Patient-ventilator asynchrony during assisted mechanical ventilation

High Airway Occlusion Pressure Is Associated with Dyspnea and Increased Mortality in Critically Ill Mechanically Ventilated Patients

Rationale: Airway occlusion pressure at 100 ms (P0.1) reflects central respiratory drive. Objectives: We aimed to assess factors associated with P0.1 and whether an abnormally low or high P0.1 value is associated with higher mortality and longer duration of mechanical ventilation (MV). Methods: We performed a secondary analysis of a prospective cohort study conducted in 10 ICUs in France to evaluate dyspnea in communicative MV patients. In patients intubated for more than 24 hours, P0.1 was measured with dyspnea as soon as patients could communicate and the next day. Measurements and Main Results: Among 260 patients assessed after a median time of ventilation of 4 days, P0.1 was 1.9 (1-3.5) cm H2O at enrollment, 24% had P0.1 values >3.5 cm H2O, 37% had P0.1 values between 1.5 and 3.5 cm H2O, and 39% had P0.1 values <1.5 cm H2O. In multivariable linear regression, independent factors associated with P0.1 were the presence of dyspnea (P = 0.037), respiratory rate (P < 0.001), and PaO2 (P = 0.008). Ninety-day mortality was 33% in patients with P0.1 > 3.5 cm H2O versus 19% in those with P0.1 between 1.5 and 3.5 cm H2O and 17% in those with P0.1 < 1.5 cm H2O (P = 0.046). After adjustment for the main risk factors, P0.1 was associated with 90-day mortality (hazard ratio per 1 cm H2O, 1.19 [95% confidence interval, 1.04-1.37]; P = 0.011). P0.1 was also independently associated with a longer duration of MV (hazard ratio per 1 cm H2O, 1.10 [95% confidence interval, 1.02-1.19]; P = 0.016). Conclusions: In patients receiving invasive MV, abnormally high P0.1 values may suggest dyspnea and are associated with higher mortality and prolonged duration of MV.

Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit

Background

Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs.

Methods

Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians' acceptance and opinions of this system.

Results

The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform.

Conclusions

The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE

Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort.

METHODS

Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison.

RESULTS

Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN.

CONCLUSION

The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

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 .

Expiratory flow limitation during mechanical ventilation: real-time detection and physiological subtypes

BACKGROUND

Tidal expiratory flow limitation (EFLT) complicates the delivery of mechanical ventilation but is only diagnosed by performing specific manoeuvres. Instantaneous analysis of expiratory resistance (Rex) can be an alternative way to detect EFLT without changing ventilatory settings. This study aimed to determine the agreement of EFLT detection by Rex analysis and the PEEP reduction manoeuvre using contingency table and agreement coefficient. The patterns of Rex were explored.

METHODS

Medical patients ≥ 15-year-old receiving mechanical ventilation underwent a PEEP reduction manoeuvre from 5 cmH2O to zero for EFLT detection. Waveforms were recorded and analyzed off-line. The instantaneous Rex was calculated and was plotted against the volume axis, overlapped by the flow-volume loop for inspection. Lung mechanics, characteristics of the patients, and clinical outcomes were collected. The result of the Rex method was validated using a separate independent dataset.

RESULTS

339 patients initially enrolled and underwent a PEEP reduction. The prevalence of EFLT was 16.5%. EFLT patients had higher adjusted hospital mortality than non-EFLT cases. The Rex method showed 20% prevalence of EFLT and the result was 90.3% in agreement with PEEP reduction manoeuvre. In the validation dataset, the Rex method had resulted in 91.4% agreement. Three patterns of Rex were identified: no EFLT, early EFLT, associated with airway disease, and late EFLT, associated with non-airway diseases, including obesity. In early EFLT, external PEEP was less likely to eliminate EFLT.

CONCLUSIONS

The Rex method shows an excellent agreement with the PEEP reduction manoeuvre and allows real-time detection of EFLT. Two subtypes of EFLT are identified by Rex analysis.

TRIAL REGISTRATION

Clinical trial registered with www.thaiclinicaltrials.org (TCTR20190318003). The registration date was on 18 March 2019, and the first subject enrollment was performed on 26 March 2019.

The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study

BACKGROUND

Patient-ventilator asynchrony commonly occurs during pressure support ventilation (PSV). IntelliSync + software (Hamilton Medical AG, Bonaduz, Switzerland) is a new ventilation technology that continuously analyzes ventilator waveforms to detect the beginning and end of patient inspiration in real time. This study aimed to evaluate the physiological effect of IntelliSync + software on inspiratory trigger delay time, delta airway (Paw) and esophageal (Pes) pressure drop during the trigger phase, airway occlusion pressure at 0.1 s (P0.1), and hemodynamic variables.

METHODS

A randomized crossover physiologic study was conducted in 14 mechanically ventilated patients under PSV. Patients were randomly assigned to receive conventional flow trigger and cycling, inspiratory trigger synchronization (I-sync), cycle synchronization (C-sync), and inspiratory trigger and cycle synchronization (I/C-sync) for 15 min at each step. Other ventilator settings were kept constant. Paw, Pes, airflow, P0.1, respiratory rate, SpO2, and hemodynamic variables were recorded. The primary outcome was inspiratory trigger and cycle delay time between each intervention. Secondary outcomes were delta Paw and Pes drop during the trigger phase, P0.1, SpO2, and hemodynamic variables.

RESULTS

The time to initiate the trigger was significantly shorter with I-sync compared to baseline (208.9±91.7 vs. 301.4±131.7 msec; P = 0.002) and I/C-sync compared to baseline (222.8±94.0 vs. 301.4±131.7 msec; P = 0.005). The I/C-sync group had significantly lower delta Paw and Pes drop during the trigger phase compared to C-sync group (-0.7±0.4 vs. -1.2±0.8 cmH2O; P = 0.028 and - 1.8±2.2 vs. -2.8±3.2 cmH2O; P = 0.011, respectively). No statistically significant differences were found in cycle delay time, P0.1 and other physiological variables between the groups.

CONCLUSIONS

IntelliSync + software reduced inspiratory trigger delay time compared to the conventional flow trigger system during PSV mode. However, no significant improvements in cycle delay time and other physiological variables were observed with IntelliSync + software.

