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

An overview of patient-ventilator asynchrony in children

INTRODUCTION

Mechanically ventilated children often have patient-ventilator asynchrony (PVA). When a ventilated patient has spontaneous effort, the ventilator attempts to synchronize with the patient, but PVA represents a mismatch between patient respiratory effort and ventilator delivered breaths.

AREAS COVERED

This review will focus on subtypes of patient ventilator asynchrony, methods to detect or measure PVA, risk factors for and characteristics of patients with PVA subtypes, potential clinical implications, treatment or prevention strategies, and future areas for research. Throughout this review, we will provide pediatric specific considerations.

EXPERT OPINION

PVA in pediatric patients supported by mechanical ventilation occurs frequently and is understudied. Pediatric patients have unique physiologic and pathophysiologic characteristics which affect PVA. While recognition of PVA and its subtypes is important for bedside clinicians, the clinical implications and risks versus benefits of treatment targeted at reducing PVA remain unknown. Future research should focus on harmonizing PVA terminology, refinement of automated detection technologies, determining which forms of PVA are harmful, and development of PVA-specific ventilator interventions.

Double cycling with breath-stacking during partial support ventilation in ARDS: Just a feature of natural variability?

BACKGROUND

Double cycling with breath-stacking (DC/BS) during controlled mechanical ventilation is considered potentially injurious, reflecting a high respiratory drive. During partial ventilatory support, its occurrence might be attributable to physiological variability of breathing patterns, reflecting the response of the mode without carrying specific risks.

METHODS

This secondary analysis of a crossover study evaluated DC/BS events in hypoxemic patients resuming spontaneous breathing in cross-over under neurally adjusted ventilatory assist (NAVA), proportional assist ventilation (PAV +), and pressure support ventilation (PSV). DC/BS was defined as two inspiratory cycles with incomplete exhalation. Measurements included electrical impedance signal, airway pressure, esophageal and gastric pressures, and flow. Breathing variability, dynamic compliance (CLdyn), and end-expiratory lung impedance (EELI) were analyzed.

RESULTS

Twenty patients under assisted breathing, with a median of 9 [5-14] days on mechanical ventilation, were included. DC/BS was attributed to either a single (42%) or two apparent consecutive inspiratory efforts (58%). The median [IQR] incidence of DC/BS was low: 0.6 [0.1-2.6] % in NAVA, 0.0 [0.0-0.4] % in PAV + , and 0.1 [0.0-0.4] % in PSV (p = 0.06). DC/BS events were associated with patient's coefficient of variability for tidal volume (p = 0.014) and respiratory rate (p = 0.011). DC/BS breaths exhibited higher tidal volume, muscular pressure and regional stretch compared to regular breaths. Post-DC/BS cycles frequently exhibited improved EELI and CLdyn, with no evidence of expiratory muscle activation in 63% of cases.

CONCLUSIONS

DC/BS events during partial ventilatory support were infrequent and linked to breathing variability. Their frequency and physiological effects on lung compliance and EELI resemble spontaneous sighs and may not be considered a priori as harmful.

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

BACKGROUND

Subject-ventilator asynchrony (SVA) was shown to be associated with negative clinical outcomes. To elucidate pathophysiology pathways and effects of SVA on lung tissue histology a reproducible animal model of artificially induced asynchrony was developed and evaluated.

METHODS

Alterations in ventilator parameters were used to induce the three main types of asynchrony: ineffective efforts (IE), auto-triggering (AT), and double-triggering (DT). Airway flow and pressure, as well as oesophageal pressure waveforms, were recorded, asynchrony cycles were manually classified and the asynchrony index (AIX) was calculated. Bench tests were conducted on an active lung simulator with ventilator settings altered cycle by cycle. The developed algorithm was evaluated in three pilot experiments and a study in pigs ventilated for twelve hours with AIX = 25%.

