Reverse Trigger Phenotypes in Acute Respiratory Distress Syndrome

Flow-Limited and Reverse-Triggered Ventilator Dyssynchrony Are Associated With Increased Tidal and Dynamic Transpulmonary Pressure

OBJECTIVES

Ventilator dyssynchrony may be associated with increased delivered tidal volumes (V t s) and dynamic transpulmonary pressure (ΔP L,dyn ), surrogate markers of lung stress and strain, despite low V t ventilation. However, it is unknown which types of ventilator dyssynchrony are most likely to increase these metrics or if specific ventilation or sedation strategies can mitigate this potential.

DESIGN

A prospective cohort analysis to delineate the association between ten types of breaths and delivered V t , ΔP L,dyn , and transpulmonary mechanical energy.

SETTING

Patients admitted to the medical ICU.

PATIENTS

Over 580,000 breaths from 35 patients with acute respiratory distress syndrome (ARDS) or ARDS risk factors.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients received continuous esophageal manometry. Ventilator dyssynchrony was identified using a machine learning algorithm. Mixed-effect models predicted V t , ΔP L,dyn , and transpulmonary mechanical energy for each type of ventilator dyssynchrony while controlling for repeated measures. Finally, we described how V t , positive end-expiratory pressure (PEEP), and sedation (Richmond Agitation-Sedation Scale) strategies modify ventilator dyssynchrony's association with these surrogate markers of lung stress and strain. Double-triggered breaths were associated with the most significant increase in V t , ΔP L,dyn , and transpulmonary mechanical energy. However, flow-limited, early reverse-triggered, and early ventilator-terminated breaths were also associated with significant increases in V t , ΔP L,dyn , and energy. The potential of a ventilator dyssynchrony type to increase V t , ΔP L,dyn , or energy clustered similarly. Increasing set V t may be associated with a disproportionate increase in high-volume and high-energy ventilation from double-triggered breaths, but PEEP and sedation do not clinically modify the interaction between ventilator dyssynchrony and surrogate markers of lung stress and strain.

CONCLUSIONS

Double-triggered, flow-limited, early reverse-triggered, and early ventilator-terminated breaths are associated with increases in V t , ΔP L,dyn , and energy. As flow-limited breaths are more than twice as common as double-triggered breaths, further work is needed to determine the interaction of ventilator dyssynchrony frequency to cause clinically meaningful changes in patient outcomes.

Respiratory entrainment related reverse triggering in mechanically ventilated children

BACKGROUND

The underlying pathophysiological pathways how reverse triggering is being caused are not fully understood. Respiratory entrainment may be one of these mechanisms, but both terms are used interchangeably. We sought to characterize reverse triggering and the relationship with respiratory entrainment among mechanically ventilated children with and without acute lung injury.

METHODS

We performed a secondary phyiology analysis of two previously published data sets of invasively mechanically ventilated children < 18 years with and without lung injury mechanically ventilated in a continuous or intermittent mandatory ventilation mode. Ventilator waveforms, electrical activity of the diaphragm measured with surface electromyography and oesophageal tracings were analyzed for entrained and non-entrained reverse triggered breaths.

RESULTS

In total 102 measurements (3110 min) from 67 patients (median age 4.9 [1.8 ; 19,1] months) were analyzed. Entrained RT was identified in 12 (12%) and non-entrained RT in 39 (38%) recordings. Breathing variability for entrained RT breaths was lower compared to non-entrained RT breaths. We did not observe breath stacking during entrained RT. Double triggering often occurred during non-entrained RT and led to an increased tidal volume. Patients with respiratory entrainment related RT had a shorter duration of MV and length of PICU stay.

CONCLUSIONS

Reverse triggering is not one entity but a clinical spectrum with different mechanisms and consequences.

TRIAL REGISTRATION

Not applicable.