TRIAL REGISTRATION

This study was registered in the Thai Clinical Trial Registry (TCTR20200528003; date of registration 28/05/2020).

Failing with Cyanosis-Heart Failure in End-Stage Unrepaired or Partially Palliated Congenital Heart Disease

Heart failure in cyanotic congenital heart disease (CHD) is diagnosed clinically rather than relying solely on ventricular function assessments. Patients with cyanosis often present with clinical features indicative of heart failure. Although myocardial injury and dysfunction likely contribute to cyanotic CHD, the primary concern is the reduced delivery of oxygen to tissues. Symptoms such as fatigue, lassitude, dyspnea, headaches, myalgias, and a cold sensation underscore inadequate tissue oxygen delivery, forming the basis for defining heart failure in cyanotic CHD. Thus, it is pertinent to delve into the components of oxygen delivery in this context.

Invasive Mechanical Ventilation

Invasive mechanical ventilation allows clinicians to support gas exchange and work of breathing in patients with respiratory failure. However, there is also potential for iatrogenesis. By understanding the benefits and limitations of different modes of ventilation and goals for gas exchange, clinicians can choose a strategy that provides appropriate support while minimizing harm. The ventilator can also provide crucial diagnostic information in the form of respiratory mechanics. These, and the mechanical ventilation strategy, should be regularly reassessed.

Electrical Impedance Tomography to Monitor Hypoxemic Respiratory Failure

Hypoxemic respiratory failure is one of the leading causes of mortality in intensive care. Frequent assessment of individual physiological characteristics and delivery of personalized mechanical ventilation (MV) settings is a constant challenge for clinicians caring for these patients. Electrical impedance tomography (EIT) is a radiation-free bedside monitoring device that is able to assess regional lung ventilation and changes in aeration. With real-time tomographic functional images of the lungs obtained through a thoracic belt, clinicians can visualize and estimate the distribution of ventilation at different ventilation settings or following procedures such as prone positioning. Several studies have evaluated the performance of EIT to monitor the effects of different MV settings in patients with acute respiratory distress syndrome, allowing more personalized MV. For instance, EIT could help clinicians find the positive end-expiratory pressure that represents a compromise between recruitment and overdistension and assess the effect of prone positioning on ventilation distribution. The clinical impact of the personalization of MV remains to be explored. Despite inherent limitations such as limited spatial resolution, EIT also offers a unique noninvasive bedside assessment of regional ventilation changes in the ICU. This technology offers the possibility of a continuous, operator-free diagnosis and real-time detection of common problems during MV. This review provides an overview of the functioning of EIT, its main indices, and its performance in monitoring patients with acute respiratory failure. Future perspectives for use in intensive care are also addressed.

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.

Divergent expiratory braking activity of costal and crural diaphragm

BACKGROUND

There is increasing clinical interest in understanding the contribution of the diaphragm in early expiration, especially during mechanical ventilation. However, current experimental evidence is limited, so essential activity of the diaphragm during expiration and diaphragm segmental differences in expiratory activity, are unknown.

OBJECTIVES

To determine if: 1) the diaphragm is normally active into expiration during spontaneous breathing and hypercapnic ventilation, 2) expiratory diaphragmatic activity is distributed equally among the segments of the diaphragm, costal and crural.

METHODS

In 30 spontaneously breathing male and female canines, awake without confounding anesthetic, we measured directly both inspiratory and expiratory electrical activity (EMG), and corresponding mechanical shortening, of costal and crural diaphragm, during room air and hypercapnia.

RESULTS

During eupnea, costal and crural diaphragm are active into expiration, showing significant and distinct expiratory activity, with crural expiratory activity greater than costal, for both magnitude and duration. This diaphragm segmental difference diverged further during progressive hypercapnic ventilation: crural expiratory activity progressively increased, while costal expiratory activity disappeared.

CONCLUSION

The diaphragm is not passive during expiration. During spontaneous breathing, expiratory activity -"braking"- of the diaphragm is expressed routinely, but is not equally distributed. Crural muscle "braking" is greater than costal muscle in magnitude and duration. With increasing ventilation during hypercapnia, expiratory activity -"braking"- diverges notably. Crural expiratory activity greatly increases, while costal expiratory "braking" decreases in magnitude and duration, and disappears. Thus, diaphragm expiratory "braking" action represents an inherent, physiological function of the diaphragm, distinct for each segment, expressing differing neural activation.

Automated characterization of patient-ventilator interaction using surface electromyography

BACKGROUND

Characterizing patient-ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient.

METHODS

In this study, we recorded surface electromyography ([Formula: see text]) signals from the diaphragm and intercostal muscles and esophageal pressure ([Formula: see text]) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in [Formula: see text], blinded toward each other, the [Formula: see text] signals, and the algorithmic results. We also classified patient-ventilator interaction and calculated asynchrony indices with manually detected inspirations in [Formula: see text] as a reference for automated asynchrony classification and asynchrony index calculation.

RESULTS

Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in [Formula: see text] demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in [Formula: see text]. The average delay of automatically detected inspiratory onset to the [Formula: see text] reference was [Formula: see text]79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of [Formula: see text] to the [Formula: see text]-based reference.

CONCLUSIONS

Our study demonstrates the feasibility of automating the quantification of patient-ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient-ventilator interaction.

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

BACKGROUND

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.

METHODS

An observational study was conducted in 23 babies randomly selected from 170 neonates who were ventilated with SIPPV-VG, SIMV-VG or PSV-VG mode for at least 12 h. 500 breaths were randomly selected and manually annotated from each recording to train convolutional neural network (CNN) models for PVI classification.

RESULTS

The average asynchrony index (AI) over all recordings was 52.5%. The most frequently occurring PVIs included expiratory work (median: 28.4%, interquartile range: 23.2-40.2%), late cycling (7.6%, 2.8-10.2%), failed triggering (4.6%, 1.2-6.2%) and late triggering (4.4%, 2.8-7.4%). Approximately 25% of breaths with a PVI had two or more PVIs occurring simultaneously. Binary CNN classifiers were developed for PVIs affecting ≥1% of all breaths (n = 7) and they achieved F1 scores of >0.9 on the test set except for early triggering where it was 0.809.