RESULTS

IE and AT were induced reliably and fail-safe by end-expiratory hold and adjustment of respiratory rate, respectively. DT was provoked using airway pressure ramp prolongation, however not controlled specifically in the pilots. In the subsequent study, an AIX = 28.8% [24.0%-34.4%] was induced and maintained over twelve hours.

CONCLUSIONS

The method allows to reproducibly induce and maintain three clinically relevant types of SVA observed in ventilated patients and may thus serve as a useful tool for future investigations on cellular and inflammatory effects of asynchrony.

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.

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.

Neuromuscular Blocking Agent Use in Critical Care Transport Not Associated With Intubation

OBJECTIVE

Variable indications exist for neuromuscular blocking agents (NMBAs) in the critical care transport setting beyond facilitation of intubation.

METHODS

This retrospective cohort study included adult patients (≥ 18 years) who underwent critical care transport from July 1, 2020, to May 2, 2023, and received NMBAs during transport that was not associated with intubation. The primary outcome was the indication for NMBA administration. Secondary outcomes included the characterization of NMBA use, mean Richmond Agitation Sedation Scale score before NMBA administration, sedation strategy used, and continuation of NMBAs within 48 hours of hospital admission.

RESULTS

One hundred twenty-six patients met the inclusion criteria. The most common indication for NMBA administration was ventilator dyssynchrony (n = 71, 56.4%). The majority of patients received rocuronium during transport (n = 113, 89.7%). The mean pre-NMBA Richmond Agitation Sedation Scale score was -3.7 ± 2.4. The most common sedation strategy was a combination of continuous infusion and bolus sedatives (76.2%). One hundred (79.4%) patients had sedation changes in response to NMBA administration. Seventy-two (57.1%) received NMBAs during the first 48 hours of their intensive care unit admission.

CONCLUSION

NMBAs were frequently administered for ventilator dyssynchrony and continuation of prior therapy. Optimization opportunities exist to ensure adequate deep sedation and reassessment of NMBA indication.

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 .

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.

Mechanical Ventilation, Past, Present, and Future

Mechanical ventilation (MV) has played a crucial role in the medical field, particularly in anesthesia and in critical care medicine (CCM) settings. MV has evolved significantly since its inception over 70 years ago and the future promises even more advanced technology. In the past, ventilation was provided manually, intermittently, and it was primarily used for resuscitation or as a last resort for patients with severe respiratory or cardiovascular failure. The earliest MV machines for prolonged ventilatory support and oxygenation were large and cumbersome. They required a significant amount of skills and expertise to operate. These early devices had limited capabilities, battery, power, safety features, alarms, and therefore these often caused harm to patients. Moreover, the physiology of MV was modified when mechanical ventilators moved from negative pressure to positive pressure mechanisms. Monitoring systems were also very limited and therefore the risks related to MV support were difficult to quantify, predict and timely detect for individual patients who were necessarily young with few comorbidities. Technology and devices designed to use tracheostomies versus endotracheal intubation evolved in the last century too and these are currently much more reliable. In the present, positive pressure MV is more sophisticated and widely used for extensive period of time. Modern ventilators use mostly positive pressure systems and are much smaller, more portable than their predecessors, and they are much easier to operate. They can also be programmed to provide different levels of support based on evolving physiological concepts allowing lung-protective ventilation. Monitoring systems are more sophisticated and knowledge related to the physiology of MV is improved. Patients are also more complex and elderly compared to the past. MV experts are informed about risks related to prolonged or aggressive ventilation modalities and settings. One of the most significant advances in MV has been protective lung ventilation, diaphragm protective ventilation including noninvasive ventilation (NIV). Health care professionals are familiar with the use of MV and in many countries, respiratory therapists have been trained for the exclusive purpose of providing safe and professional respiratory support to critically ill patients. Analgo-sedation drugs and techniques are improved, and more sedative drugs are available and this has an impact on recovery, weaning, and overall patients' outcome. Looking toward the future, MV is likely to continue to evolve and improve alongside monitoring techniques and sedatives. There is increasing precision in monitoring global "patient-ventilator" interactions: structure and analysis (asynchrony, desynchrony, etc). One area of development is the use of artificial intelligence (AI) in ventilator technology. AI can be used to monitor patients in real-time, and it can predict when a patient is likely to experience respiratory distress. This allows medical professionals to intervene before a crisis occurs, improving patient outcomes and reducing the need for emergency intervention. This specific area of development is intended as "personalized ventilation." It involves tailoring the ventilator settings to the individual patient, based on their physiology and the specific condition they are being treated for. This approach has the potential to improve patient outcomes by optimizing ventilation and reducing the risk of harm. In conclusion, MV has come a long way since its inception, and it continues to play a critical role in anesthesia and in CCM settings. Advances in technology have made MV safer, more effective, affordable, and more widely available. As technology continues to improve, more advanced and personalized MV will become available, leading to better patients' outcomes and quality of life for those in need.