Pendelluft in patients with acute respiratory distress syndrome during trigger and reverse triggering breaths

Pendelluft, the shift of air from non-dependent to dependent lung regions, is known to occur during active breathing in ventilated patients. However, information about pendelluft in ARDS patients under assisted mechanical ventilation is limited. In this prospectively collected and retrospectively analyzed study, we combined electrical impedance tomography and respiratory mechanics monitoring to quantitatively examine pendelluft in trigger and reverse triggering breaths in 20 mechanically ventilated patients with ARDS during the transition from controlled to active breaths under volume-cycled ventilation. Besides the 10 resting breaths in each patient, 20% of the counted active breaths were selected based on three levels of esophageal pressure swing (∆Pes): low (< 5 cm H2O, breaths = 471), moderate (≥ 5, < 10 cm H2O, breaths = 906), and high effort (≥ 10 cm H2O, breaths = 565). The pendelluft response to breathing efforts was significantly greater in trigger breaths than in reverse triggering breaths (p < 0.0001). Based on the pendelluft-∆Pes slope (ml/cmH2O), there were two distinct patterns of effort-related pendelluft (high vs. low pendelluft group). For trigger breaths, the high pendelluft group (n = 9, slope 0.7-2.4 ml/cmH2O) was significantly associated with lower peak airway/plateau pressure and lower respiratory system/lung elastance than the low pendelluft group (n = 11, slope - 0.1 to 0.3 ml/cmH2O). However, there was no difference in respiratory mechanics between high and low pendelluft groups for reverse triggering breathes. The use of ∆Pes to predict pendelluft was found to have a low positive predictive value.

The Development, Optimization, and Validation of Four Different Machine Learning Algorithms to Identify Ventilator Dyssynchrony

Invasive mechanical ventilation can worsen lung injury. Ventilator dyssynchrony (VD) may propagate ventilator-induced lung injury (VILI) and is challenging to detect and systematically monitor because each patient takes approximately 25,000 breaths a day yet some types of VD are rare, accounting for less than 1% of all breaths. Therefore, we sought to develop and validate accurate machine learning (ML) algorithms to detect multiple types of VD by leveraging esophageal pressure waveform data to quantify patient effort with airway pressure, flow, and volume data generated during mechanical ventilation, building a computational pipeline to facilitate the study of VD.

MATERIALS AND METHODS

We collected ventilator waveform and esophageal pressure data from 30 patients admitted to the ICU. Esophageal pressure allows the measurement of transpulmonary pressure and patient effort. Waveform data were cleaned, features considered essential to VD detection were calculated, and a set of 10,000 breaths were manually labeled. Four ML algorithms were trained to classify each type of VD: logistic regression, support vector classification, random forest, and XGBoost.

RESULTS

We trained ML models to detect different families and seven types of VD with high sensitivity (>90% and >80%, respectively). Three types of VD remained difficult for ML to classify because of their rarity and lack of sample size. XGBoost classified breaths with increased specificity compared to other ML algorithms.

DISCUSSION

We developed ML models to detect multiple types of VD accurately. The ability to accurately detect multiple VD types addresses one of the significant limitations in understanding the role of VD in affecting patient outcomes.

CONCLUSION

ML models identify multiple types of VD by utilizing esophageal pressure data and airway pressure, flow, and volume waveforms. The development of such computational pipelines will facilitate the identification of VD in a scalable fashion, allowing for the systematic study of VD and its impact on patient outcomes.

Eccentric Contractions of the Diaphragm During Mechanical Ventilation

Diaphragm dysfunction is a highly prevalent phenomenon in patients receiving mechanical ventilation, mainly due to ventilatory over-assistance and the development of diaphragm disuse atrophy. Promoting diaphragm activation whenever possible and facilitating an adequate interaction between the patient and the ventilator is encouraged at the bedside to avoid myotrauma and further lung injury. Eccentric contractions of the diaphragm are defined as muscle activation while muscle fibers are lengthening within the exhalation phase. There is recent evidence that suggests that eccentric activation of the diaphragm is very frequent and may occur during post-inspiratory activity or under different types of patient-ventilator asynchronies, which include ineffective efforts, premature cycling, and reverse triggering. The consequences of this eccentric contraction of the diaphragm may have opposite effects, depending on the level of breathing effort. For instance, during high or excessive effort, eccentric contractions can result in diaphragm dysfunction and injured muscle fibers. Conversely, when eccentric contractions of the diaphragm occur along with low breathing effort, a preserved diaphragm function, better oxygenation, and more aerated lung tissue are observed. Despite this controversial evidence, evaluating the level of breathing effort at the bedside seems crucial and is highly recommended to optimize ventilatory therapy. The impact of eccentric contractions of the diaphragm on the patient's outcome remains to be elucidated.