CONCLUSIONS

PVIs occur frequently in neonates undergoing conventional mechanical ventilation with a significant proportion of breaths containing multiple PVIs. We have developed computational models for seven different PVIs to facilitate automated detection and further evaluation of their clinical significance in neonates.

IMPACT

The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses. By adapting a recent taxonomy of PVI definitions in adults, we have manually annotated neonatal ventilator waveforms to determine prevalence and co-occurrence of neonatal PVIs. We have also developed binary deep learning classifiers for common PVIs to facilitate their automatic detection and quantification.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations

BACKGROUND

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery.

AIM

This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them.

METHODOLOGY

Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties.

RESULTS

The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare.

CONCLUSION

Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.

Understanding the mechanisms of ventilator-induced lung injury using animal models

Mechanical ventilation is a life-saving therapy in several clinical situations, promoting gas exchange and providing rest to the respiratory muscles. However, mechanical ventilation may cause hemodynamic instability and pulmonary structural damage, which is known as ventilator-induced lung injury (VILI). The four main injury mechanisms associated with VILI are as follows: barotrauma/volutrauma caused by overstretching the lung tissues; atelectrauma, caused by repeated opening and closing of the alveoli resulting in shear stress; and biotrauma, the resulting biological response to tissue damage, which leads to lung and multi-organ failure. This narrative review elucidates the mechanisms underlying the pathogenesis, progression, and resolution of VILI and discusses the strategies that can mitigate VILI. Different static variables (peak, plateau, and driving pressures, positive end-expiratory pressure, and tidal volume) and dynamic variables (respiratory rate, airflow amplitude, and inspiratory time fraction) can contribute to VILI. Moreover, the potential for lung injury depends on tissue vulnerability, mechanical power (energy applied per unit of time), and the duration of that exposure. According to the current evidence based on models of acute respiratory distress syndrome and VILI, the following strategies are proposed to provide lung protection: keep the lungs partially collapsed (SaO2 > 88%), avoid opening and closing of collapsed alveoli, and gently ventilate aerated regions while keeping collapsed and consolidated areas at rest. Additional mechanisms, such as subject-ventilator asynchrony, cumulative power, and intensity, as well as the damaging threshold (stress-strain level at which tidal damage is initiated), are under experimental investigation and may enhance the understanding of VILI.

Asynchrony Injures Lung and Diaphragm in Acute Respiratory Distress Syndrome

OBJECTIVES

Patient-ventilator asynchrony is often observed during mechanical ventilation and is associated with higher mortality. We hypothesized that patient-ventilator asynchrony causes lung and diaphragm injury and dysfunction.

DESIGN

Prospective randomized animal study.

SETTING

University research laboratory.

SUBJECTS

Eighteen New Zealand White rabbits.

INTERVENTIONS

Acute respiratory distress syndrome (ARDS) model was established by depleting surfactants. Each group (assist control, breath stacking, and reverse triggering) was simulated by phrenic nerve stimulation. The effects of each group on lung function, lung injury (wet-to-dry lung weight ratio, total protein, and interleukin-6 in bronchoalveolar lavage), diaphragm function (diaphragm force generation curve), and diaphragm injury (cross-sectional area of diaphragm muscle fibers, histology) were measured. Diaphragm RNA sequencing was performed using breath stacking and assist control ( n = 2 each).

MEASUREMENTS AND MAIN RESULTS

Inspiratory effort generated by phrenic nerve stimulation was small and similar among groups (esophageal pressure swing ≈ -2.5 cm H 2 O). Breath stacking resulted in the largest tidal volume (>10 mL/kg) and highest inspiratory transpulmonary pressure, leading to worse oxygenation, worse lung compliance, and lung injury. Reverse triggering did not cause lung injury. No asynchrony events were observed in assist control, whereas eccentric contractions occurred in breath stacking and reverse triggering, but more frequently in breath stacking. Breath stacking and reverse triggering significantly reduced diaphragm force generation. Diaphragmatic histology revealed that the area fraction of abnormal muscle was ×2.5 higher in breath stacking (vs assist control) and ×2.1 higher in reverse triggering (vs assist control). Diaphragm RNA sequencing analysis revealed that genes associated with muscle differentiation and contraction were suppressed, whereas cytokine- and chemokine-mediated proinflammatory responses were activated in breath stacking versus assist control.

CONCLUSIONS

Breath stacking caused lung and diaphragm injury, whereas reverse triggering caused diaphragm injury. Thus, careful monitoring and management of patient-ventilator asynchrony may be important to minimize lung and diaphragm injury from spontaneous breathing in ARDS.

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.

A Novel Application of Spectrograms with Machine Learning Can Detect Patient Ventilator Dyssynchrony

Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respiratory distress syndrome. Waveforms of ventilator flow, airway pressure and esophageal manometry were annotated to identify DC breaths and underlying PVD subtypes. Spectrograms were generated from those waveforms to train Convolutional Neural Network (CNN) models in detecting DC and underlying PVD subtypes: Reverse Trigger (RT) and Inadequate Support (IS). The DC breath detection model yielded AUROC of 0.980, while the multi-target detection model for underlying dyssynchrony yielded AUROC of 0.980 (RT) and 0.976 (IS). When operating at 75% sensitivity, DC breath detection had a number needed to alert (NNA) 1.3 (99% specificity), while underlying PVD had a NNA 1.6 (98.5% specificity) for RT and NNA 4.0 (98.2% specificity) for IS. CNNs using spectrograms of ventilator waveforms can identify DC breaths and detect the underlying PVD for targeted clinical interventions.