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.

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.

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.

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.

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.

The Effect of Clusters of Double Triggering and Ineffective Efforts in Critically Ill Patients

OBJECTIVES

To characterize clusters of double triggering and ineffective inspiratory efforts throughout mechanical ventilation and investigate their associations with mortality and duration of ICU stay and mechanical ventilation.

DESIGN

Registry-based, real-world study.

BACKGROUND

Asynchronies during invasive mechanical ventilation can occur as isolated events or in clusters and might be related to clinical outcomes.

SUBJECTS

Adults requiring mechanical ventilation greater than 24 hours for whom greater than or equal to 70% of ventilator waveforms were available.

INTERVENTIONS

We identified clusters of double triggering and ineffective inspiratory efforts and determined their power and duration. We used Fine-Gray's competing risk model to analyze their effects on mortality and generalized linear models to analyze their effects on duration of mechanical ventilation and ICU stay.

MEASUREMENTS AND MAIN RESULTS

We analyzed 58,625,796 breaths from 180 patients. All patients had clusters (mean/d, 8.2 [5.4-10.6]; mean power, 54.5 [29.6-111.4]; mean duration, 20.3 min [12.2-34.9 min]). Clusters were less frequent during the first 48 hours (5.5 [2.5-10] vs 7.6 [4.4-9.9] in the remaining period [p = 0.027]). Total number of clusters/d was positively associated with the probability of being discharged alive considering the total period of mechanical ventilation (p = 0.001). Power and duration were similar in the two periods. Power was associated with the probability of being discharged dead (p = 0.03), longer mechanical ventilation (p < 0.001), and longer ICU stay (p = 0.035); cluster duration was associated with longer ICU stay (p = 0.027).

CONCLUSIONS

Clusters of double triggering and ineffective inspiratory efforts are common. Although higher numbers of clusters might indicate better chances of survival, clusters with greater power and duration indicate a risk of worse clinical outcomes.

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

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

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

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

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

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.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Frequency and Risk Factors for Reverse Triggering in Pediatric Acute Respiratory Distress Syndrome during Synchronized Intermittent Mandatory Ventilation

Rationale: Reverse triggering (RT) occurs when respiratory effort begins after a mandatory breath is initiated by the ventilator. RT may exacerbate ventilator-induced lung injury and lead to breath stacking.Objectives: We sought to describe the frequency and risk factors for RT among patients with acute respiratory distress syndrome (ARDS) and identify risk factors for breath stacking.Methods: We performed a secondary analysis of physiologic data from children on synchronized intermittent mandatory pressure-controlled ventilation enrolled in a single-center randomized controlled trial for ARDS. When children had a spontaneous effort on esophageal manometry, waveforms were recorded and independently analyzed by two investigators to identify RT.Results: We included 81,990 breaths from 100 patient-days and 36 patients. Overall, 2.46% of breaths were RTs, occurring in 15/36 patients (41.6%). A higher tidal volume and a minimal difference between neural respiratory rate and set ventilator rate were independently associated with RT (P = 0.001) in multivariable modeling. Breath stacking occurred in 534 (26.5%) of 2,017 RT breaths and in 14 (93.3%) of 15 patients with RT. In multivariable modeling, breath stacking was more likely to occur when total airway Δpressure (peak inspiratory pressure - positive end-expiratory pressure [PEEP]) at the time patient effort began, peak inspiratory pressure, PEEP, and Δpressure were lower and when patient effort started well after the ventilator-initiated breath (higher phase angle) (all P < 0.05). Together, these parameters were highly predictive of breath stacking (area under the curve, 0.979).Conclusions: Patients with higher tidal volume who have a set ventilator rate close to their spontaneous respiratory rate are more likely to have RT, which results in breath stacking >25% of the time.Clinical trial registered with ClinicalTrials.gov (NCT03266016).

Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

The central nervous system during lung injury and mechanical ventilation: a narrative review

Mechanical ventilation induces a number of systemic responses for which the brain plays an essential role. During the last decade, substantial evidence has emerged showing that the brain modifies pulmonary responses to physical and biological stimuli by various mechanisms, including the modulation of neuroinflammatory reflexes and the onset of abnormal breathing patterns. Afferent signals and circulating factors from injured peripheral tissues, including the lung, can induce neuronal reprogramming, potentially contributing to neurocognitive dysfunction and psychological alterations seen in critically ill patients. These impairments are ubiquitous in the presence of positive pressure ventilation. This narrative review summarises current evidence of lung-brain crosstalk in patients receiving mechanical ventilation and describes the clinical implications of this crosstalk. Further, it proposes directions for future research ranging from identifying mechanisms of multiorgan failure to mitigating long-term sequelae after critical illness.

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.

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

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

Reverse Triggering Dyssynchrony 24 h after Initiation of Mechanical Ventilation

BACKGROUND

Reverse triggering is a delayed asynchronous contraction of the diaphragm triggered by passive insufflation by the ventilator in sedated mechanically ventilated patients. The incidence of reverse triggering is unknown. This study aimed at determining the incidence of reverse triggering in critically ill patients under controlled ventilation.

METHODS

In this ancillary study, patients were continuously monitored with a catheter measuring the electrical activity of the diaphragm. A method for automatic detection of reverse triggering using electrical activity of the diaphragm was developed in a derivation sample and validated in a subsequent sample. The authors assessed the predictive value of the software. In 39 recently intubated patients under assist-control ventilation, a 1-h recording obtained 24 h after intubation was used to determine the primary outcome of the study. The authors also compared patients' demographics, sedation depth, ventilation settings, and time to transition to assisted ventilation or extubation according to the median rate of reverse triggering.

RESULTS

The positive and negative predictive value of the software for detecting reverse triggering were 0.74 (95% CI, 0.67 to 0.81) and 0.97 (95% CI, 0.96 to 0.98). Using a threshold of 1 μV of electrical activity to define diaphragm activation, median reverse triggering rate was 8% (range, 0.1 to 75), with 44% (17 of 39) of patients having greater than or equal to 10% of breaths with reverse triggering. Using a threshold of 3 μV, 26% (10 of 39) of patients had greater than or equal to 10% reverse triggering. Patients with more reverse triggering were more likely to progress to an assisted mode or extubation within the following 24 h (12 of 39 [68%]) vs. 7 of 20 [35%]; P = 0.039).

CONCLUSIONS

Reverse triggering detection based on electrical activity of the diaphragm suggests that this asynchrony is highly prevalent at 24 h after intubation under assist-control ventilation. Reverse triggering seems to occur during the transition phase between deep sedation and the onset of patient triggering.

EDITOR’S PERSPECTIVE

[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.

Clusters of Double Triggering Impact Clinical Outcomes: Insights From the EPIdemiology of Patient-Ventilator aSYNChrony (EPISYNC) Cohort Study

OBJECTIVES

To measure the impact of clusters of double triggering on clinical outcomes.