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.

Reverse triggering during mechanical ventilation: Diagnosis and clinical implications

This review addresses the phenomenon of "reverse triggering", an asynchrony that occurs in deeply sedated patients or patients in transition from deep to light sedation. Reverse triggering has been reported to occur in 30-90% of all ventilated patients. The underlying pathophysiological mechanisms remain unclear, but "entrainment" is proposed as one of them. Detecting this asynchrony is crucial, and methods such as visual inspection, esophageal pressure, diaphragmatic ultrasound and automated methods have been used. Reverse triggering may have effects on lung and diaphragm function, probably mediated by the level of breathing effort and eccentric activation of the diaphragm. The optimal management of reverse triggering has not been established, but may include the adjustment of ventilatory parameters as well as of sedation level, and in extreme cases, neuromuscular block. It is important to understand the significance of this condition and its detection, but also to conduct dedicated research to improve its clinical management and potential effects in critically ill patients.

Esophageal Pressure Measurement: A Primer

Over the last decade, the literature exploring clinical applications for esophageal manometry in critically ill patients has increased. New mechanical ventilators and bedside monitors allow measurement of esophageal pressures easily at the bedside. The bedside clinician can now evaluate the magnitude and timing of esophageal pressure swings to evaluate respiratory muscle activity and transpulmonary pressures. The respiratory therapist has all the tools to perform these measurements to optimize mechanical ventilation delivery. However, as with any measurement, technique, fidelity, and accuracy are paramount. This primer highlights key knowledge necessary to perform measurements and highlights areas of both uncertainty and ongoing development.

Reverse triggering neural network and rules-based automated detection in acute respiratory distress syndrome

PURPOSE

Dyssynchrony may cause lung injury and is associated with worse outcomes in mechanically ventilated patients. Reverse triggering (RT) is a common type of dyssynchrony presenting with several phenotypes which may directly cause lung injury and be difficult to identify. Due to these challenges, automated software to assist in identification is needed.

MATERIALS AND METHODS

This was a prospective observational study using a training set of 15 patients and a validation dataset of 13 patients. RT events were manually identified and compared with "rules-based" programs (with and without esophageal manometry and reverse triggering with breath stacking), and were used to train a neural network artificial intelligence (AI) program. RT phenotypes were identified using previously defined rules. Performance of the programs was compared via sensitivity, specificity, positive predictive value (PPV) and F1 score.

RESULTS

33,244 breaths were manually analyzed, with 8718 manually identified as reverse-triggers. The rules-based and AI programs yielded excellent specificity (>95% in all programs) and F1 score (>75% in all programs). RT with breath stacking (24.4%) and mid-cycle RT (37.8%) were the most common phenotypes.

CONCLUSIONS

Automated detection of RT demonstrated good performance, with the potential application of these programs for research and clinical care.

Reverse Triggering during Controlled Ventilation: From Physiology to Clinical Management

Reverse triggering dyssynchrony is a frequent phenomenon recently recognized in sedated critically ill patients under controlled ventilation. It occurs in at least 30-55% of these patients and often occurs in the transition from fully passive to assisted mechanical ventilation. During reverse triggering, patient inspiratory efforts start after the passive insufflation by mechanical breaths. The most often referred mechanism is the entrainment of the patient's intrinsic respiratory rhythm from the brainstem respiratory centers to periodic mechanical insufflations from the ventilator. However, reverse triggering might also occur because of local reflexes without involving the respiratory rhythm generator in the brainstem. Reverse triggering is observed during the acute phase of the disease, when patients may be susceptible to potential deleterious consequences of injurious or asynchronous efforts. Diagnosing reverse triggering might be challenging and can easily be missed. Inspection of ventilator waveforms or more sophisticated methods, such as the electrical activity of the diaphragm or esophageal pressure, can be used for diagnosis. The occurrence of reverse triggering might have clinical consequences. On the basis of physiological data, reverse triggering might be beneficial or injurious for the diaphragm and the lung, depending on the magnitude of the inspiratory effort. Reverse triggering can cause breath-stacking and loss of protective lung ventilation when triggering a second cycle. Little is known about how to manage patients with reverse triggering; however, available evidence can guide management on the basis of physiological principles.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Analysis of clinical characteristics and prognostic factors of ARDS caused by community-acquired pneumonia in people with different immune status

BACKGROUND

The purpose of this study is to describe the clinical characteristics and prognostic risk factors of acute respiratory distress syndrome (ARDS) caused by community-acquired pneumonia under different immune states.