Management of severe acute respiratory distress syndrome: a primer

This narrative review explores the physiology and evidence-based management of patients with severe acute respiratory distress syndrome (ARDS) and refractory hypoxemia, with a focus on mechanical ventilation, adjunctive therapies, and veno-venous extracorporeal membrane oxygenation (V-V ECMO). Severe ARDS cases increased dramatically worldwide during the Covid-19 pandemic and carry a high mortality. The mainstay of treatment to improve survival and ventilator-free days is proning, conservative fluid management, and lung protective ventilation. Ventilator settings should be individualized when possible to improve patient-ventilator synchrony and reduce ventilator-induced lung injury (VILI). Positive end-expiratory pressure can be individualized by titrating to best respiratory system compliance, or by using advanced methods, such as electrical impedance tomography or esophageal manometry. Adjustments to mitigate high driving pressure and mechanical power, two possible drivers of VILI, may be further beneficial. In patients with refractory hypoxemia, salvage modes of ventilation such as high frequency oscillatory ventilation and airway pressure release ventilation are additional options that may be appropriate in select patients. Adjunctive therapies also may be applied judiciously, such as recruitment maneuvers, inhaled pulmonary vasodilators, neuromuscular blockers, or glucocorticoids, and may improve oxygenation, but do not clearly reduce mortality. In select, refractory cases, the addition of V-V ECMO improves gas exchange and modestly improves survival by allowing for lung rest. In addition to VILI, patients with severe ARDS are at risk for complications including acute cor pulmonale, physical debility, and neurocognitive deficits. Even among the most severe cases, ARDS is a heterogeneous disease, and future studies are needed to identify ARDS subgroups to individualize therapies and advance care.

Interventions Relieving Dyspnea in Intubated Patients Show Responsiveness of the Mechanical Ventilation-Respiratory Distress Observation Scale

Rationale: Breathing difficulties are highly stressful. In critically ill patients, they are associated with an increased risk of posttraumatic manifestations. Dyspnea, the corresponding symptom, cannot be directly assessed in noncommunicative patients. This difficulty can be circumvented using observation scales such as the mechanical ventilation-respiratory distress observation scale (MV-RDOS). Objective: To investigate the performance and responsiveness of the MV-RDOS to infer dyspnea in noncommunicative intubated patients. Methods: Communicative and noncommunicative patients exhibiting breathing difficulties under mechanical ventilation were prospectively included and assessed using a dyspnea visual analog scale, MV-RDOS, EMG activity of alae nasi and parasternal intercostals, and EEG signatures of respiratory-related cortical activation (preinspiratory potentials). Inspiratory-muscle EMG and preinspiratory cortical activities are surrogates of dyspnea. Assessments were conducted at baseline, after adjustment of ventilator settings, and, in some cases, after morphine administration. Measurements and Main Results: Fifty patients (age, 67 [(interquartile interval [IQR]), 61-76] yr; Simplified Acute Physiology Score II, 52 [IQR, 35-62]) were included, 25 of whom were noncommunicative. Relief occurred in 25 (50%) patients after ventilator adjustments and in 21 additional patients after morphine administration. In noncommunicative patients, MV-RDOS score decreased from 5.5 (IQR, 4.2-6.6) at baseline to 4.2 (IQR, 2.1-4.7; P < 0.001) after ventilator adjustments and 2.5 (IQR, 2.1-4.2; P = 0.024) after morphine administration. MV-RDOS and alae nasi/parasternal EMG activities were positively correlated (ρ = 0.41 and 0.37, respectively). MV-RDOS scores were higher in patients with EEG preinspiratory potentials (4.9 [IQR, 4.2-6.3] vs. 4.0 [IQR, 2.1-4.9]; P = 0.002). Conclusions: The MV-RDOS seems able to detect and monitor respiratory symptoms reasonably well in noncommunicative intubated patients. Clinical trial registered with www.clinicaltrials.gov (NCT02801838).

Managing respiratory muscle weakness during weaning from invasive ventilation

Weaning is a critical stage of an intensive care unit (ICU) stay, in which the respiratory muscles play a major role. Weakness of the respiratory muscles, which is associated with significant morbidity in the ICU, is not limited to atrophy and subsequent dysfunction of the diaphragm; the extradiaphragmatic inspiratory and expiratory muscles also play important parts. In addition to the well-established deleterious effect of mechanical ventilation on the respiratory muscles, other risk factors such as sepsis may be involved. Weakness of the respiratory muscles can be suspected visually in a patient with paradoxical movement of the abdominal compartment. Measurement of maximal inspiratory pressure is the simplest way to assess respiratory muscle function, but it does not specifically take the diaphragm into account. A cut-off value of -30 cmH₂O could identify patients at risk for prolonged ventilatory weaning; however, ultrasound may be better for assessing respiratory muscle function in the ICU. Although diaphragm dysfunction has been associated with weaning failure, this diagnosis should not discourage clinicians from performing spontaneous breathing trials and considering extubation. Recent therapeutic developments aimed at preserving or restoring respiratory muscle function are promising.

Linking Acute Physiology to Outcomes in the ICU: Challenges and Solutions for Research

ICU clinicians rely on bedside physiological measurements to inform many routine clinical decisions. Because deranged physiology is usually associated with poor clinical outcomes, it is tempting to hypothesize that manipulating and intervening on physiological parameters might improve outcomes for patients. However, testing these hypotheses through mathematical models of the relationship between physiology and outcomes presents a number of important methodological challenges. These models reflect the theories of the researcher and can therefore be heavily influenced by one's assumptions and background beliefs. Model building must therefore be approached with great care and forethought, because failure to consider relevant sources of measurement error, confounding, coupling, and time dependency or failure to assess the direction of causality for associations of interest before modeling may give rise to spurious results. This paper outlines the main challenges in analyzing and interpreting these models and offers potential solutions to address these challenges.

Influence of Mechanical Ventilation Modes on the Efficacy of Nebulized Bronchodilators in the Treatment of Intubated Adult Patients with Obstructive Pulmonary Disease

BACKGROUND

Little has been reported in terms of clinical outcomes to confirm the benefits of nebulized bronchodilators during mechanical ventilation (MV). Electrical Impedance Tomography (EIT) could be a valuable method to elucidate this gap.

OBJECTIVE

The purpose of this study is to evaluate the impact of nebulized bronchodilators during invasive MV with EIT by comparing three ventilation modes on the overall and regional lung ventilation and aeration in critically ill patients with obstructive pulmonary disease.

METHOD

A blind clinical trial in which eligible patients underwent nebulization with salbutamol sulfate (5 mg/1 mL) and ipratropium bromide (0.5 mg/2 mL) in the ventilation mode they were receiving. EIT evaluation was performed before and after the intervention. A joint and stratified analysis into ventilation mode groups was performed, with p < 0.05.

