine whether proportional modes improved patient–ventilator interaction and whether they had an impact on the weaning success and length of mechanical ventilation, in comparison with PSV.

Methods

We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials from inception through May 13, 2018. We included both parallel-group and crossover randomized studies that examined the efficacy of proportional modes in comparison with PSV in mechanically ventilated adults. The primary outcomes were (1) asynchrony index (AI), (2) weaning failure, and (3) duration of mechanical ventilation.

Results

We included 15 studies (four evaluated PAV, ten evaluated NAVA, and one evaluated both modes). Although the use of proportional modes was not associated with a reduction in AI (WMD − 1.43; 95% CI − 3.11 to 0.25; p = 0.096; PAV—one study, and NAVA—seven studies), the use of proportional modes was associated with a reduction in patients with AI > 10% (RR 0.15; 95% CI 0.04–0.58; p = 0.006; PAV—two studies, and NAVA—five studies), compared with PSV. There was a significant heterogeneity among studies for AI, especially with NAVA. Compared with PSV, use of proportional modes was associated with a reduction in weaning failure (RR 0.44; 95% CI 0.26–0.75; p = 0.003; PAV—three studies) and duration of mechanical ventilation (WMD − 1.78 days; 95% CI − 3.24 to − 0.32; p = 0.017; PAV—three studies, and NAVA—two studies). Reduced duration of mechanical ventilation was found with PAV but not with NAVA.

Conclusion

The use of proportional modes was associated with a reduction in the incidence with AI > 10%, weaning failure and duration of mechanical ventilation, compared with PSV. However, reduced weaning failure and duration of mechanical ventilation were found with only PAV. Due to a significant heterogeneity among studies and an insufficient number of studies, further investigation seems warranted to better understand the impact of proportional modes.

Clinical trial registration PROSPERO registration number, CRD42017059791. Registered 20 March 2017

Electronic supplementary material

The online version of this article (10.1186/s13613-018-0470-y) contains supplementary material, which is available to authorized users.

Patient-ventilator asynchronies: types, outcomes and nursing detection skills

BACKGROUND

Mechanical ventilation is often employed as partial ventilatory support where both the patient and the ventilator work together. The ventilator settings should be adjusted to maintain a harmonious patient-ventilator interaction. However, this balance is often altered by many factors able to generate a patient ventilator asynchrony (PVA). The aims of this review were: to identify PVAs, their typologies and classifications; to describe how and to what extent their occurrence can affect the patients' outcomes; to investigate the levels of nursing skill in detecting PVAs.

METHODS

Literature review performed on Cochrane Library, Medline and CINAHL databases.

RESULTS

1610 records were identified; 43 records were included after double blind screening. PVAs have been classified with respect to the phase of the respiratory cycle or based on the circumstance of occurrence. There is agreement on the existence of 7 types of PVAs: ineffective effort, double trigger, premature cycling, delayed cycling, reverse triggering, flow starvation and auto-cycling. PVAs can be identified through the ventilator graphics monitoring of pressure and flow waveforms.  The influence on patient outcomes varies greatly among studies but PVAs are mostly associated with poorer outcomes. Adequately trained nurses can learn and retain how to correctly detect PVAs.

CONCLUSIONS

Since its challenging interpretation and the potential advantages of its implementation, ventilator graphics monitoring can be classified as an advanced competence for ICU nurses. The knowledge and skills to adequately manage PVAs should be provided by specific post-graduate university courses.

Selecting the best ventilator hyperinflation technique based on physiologic markers: A randomized controlled crossover study

BACKGROUND

Ventilator hyperinflation (VHI) is effective in improving respiratory mechanics, secretion removal, and gas exchange in mechanically ventilated subjects; however, there are no recommendations for the best ventilator settings to perform the technique.

OBJECTIVE

To compare six modes of VHI, concerning physiological markers of efficacy and safety criteria to support the selection of optimal settings.

METHODS

Thirty mechanically ventilated patients underwent six modes of VHI in a randomized order. The delivered volume, expiratory flow bias criteria, overdistension, patient-ventilator asynchronies and hemodynamic variables were assessed during the interventions.

RESULTS

Volume-controlled ventilation with inspiratory flow of 20 lpm (VC-CMV20) and pressure support ventilation (PSV) achieved the best effectiveness scores (P < 0.05). The target peak pressure of 40 cmH₂O was associated with a high incidence of overdistension. PSV showed a lower incidence of patient-ventilator asynchronies.

CONCLUSIONS

The modes VC-CMV20 and PSV are the most effective for VHI. Alveolar overdistension and patient-ventilator asynchronies must be considered when applying VHI.