DESIGN

Prospective cohort study.

SETTING

Respiratory ICU in Brazil.

PATIENTS

Adult patients under recent mechanical ventilation and with expectation of mechanical ventilation for more than 24 hours after enrollment.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used a dedicated software to analyze ventilator waveforms throughout the entire period of mechanical ventilation and detect double triggering. We defined a cluster of double triggering as a period of time containing at least six double triggering events in a 3-minute period. Patients were followed until hospital discharge. We addressed the association between the presence and the duration of clusters with clinical outcomes. A total of 103 patients were enrolled in the study and 90 (87%) had at least one cluster of double triggering. The median number of clusters per patient was 19 (interquartile range, 6-41), with a median duration of 8 minutes (6-12 min). Compared with patients who had no clusters, patients with at least one cluster had longer duration of mechanical ventilation (7 d [4-11 d] vs 2 d [2-3 d]) and ICU length of stay (9 d [7-16 d] vs 13 d [2-8 d]). Thirty-three patients had high cumulative duration of clusters of double triggering (≥ 12 hr), and it was associated with longer duration of mechanical ventilation, fewer ventilator-free days, and longer ICU length of stay. Adjusted by duration of mechanical ventilation and severity of illness, high cumulative duration of clusters was associated with shorter survival at 28 days (hazard ratio, 2.09 d; 95% CI, 1.04-4.19 d).

CONCLUSIONS

Clusters of double triggering are common and were associated with worse clinical outcomes. Patients who had a high cumulative duration of clusters had fewer ventilator-free days, longer duration of mechanical ventilation, longer ICU length of stay, and shorter survival than patients with low cumulative duration of cluster.

Oxygen administration for patients with ARDS

Acute respiratory distress syndrome (ARDS) is a fatal condition with insufficiently clarified etiology. Supportive care for severe hypoxemia remains the mainstay of essential interventions for ARDS. In recent years, adequate ventilation to prevent ventilator-induced lung injury (VILI) and patient self-inflicted lung injury (P-SILI) as well as lung-protective mechanical ventilation has an increasing attention in ARDS.

Ventilation-perfusion mismatch may augment severe hypoxemia and inspiratory drive and consequently induce P-SILI. Respiratory drive and effort must also be carefully monitored to prevent P-SILI. Airway occlusion pressure (P_0.1) and airway pressure deflection during an end-expiratory airway occlusion (P_occ) could be easy indicators to evaluate the respiratory drive and effort. Patient-ventilator dyssynchrony is a time mismatching between patient’s effort and ventilator drive. Although it is frequently unrecognized, dyssynchrony can be associated with poor clinical outcomes. Dyssynchrony includes trigger asynchrony, cycling asynchrony, and flow delivery mismatch. Ventilator-induced diaphragm dysfunction (VIDD) is a form of iatrogenic injury from inadequate use of mechanical ventilation. Excessive spontaneous breathing can lead to P-SILI, while excessive rest can lead to VIDD. Optimal balance between these two manifestations is probably associated with the etiology and severity of the underlying pulmonary disease.

High-flow nasal cannula (HFNC) and non-invasive positive pressure ventilation (NPPV) are non-invasive techniques for supporting hypoxemia. While they are beneficial as respiratory supports in mild ARDS, there can be a risk of delaying needed intubation. Mechanical ventilation and ECMO are applied for more severe ARDS. However, as with HFNC/NPPV, inappropriate assessment of breathing workload potentially has a risk of delaying the timing of shifting from ventilator to ECMO. Various methods of oxygen administration in ARDS are important. However, it is also important to evaluate whether they adequately reduce the breathing workload and help to improve ARDS.