RESULTS

Five of nineteen procedures occurred in controlled MV mode, seven in assisted mode and seven in spontaneous mode. In the intra-group analysis, the nebulization increased total ventilation in controlled (p = 0.04 and ⅆ = 2) and spontaneous (p = 0.01 and ⅆ = 1.5) MV modes. There was an increase in the dependent pulmonary region in assisted mode (p = 0.01 and ⅆ = 0.3) and in spontaneous mode (p = 0.02 and ⅆ = 1.6). There was no difference in the intergroup analysis.

CONCLUSIONS

Nebulized bronchodilators reduce the aeration of non-dependent pulmonary regions and increase overall lung ventilation but there was no difference between the ventilation modes. As a limitation, it is important to note that the muscular effort in PSV and A/C PCV modes influences the impedance variation, and consequently the aeration and ventilation values. Thus, future studies are needed to evaluate this effort as well as the time on ventilator, time in UCI and other variables.

Respiratory effort during noninvasive positive pressure ventilation and continuous positive airway pressure in severe acute viral bronchiolitis

OBJECTIVES

To assess if noninvasive positive pressure ventilation (NIPPV) is associated with a greater reduction in respiratory effort as compared to continuous positive airway pressure (CPAP) during severe acute bronchiolitis, with both supports set either clinically or physiologically.

METHODS

Twenty infants (median [IQR] age 1.2 [0.9; 3.2] months) treated <24 h with noninvasive respiratory support (CPAP Clin, set at 7 cmH₂ O, or NIPPV Clin) for bronchiolitis were included in a prospective single-center crossover study. Esogastric pressures were measured first with the baseline support, then with the other support. For each support, recordings were performed with the clinical setting and a physiological setting (CPAP Phys and NIPPV Phys), aiming at normalising respiratory effort. Patients were then treated with the optimal support. The primary outcome was the greatest reduction in esophageal pressure-time product (PTP_ES /min). Other outcomes included improvement of the other components of the respiratory effort.

RESULTS

NIPPV Clin and Phys were associated with a lower PTP_ES /min (164 [105; 202] and 106 [78; 161] cmH₂ O s/min, respectively) than CPAP Clin (178 [145; 236] cmH₂ O s/min; p = 0.01 and 2 × 10^-4 , respectively). NIPPV Clin and Phys were also associated with a significant reduction of all other markers of respiratory effort as compared to CPAP Clin. PTP_ES /min with NIPPV (Clin or Phys) was not different from PTP_ES /min with CPAP Phys. There was no significant difference between physiological and clinical settings.

CONCLUSION

NIPPV is associated with a significant reduction in respiratory effort as compared to CPAP set at +7 cmH₂ O in infants with severe acute bronchiolitis. CPAP Phys performs as well as NIPPV Clin.

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.

Study protocol for a randomized controlled trial of Proportional Assist Ventilation for Minimizing the Duration of Mechanical Ventilation: the PROMIZING study

BACKGROUND

Proportional assist ventilation with load-adjustable gain factors (PAV+) is a mechanical ventilation mode that delivers assistance to breathe in proportion to the patient's effort. The proportional assistance, called the gain, can be adjusted by the clinician to maintain the patient's respiratory effort or workload within a normal range. Short-term and physiological benefits of this mode compared to pressure support ventilation (PSV) include better patient-ventilator synchrony and a more physiological response to changes in ventilatory demand.

METHODS

The objective of this multi-centre randomized controlled trial (RCT) is to determine if, for patients with acute respiratory failure, ventilation with PAV+ will result in a shorter time to successful extubation than with PSV. This multi-centre open-label clinical trial plans to involve approximately 20 sites in several continents. Once eligibility is determined, patients must tolerate a short-term PSV trial and either (1) not meet general weaning criteria or (2) fail a 2-min Zero Continuous Positive Airway Pressure (CPAP) Trial using the rapid shallow breathing index, or (3) fail a spontaneous breathing trial (SBT), in this sequence. Then, participants in this study will be randomized to either PSV or PAV+ in a 1:1 ratio. PAV+ will be set according to a target of muscular pressure. The weaning process will be identical in the two arms. Time to liberation will be the primary outcome; ventilator-free days and other outcomes will be measured.

DISCUSSION

Meta-analyses comparing PAV+ to PSV suggest PAV+ may benefit patients and decrease healthcare costs but no powered study to date has targeted the difficult to wean patient population most likely to benefit from the intervention, or used consistent timing for the implementation of PAV+. Our enrolment strategy, primary outcome measure, and liberation approaches may be useful for studying mechanical ventilation and weaning and can offer important results for patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02447692 . Prospectively registered on May 19, 2015.

Diaphragm Ultrasound in Critically Ill Patients on Mechanical Ventilation—Evolving Concepts

Mechanical ventilation (MV) is a life-saving respiratory support therapy, but MV can lead to diaphragm muscle injury (myotrauma) and induce diaphragmatic dysfunction (DD). DD is relevant because it is highly prevalent and associated with significant adverse outcomes, including prolonged ventilation, weaning failures, and mortality. The main mechanisms involved in the occurrence of myotrauma are associated with inadequate MV support in adapting to the patient’s respiratory effort (over- and under-assistance) and as a result of patient-ventilator asynchrony (PVA). The recognition of these mechanisms associated with myotrauma forced the development of myotrauma prevention strategies (MV with diaphragm protection), mainly based on titration of appropriate levels of inspiratory effort (to avoid over- and under-assistance) and to avoid PVA. Protecting the diaphragm during MV therefore requires the use of tools to monitor diaphragmatic effort and detect PVA. Diaphragm ultrasound is a non-invasive technique that can be used to monitor diaphragm function, to assess PVA, and potentially help to define diaphragmatic effort with protective ventilation. This review aims to provide clinicians with an overview of the relevance of DD and the main mechanisms underlying myotrauma, as well as the most current strategies aimed at minimizing the occurrence of myotrauma with special emphasis on the role of ultrasound in monitoring diaphragm function.

Driving Pressure, Elastance, and Outcomes in a Real-World Setting: A Bi-Center Analysis of Electronic Health Record Data

Emerging evidence suggests the potential importance of inspiratory driving pressure (DP) and respiratory system elastance (E_RS) on outcomes among patients with the acute respiratory distress syndrome. Their association with outcomes among heterogeneous populations outside of a controlled clinical trial is underexplored. We used electronic health record (EHR) data to characterize the associations of DP and E_RS with clinical outcomes in a real-world heterogenous population.