METHODS

The patients were divided into immunocompetent and immunocompromised groups according to their immune status. The basic clinical data of the two groups were collected and statistically analyzed, and the clinical characteristics and prognostic factors of ARDS caused by community-acquired pneumonia under different immune states were summarized.

RESULTS

128 patients with ARDS caused by community-acquired pneumonia were enrolled. The chest High-Resolution Computed Tomography (HRCT) scores of patients with immunosuppression were higher (236.0 ± 55.0 vs. 207.5 ± 49.6, p < 0.05) and the score of APACHE II was higher (17.3 ± 4.8 vs. 15.1 ± 5.4, p < 0.05). The 28-day intensive care unit (ICU) mortality was higher in the immunocompromised group (54.5% vs. 34.7%, p = 0.045). The 28-day in-hospital mortality in the immunocompetent group was mainly related to NLR and the oxygenation index. The 28-day in-hospital mortality in the immunocompromised group was mainly related to LDH and APACHE II.

CONCLUSION

There are differences in clinical characteristics and mortality of ARDS patients caused by community-acquired pneumonia under different immune states.

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.

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.

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.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

BACKGROUND

Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics.

METHODS

Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony.

RESULTS

Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony.

CONCLUSIONS

Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.

Impact of Reverse Triggering Dyssynchrony During Lung-Protective Ventilation on Diaphragm Function: An Experimental Model

RATIONALE

Reverse triggering is a patient-ventilator interaction where a respiratory muscle contraction is triggered by a passive mechanical insufflation. Its impact on diaphragm structure and function is unknown.

OBJECTIVE

To establish an animal model of reverse triggering with lung injury receiving lung-protective ventilation and to assess its impact on structure and function of the diaphragm.

METHODS

Lung injury was induced by surfactant depletion and high stress ventilation in 32 ventilated pigs. Animals were allocated to receive passive mechanical ventilation or a lung-protective strategy with adjustments facilitating the occurrence of reverse triggering for 3 hours. Diaphragm function (transdiaphragmatic pressure (Pdi) during phrenic nerve stimulation [Force/frequency curve]) and structure (biopsies) were assessed. The impact of reverse triggering on diaphragm function was analyzed according to the breathing effort.

RESULTS

Compared to passive ventilation, the protective ventilation group with reverse triggering received significantly lower tidal volume (7 vs 10 ml/kg) and higher respiratory rate (45 vs 31 bpm). An entrainment pattern of 1:1 was frequent. Breathing effort induced by reverse triggering was highly variable across animals. Reverse triggering with the lowest tercile of breathing effort was associated with 23% higher twitch Pdi compared to passive ventilation, whereas reverse triggering with high breathing effort was associated with a 10% lower twitch Pdi and a higher proportion of abnormal muscle fibers.

CONCLUSION

In a reproducible animal model of reverse triggering with variable levels of breathing effort and entrainment patterns, reverse triggering with high effort is associated with impaired diaphragm function whereas reverse triggering with low effort is associated with preserved diaphragm force.

A Taxonomy for Patient-Ventilator Interactions and a Method to Read Ventilator Waveforms

Mechanical ventilators display detailed waveforms which contain a wealth of clinically relevant information. Although much has been written about interpretation of waveforms and patient-ventilator interactions, variability remains on the nomenclature (multiple and ambiguous terms) and waveform interpretation. There are multiple reasons for this variability (legacy terms, language, multiple definitions). In addition, there is no widely accepted systematic method to read ventilator waveforms. We propose a standardized nomenclature and taxonomy along with a method to interpret mechanical ventilator displayed waveforms.