Reverse Trigger Phenotypes in Acute Respiratory Distress Syndrome

Rationale: Reverse triggering is an underexplored form of dyssynchrony with important clinical implications in patients with acute respiratory distress syndrome.Objectives: This retrospective study identified reverse trigger phenotypes and characterized their impacts on Vt and transpulmonary pressure.Methods: Fifty-five patients with acute respiratory distress syndrome on pressure-regulated ventilator modes were included. Four phenotypes of reverse triggering with and without breath stacking and their impact on lung inflation and deflation were investigated.Measurements and Main Results: Inflation volumes, respiratory muscle pressure generation, and transpulmonary pressures were determined and phenotypes differentiated using Campbell diagrams of respiratory activity. Reverse triggering was detected in 25 patients, 15 with associated breath stacking, and 13 with stable reverse triggering consistent with respiratory entrainment. Phenotypes were associated with variable levels of inspiratory effort (mean 4-10 cm H₂O per phenotype). Early reverse triggering with early expiratory relaxation increased Vts (88 [64-113] ml) and inspiratory transpulmonary pressures (3 [2-3] cm H₂O) compared with passive breaths. Early reverse triggering with delayed expiratory relaxation increased Vts (128 [86-170] ml) and increased inspiratory and mean-expiratory transpulmonary pressure (7 [5-9] cm H₂O and 5 [4-6] cm H₂O). Mid-cycle reverse triggering (initiation during inflation and maximal effort during deflation) increased Vt (51 [38-64] ml), increased inspiratory and mean-expiratory transpulmonary pressure (3 [2-4] cm H₂O and 3 [2-3] cm H₂O), and caused incomplete exhalation. Late reverse triggering (occurring exclusively during exhalation) increased mean expiratory transpulmonary pressure (2 [1-2] cm H₂O) and caused incomplete exhalation. Breath stacking resulted in large delivered volumes (176 [155-197] ml).Conclusions: Reverse triggering causes variable physiological effects, depending on the phenotype. Differentiation of phenotype effects may be important to understand the clinical impacts of these events.

Impact of Novel Multiinstitutional Curriculum on Critical Care Fellow Ventilator Knowledge

Background: Management of mechanical ventilation (MV) is a curricular milestone for trainees in pulmonary critical care medicine (PCCM) and critical care medicine (CCM) fellowships. Though recognition of ventilator waveform abnormalities that could result in patient complications is an important part of management, it is unclear how well fellows recognize these abnormalities.

Objective: To study proficiency of ventilator waveform analysis among first-year fellows enrolled in a MV course compared with that of traditionally trained fellows.

Methods: The study took place from July 2016 to January 2019, with 93 fellows from 10 fellowship programs completing the waveform examination. Seventy-three fellows participated in a course during their first year of fellowship, with part I occurring at the beginning of fellowship in July and part II occurring after 6 months of clinical work. These fellows were given a five-question ventilator waveform examination at multiple time points throughout the two-part course. Twenty fellows from three other fellowship programs who were in their first, second, or third year of fellowship and who did not participate in this course served as the control group. These fellows took the waveform examination a single time, at a median of 23 months into their training.

Results: Before the course, scores were low but improved after 3 days of education at the beginning of the fellowship (18.0 ± 1.6 vs. 45.6 ± 3.0; P < 0.0001). Scores decreased after 6 months of clinical rotations but increased to their highest levels after part II of the course (33.7 ± 3.1 for part II pretest vs. 77.4 ± 2.4 for part II posttest; P < 0.0001). After completing part I at the beginning of fellowship, fellows participating in the course outperformed control fellows, who received a median of 23 months of traditional fellowship training at the time of testing (45.6 ± 3.0 vs. 25.3 ± 2.7; P < 0.0001). There was no difference in scores between PCCM and CCM fellows. In anonymous surveys, the fellows also rated the mechanical ventilator lectures highly.

Conclusion: PCCM and CCM fellows do not recognize common waveform abnormalities at the beginning of fellowship but can be trained to do so. Traditional fellowship training may be insufficient to master ventilator waveform analysis, and a more intentional, structured course for MV may help fellowship programs meet the curricular milestones for MV.