DESIGN

Observational cohort study.

SETTING

Fourteen ICUs in two quaternary academic medical centers.

PATIENTS

Adult patients who received mechanical ventilation for more than 48 hours and less than 30 days.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

EHR data from 4,233 ventilated patients from 2016 to 2018 were extracted, harmonized, and merged. A minority of the analytic cohort (37%) experienced a Pao₂/Fio₂ of less than 300. A time-weighted mean exposure was calculated for ventilatory variables including tidal volume (V_T), plateau pressures (P_PLAT), DP, and E_RS. Lung-protective ventilation adherence was high (94% with V_T < 8.5 mL/kg, time-weighted mean V_T = 6. 8 mL/kg, 88% with P_PLAT ≤ 30 cm H₂O). Although time-weighted mean DP (12.2 cm H₂O) and E_RS (1.9 cm H₂O/[mL/kg]) were modest, 29% and 39% of the cohort experienced a DP greater than 15 cm H₂O or an E_RS greater than 2 cm H₂O/(mL/kg), respectively. Regression modeling with adjustment for relevant covariates determined that exposure to time-weighted mean DP (> 15 cm H₂O) was associated with increased adjusted risk of mortality and reduced adjusted ventilator-free days independent of adherence to lung-protective ventilation. Similarly, exposure to time-weighted mean E_RS greater than 2 cm H₂O/(mL/kg) was associated with increased adjusted risk of mortality.

CONCLUSIONS

Elevated DP and E_RS are associated with increased risk of mortality among ventilated patients independent of severity of illness or oxygenation impairment. EHR data can enable assessment of time-weighted ventilator variables and their association with clinical outcomes in a multicenter real-world setting.

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.

An implantable soft robotic ventilator augments inspiration in a pig model of respiratory insufficiency

Severe diaphragm dysfunction can lead to respiratory failure and to the need for permanent mechanical ventilation. Yet permanent tethering to a mechanical ventilator through the mouth or via tracheostomy can hinder a patient's speech, swallowing ability and mobility. Here we show, in a porcine model of varied respiratory insufficiency, that a contractile soft robotic actuator implanted above the diaphragm augments its motion during inspiration. Synchronized actuation of the diaphragm-assist implant with the native respiratory effort increased tidal volumes and maintained ventilation flow rates within the normal range. Robotic implants that intervene at the diaphragm rather than at the upper airway and that augment physiological metrics of ventilation may restore respiratory performance without sacrificing quality of life.

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

PURPOSE

No study has compared neurally adjusted ventilator assist (NAVA) with adaptive support ventilation (ASV) during non-invasive ventilation (NIV) in subjects with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

MATERIALS AND METHODS

In this randomized controlled trial, we compared NAVA-NIV with ASV-NIV for delivering NIV in consecutive subjects with AECOPD. The primary outcome was NIV failure rate (invasive mechanical ventilation). The key secondary outcomes were number of NIV manipulations, asynchrony index, and 90-day mortality.

RESULTS

We enrolled 76 subjects (NAVA-NIV, n = 36, ASV-NIV, n = 40; 74% males) with a mean ± SD age of 61.4 ± 8.2 years. We found no difference in NIV failure rates between the two arms (NAVA-NIV vs. ASV-NIV; 8/36 [22.2%] vs. 8/40 [20%]; p = 0.83). The median physician manipulations for NIV were significantly less in the ASV-NIV arm than in the NAVA-NIV arm (2 [0.8-4] vs. 3 [2-5]; p= 0.014) during the initial 24-h. We found no difference in median asynchrony index (NAVA-NIV vs. ASV-NIV, 16.6% vs. 16.4%, p = 0.5) and 90-day mortality (22.2% vs. 17.5%, p = 0.67).

CONCLUSION

The use of NAVA-NIV was not superior to ASV-NIV in reducing NIV failure rates in AECOPD. Both NAVA-NIV and ASV-NIV had similar asynchrony index and 90-day mortality.

TRIAL REGISTRY

www.

CLINICALTRIALS

gov (NCT04414891).

Monitoring the patient-ventilator asynchrony during non-invasive ventilation

Patient-ventilator asynchrony is a major issue during non-invasive ventilation and may lead to discomfort and treatment failure. Therefore, the identification and prompt management of asynchronies are of paramount importance during non-invasive ventilation (NIV), in both pediatric and adult populations. In this review, we first define the different forms of asynchronies, their classification, and the method of quantification. We, therefore, describe the technique to properly detect patient-ventilator asynchronies during NIV in pediatric and adult patients with acute respiratory failure, separately. Then, we describe the actions that can be implemented in an attempt to reduce the occurrence of asynchronies, including the use of non-conventional modes of ventilation. In the end, we analyzed what the literature reports on the impact of asynchronies on the clinical outcomes of infants, children, and adults.

Advantages and drawbacks of helmet noninvasive support in acute respiratory failure

INTRODUCTION

Non-invasive ventilation (NIV) represents an effective strategy for managing acute respiratory failure. Facemask NIV is strongly recommended in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) with hypercapnia and acute cardiogenic pulmonary edema (ACPE). Its role in managing acute hypoxemic respiratory failure (AHRF) remains a debated issue. NIV and continuous positive airway pressure (CPAP) delivered through the helmet are recently receiving growing interest for AHRF management.

AREAS COVERED

In this narrative review, we discuss the clinical applications of helmet support compared to the other available noninvasive strategies in the different phenotypes of acute respiratory failure.

EXPERT OPINION

Helmets enable the use of high positive end-expiratory pressure, which may protect from self-inflicted lung injury: in AHRF, the possible superiority of helmet support over other noninvasive strategies in terms of clinical outcome has been hypothesized in a network metanalysis and a randomized trial, but has not been confirmed by other investigations and warrants confirmation. In AECOPD patients, helmet efficacy may be inferior to that of face masks, and its use prompts caution due to the risk of CO₂ rebreathing. Helmet support can be safely applied in hypoxemic patients with ACPE, with no advantages over facemasks.

Effect of patient-ventilator asynchrony on lung and diaphragmatic injury in experimental acute respiratory distress syndrome in a porcine model

BACKGROUND

Patient-ventilator asynchrony during mechanical ventilation may exacerbate lung and diaphragm injury in spontaneously breathing subjects. We investigated whether subject-ventilator asynchrony increases lung or diaphragmatic injury in a porcine model of acute respiratory distress syndrome (ARDS).