Reverse Trigger in Ventilated Non-ARDS Patients: A Phenomenon Can Not Be Ignored!

Introduction

The role of reverse trigger (RT) was unknown in ventilated non-acute respiratory distress syndrome (ARDS) patients. So we conducted a retrospective study to evaluate the incidence, characteristics and physiologic consequence of RT in such population.

Method

Six ventilated non-ARDS patients were included, the esophageal balloon catheter were placed for measurements of respiratory mechanics in all patients. And the data were analyzed to identified the occurrence of RT, duration of the entrainment, the entrainment pattern or ratio, the phase difference (dP) and the phase angle (θ), phenotypes, Effects and clinical correlations of RT.

Result

RT was detected in four patients of our series (66.7%), and the occurrence of RT varying from 19 to 88.6% of their recording time in these 4 patients. One patient (No.2) showed a stable 1:1 ratio and Mid-cycle RT was the most common phenotype. However, the remained patients showed a mixed ratios, and Late RT was the most common phenotype, followed by RT with breath stacking. The average values of mean phase delay and phase angles were 0.39s (0.32, 0.98) and 60.52° (49.66, 102.24). Mean phase delay and phase angles were shorter in early reverse triggering with early and delayed relaxation, and longer in mid, late RT and RT with breath stacking. Pmus was variable between patients and phenotypes, and larger Pmus was generated in Early RT, Delayed Relaxation and mid cycle RT. When the RT occurred, the Peso increased 17.27 (4.91, 19.71) cmH₂O compared to the controlled breathing, and the average value of incremental ΔPeso varied widely inter and intra patients (Table 3B and Figure 1). Larger ΔPeso was always generated in Early RT, Delayed Relaxation and mid cycle RT, accompanied by an significant increase of PL with 19.12 (0.75) cmH₂O and 16.10 (6.23) cmH₂O.

Conclusion

RT could also be observed in ventilated non-ARDS patients. The characteristics of pattern and phenotype was similar to RT in ARDS patients to a large extent. And RT appeared to alter lung stress and delivered volumes.

Patient-Self Inflicted Lung Injury: A Practical Review

Patients with severe lung injury usually have a high respiratory drive, resulting in intense inspiratory effort that may even worsen lung damage by several mechanisms gathered under the name “patient-self inflicted lung injury” (P-SILI). Even though no clinical study has yet demonstrated that a ventilatory strategy to limit the risk of P-SILI can improve the outcome, the concept of P-SILI relies on sound physiological reasoning, an accumulation of clinical observations and some consistent experimental data. In this review, we detail the main pathophysiological mechanisms by which the patient’s respiratory effort could become deleterious: excessive transpulmonary pressure resulting in over-distension; inhomogeneous distribution of transpulmonary pressure variations across the lung leading to cyclic opening/closing of nondependent regions and pendelluft phenomenon; increase in the transvascular pressure favoring the aggravation of pulmonary edema. We also describe potentially harmful patient-ventilator interactions. Finally, we discuss in a practical way how to detect in the clinical setting situations at risk for P-SILI and to what extent this recognition can help personalize the treatment strategy.

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.

[Patient self-inflicted lung injury (P-SILI) : From pathophysiology to clinical evaluation with differentiated management]

Die Etablierung der unterstützten Spontanatmung gilt allgemein als eine vorteilhafte und wenig gefährdende Phase der Beatmungstherapie. Allerdings geben neuere Erkenntnisse Hinweise auf eine potenzielle Schädigung durch exzessive Spontanatembemühungen vor allem bei akuter Lungenschädigung. Das Syndrom wird unter dem Begriff „patient self-inflicted lung injury“ zusammengefasst. Ärzte, Pflegepersonen und Atmungstherapeuten sollten für diese Thematik sensibilisiert werden. Parameter, die mittels Ösophagusdruckmessung oder einfacher Manöver am Respirator bestimmt werden können, sind bei der Entscheidung zur Durchführung und zur Überwachung von Spontanatmung auch in den akuten Phasen der Lungenschädigung hilfreich. Weiterhin gibt es im Umgang mit hohem Atemantrieb oder erhöhter Atemanstrengung therapeutische Möglichkeiten, diesen zu begegnen.