METHODS

ARDS was induced in adult female pigs by lung lavage and injurious ventilation before mechanical ventilation by pressure assist-control for 12 h. Mechanically ventilated pigs were randomised to breathe spontaneously with or without induced subject-ventilator asynchrony or neuromuscular block (n=7 per group). Subject-ventilator asynchrony was produced by ineffective, auto-, or double-triggering of spontaneous breaths. The primary outcome was mean alveolar septal thickness (where thickening of the alveolar wall indicates worse lung injury). Secondary outcomes included distribution of ventilation (electrical impedance tomography), lung morphometric analysis, inflammatory biomarkers (gene expression), lung wet-to-dry weight ratio, and diaphragmatic muscle fibre thickness.

RESULTS

Subject-ventilator asynchrony (median [interquartile range] 28.8% [10.4] asynchronous breaths of total breaths; n=7) did not increase mean alveolar septal thickness compared with synchronous spontaneous breathing (asynchronous breaths 1.0% [1.6] of total breaths; n=7). There was no difference in mean alveolar septal thickness throughout upper and lower lung lobes between pigs randomised to subject-ventilator asynchrony vs synchronous spontaneous breathing (87.3-92.2 μm after subject-ventilator asynchrony, compared with 84.1-95.0 μm in synchronised spontaneous breathing;). There were also no differences between groups in wet-to-dry weight ratio, diaphragmatic muscle fibre thickness, atelectasis, lung aeration, or mRNA expression levels for inflammatory cytokines pivotal in ARDS pathogenesis.

CONCLUSIONS

Subject-ventilator asynchrony during spontaneous breathing did not exacerbate lung injury and dysfunction in experimental porcine ARDS.

Experimental asynchrony to study self-inflicted lung injury

Patient self-inflicted lung injury may be associated with worse clinical outcomes and higher mortality. Patient-ventilator asynchrony is associated with increased ventilator days and mortality, and it has been hypothesised as one of the important mechanisms leading to patient self-inflicted lung injury. However, given the observational nature of the key studies in the field so far, the hypothesis that patient-ventilator asynchrony causes patient self-inflicted lung injury has not been supported by evidence yet. Wittenstein and colleagues present a novel approach that enables controlling patient-ventilator asynchrony in a pig model of acute lung injury, to investigate the patient-ventilator asynchrony and patient self-inflicted lung injury causality. Their results suggest that increased patient-ventilator asynchrony associated with poor clinical outcomes reported in observational trials could be a marker, rather than a cause of patient self-inflicted lung injury. These findings on their own are not sufficient to justify a greater tolerance of patient-ventilator asynchrony amongst clinicians, a change for which further experimental work and clinical evidence is needed.

Driving pressure-guided ventilation decreases the mechanical power compared to predicted body weight-guided ventilation in the Acute Respiratory Distress Syndrome

Background

Whether targeting the driving pressure (∆P) when adjusting the tidal volume in mechanically ventilated patients with the acute respiratory distress syndrome (ARDS) may decrease the risk of ventilator-induced lung injury remains a matter of research. In this study, we assessed the effect of a ∆P-guided ventilation on the mechanical power.

Methods

We prospectively included adult patients with moderate-to-severe ARDS. Positive end expiratory pressure was set by the attending physician and kept constant during the study. Tidal volume was first adjusted to target 6 ml/kg of predicted body weight (PBW-guided ventilation) and subsequently modified within a range from 4 to 10 ml/kg PBW to target a ∆P between 12 and 14 cm H₂O. The respiratory rate was then re-adjusted within a range from 12 to 40 breaths/min until EtCO₂ returned to its baseline value (∆P-guided ventilation). Mechanical power was computed at each step.

Results

Fifty-one patients were included between December 2019 and May 2021. ∆P-guided ventilation was feasible in all but one patient. The ∆P during PBW-guided ventilation was already within the target range of ∆P-guided ventilation in five (10%) patients, above in nine (18%) and below in 36 (72%). The change from PBW- to ∆P-guided ventilation was thus accompanied by an overall increase in tidal volume from 6.1 mL/kg PBW [5.9–6.2] to 7.7 ml/kg PBW [6.2–8.7], while respiratory rate was decreased from 29 breaths/min [26–32] to 21 breaths/min [16–28] (p < 0.001 for all comparisons). ∆P-guided ventilation was accompanied by a significant decrease in mechanical power from 31.5 J/min [28–35.7] to 28.8 J/min [24.6–32.6] (p < 0.001), representing a relative decrease of 7% [0–16]. With ∆P-guided ventilation, the PaO₂/FiO₂ ratio increased and the ventilatory ratio decreased.

Conclusion

As compared to a conventional PBW-guided ventilation, a ∆P-guided ventilation strategy targeting a ∆P between 12 and 14 cm H₂O required to change the tidal volume in 90% of the patients. Such ∆P-guided ventilation significantly reduced the mechanical power. Whether this physiological observation could be associated with clinical benefit should be assessed in clinical trials.

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.

Validation of the flow index to detect low inspiratory effort during pressure support ventilation

Background

Bedside assessment of low levels of inspiratory effort, which are probably insufficient to prevent muscle atrophy, is challenging. The flow index, which is derived from the analysis of the inspiratory portion of the flow–time waveform, has been recently introduced as a non-invasive parameter to evaluate the inspiratory effort. The primary objective of the present study was to provide an external validation of the flow index to detect low inspiratory effort.

Methods

Datasets containing flow, airway pressure, and esophageal pressure (P_es)–time waveforms were obtained from a previously published study in 100 acute brain-injured patients undergoing pressure support ventilation. Waveforms data were analyzed offline. A low inspiratory effort was defined by one of the following criteria, work of breathing (WOB) less than 0.3 J/L, P_es–time product (PTP_es) per minute less than 50 cmH₂O•s/min, or inspiratory muscle pressure (P_mus) less than 5 cmH₂O, adding “or occurrence of ineffective effort more than 10%” for all criteria. The flow index was calculated according to previously reported method. The association of flow index with P_es-derived parameters of effort was investigated. The diagnostic accuracy of the flow index to detect low effort was analyzed.

Results

Moderate correlations were found between flow index and WOB, P_mus, and PTP_es per breath and per minute (Pearson’s correlation coefficients ranged from 0.546 to 0.634, P < 0.001). The incidence of low inspiratory effort was 62%, 51%, and 55% using the definition of WOB, PTP_es per minute, and P_mus, respectively. The area under the receiver operating characteristic curve for flow index to diagnose low effort was 0.88, 0.81, and 0.88, for the three respective definition. By using the cutoff value of flow index less than 2.1, the diagnostic performance for the three definitions showed sensitivity of 0.95–0.96, specificity of 0.57–0.71, positive predictive value of 0.70–0.84, and negative predictive value of 0.90–0.93.

Conclusions

The flow index is associated with P_es-based inspiratory effort measurements. Flow index can be used as a valid instrument to screen low inspiratory effort with a high probability to exclude cases without the condition.

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.

Relationships between double cycling and inspiratory effort with diaphragm thickness during the early phase of mechanical ventilation: A prospective observational study

Background

Increased and decreased diaphragm thickness during mechanical ventilation is associated with poor outcomes. Some types of patient-ventilator asynchrony theoretically cause myotrauma of the diaphragm. However, the effects of double cycling on structural changes in the diaphragm have not been previously evaluated. Hence, this study aimed to investigate the relationship between double cycling during the early phase of mechanical ventilation and changes in diaphragm thickness, and the involvement of inspiratory effort in the occurrence of double cycling.

Methods

We evaluated adult patients receiving invasive mechanical ventilation for more than 48 h. The end-expiratory diaphragm thickness (Tdi_ee) was assessed via ultrasonography on days 1, 2, 3, 5 and 7 after the initiation of mechanical ventilation. Then, the maximum rate of change from day 1 (ΔTdi_ee%) was evaluated. Concurrently, we recorded esophageal pressure and airway pressure on days 1, 2 and 3 for 1 h during spontaneous breathing. Then, the waveforms were retrospectively analyzed to calculate the incidence of double cycling (double cycling index) and inspiratory esophageal pressure swing (ΔP_es). Finally, the correlation between double cycling index as well as ΔP_es and ΔTdi_ee% was investigated using linear regression models.

Results

In total, 19 patients with a median age of 69 (interquartile range: 65–78) years were enrolled in this study, and all received pressure assist-control ventilation. The Tdi_ee increased by more than 10% from baseline in nine patients, decreased by more than 10% in nine and remained unchanged in one. The double cycling indexes on days 1, 2 and 3 were 2.2%, 1.3% and 4.5%, respectively. There was a linear correlation between the double cycling index on day 3 and ΔTdi_ee% (R² = 0.446, p = 0.002). The double cycling index was correlated with the ΔP_es on days 2 (R² = 0.319, p = 0.004) and 3 (R² = 0.635, p < 0.001).

Conclusions

Double cycling on the third day of mechanical ventilation was associated with strong inspiratory efforts and, possibly, changes in diaphragm thickness.

Reverse Triggering: An Introduction to Diagnosis, Management, and Pharmacologic Implications

Reverse triggering is an underdiagnosed form of patient-ventilator asynchrony in which a passive ventilator-delivered breath triggers a neural response resulting in involuntary patient effort and diaphragmatic contraction. Reverse triggering may significantly impact patient outcomes, and the unique physiology underscores critical potential implications for drug-device-patient interactions. The purpose of this review is to summarize what is known of reverse triggering and its pharmacotherapeutic consequences, with a particular focus on describing reported cases, physiology, historical context, epidemiology, and management. The PubMed database was searched for publications that reported patients presenting with reverse triggering. The current body of evidence suggests that deep sedation may predispose patients to episodes of reverse triggering; as such, providers may consider decreasing sedation or modifying ventilator settings in patients exhibiting ventilator asynchrony as an initial measure. Increased clinician awareness and research focus are necessary to understand appropriate management of reverse triggering and its association with patient outcomes.

Monitoring during extracorporeal membrane oxygenation

PURPOSE OF REVIEW

Extracorporeal membrane oxygenation (ECMO) offers advanced mechanical support to patients with severe acute respiratory and/or cardiac failure. Ensuring an adequate therapeutic approach as well as prevention of ECMO-associated complications, by means of timely liberation, forms an essential part of standard ECMO care and is only achievable through continuous monitoring and evaluation. This review focus on the cardiorespiratory monitoring tools that can be used to assess and titrate adequacy of ECMO therapy; as well as methods to assess readiness to wean and/or discontinue ECMO support.

RECENT FINDINGS

Surrogates of tissue perfusion and near infrared spectroscopy are not standards of care but may provide useful information in select patients. Echocardiography allows to determine cannulas position, evaluate cardiac structures, and function, and diagnose complications. Respiratory monitoring is mandatory to achieve lung protective ventilation and identify early lung recovery, surrogate measurements of respiratory effort and ECMO derived parameters are invaluable in optimally managing ECMO patients.

SUMMARY

Novel applications of existing monitoring modalities alongside evolving technological advances enable the advanced monitoring required for safe delivery of ECMO. Liberation trials are necessary to minimize time sensitive ECMO related complications; however, these have yet to be standardized.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

Neurally Adjusted Ventilatory Assist in Acute Respiratory Failure-A Narrative Review

Maintaining spontaneous breathing has both potentially beneficial and deleterious consequences in patients with acute respiratory failure, depending on the balance that can be obtained between the protecting and damaging effects on the lungs and the diaphragm. Neurally adjusted ventilatory assist (NAVA) is an assist mode, which supplies the respiratory system with a pressure proportional to the integral of the electrical activity of the diaphragm. This proportional mode of ventilation has the theoretical potential to deliver lung- and respiratory-muscle-protective ventilation by preserving the physiologic defense mechanisms against both lung overdistention and ventilator overassistance, as well as reducing the incidence of diaphragm disuse atrophy while maintaining patient–ventilator synchrony. This narrative review presents an overview of NAVA technology, its basic principles, the different methods to set the assist level and the findings of experimental and clinical studies which focused on lung and diaphragm protection, machine–patient interaction and preservation of breathing pattern variability. A summary of the findings of the available clinical trials which investigate the use of NAVA in acute respiratory failure will also be presented and discussed.