NAVA and PAV+ for lung and diaphragm protection

PURPOSE OF REVIEW

Complications of mechanical ventilation, such as ventilator-induced lung injury (VILI) and ventilator-induced diaphragmatic dysfunction (VIDD), adversely affect the outcome of critically ill patients. Although mostly studied during control ventilation, it is increasingly appreciated that VILI and VIDD also occur during assisted ventilation. Hence, current research focuses on identifying ways to monitor and deliver protective ventilation in assisted modes. This review describes the operating principles of proportional modes of assist, their implications for lung and diaphragm protective ventilation, and the supporting clinical data.

RECENT FINDINGS

Proportional modes of assist, proportional assist ventilation, PAV, and neurally adjusted ventilatory assist, NAVA, deliver a pressure assist that is proportional to the patient's effort, enabling ventilation to be better controlled by the patient's brain. This control underlies the potential of proportional modes to avoid over-assist and under-assist, improve patient--ventilator interaction, and provide protective ventilation. Indeed, in clinical studies, proportional modes have been associated with reduced asynchronies, enhanced diaphragmatic recovery, and limitation of excessive tidal volume. Additionally, proportional modes facilitate better monitoring of the delivery of protective assisted ventilation.

SUMMARY

Physiological rationale and clinical data suggest a potential role for proportional modes of assist in providing and monitoring lung and diaphragm protective ventilation.

Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.

Prevalence of Reverse Triggering in Early ARDS: Results From a Multicenter Observational Study

BACKGROUND

The prevalence of reverse triggering (RT) in the early phase of ARDS is unknown.

RESEARCH QUESTION

During early ARDS, what is the proportion of patients affected by RT, what are its potential predictors, and what is its association with clinical outcomes?

STUDY DESIGN AND METHODS

This was prospective, multicenter, and observational study. Patients who met the Berlin definition of ARDS with less than 72 h of mechanical ventilation and had not been paralyzed with neuromuscular blockers were screened. A 30-min recording of respiratory signals was obtained from the patients as soon as they were enrolled, and the number of breaths with RT were counted.

RESULTS

One hundred patients were included. ARDS was mild to moderate in 92% of them. The recordings were obtained after a median of 1 day (interquartile range, 1-2 days) of ventilation. Fifty patients had RT, and most of these events (97%) were not associated with breath stacking. Detecting RT was associated with lower tidal volume (Vt) and less opiate infusion. The presence of RT was not associated with time to discontinuation of mechanical ventilation (subdistribution hazard ratio, 1.03; 95% CI, 0.6-1.77), but it possibly was associated with a reduced hospital mortality (hazard ratio, 0.65; 95% CI, 0.57-0.73).

INTERPRETATION

Fifty percent of patients receiving assist-control ventilation for mild or moderate ARDS, sedated and nonparalyzed, demonstrate RT without breath stacking on the first day of mechanical ventilation. RT may be associated with low VTS and opiate doses.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT02732041; URL: www.clinicaltrials.gov.

Patient-ventilator dyssynchronies: Are they all the same? A clinical classification to guide actions

Patient ventilatory dyssynchrony (PVD) is a mismatch between the respiratory drive of the patient and ventilatory assistance. It is a complex event seen in almost all ventilated patients and at any ventilator mode, with uncertain significance and prognosis. Due to its different pathophysiological mechanisms, there is still not consensual classification to guide us in selecting the best treatment. In the present review we aimed to summarize some clinical data on PVD, and to propose a clinical classification based on the type of PVD, from potentially innocuous to clearly harmful PVD, which could help clinicians in the decision-making process from adjusting ventilator settings to deeply sedate or paralyze the patient. Clearly, further studies are needed addressing risk factors, physiologic mechanisms and direct consequences of PVD in order to help clinicians to design effective and proven strategies at the bedside.

Neurally adjusted ventilatory assist vs. pressure support to deliver protective mechanical ventilation in patients with acute respiratory distress syndrome: a randomized crossover trial

Background

Protective mechanical ventilation is recommended for patients with acute respiratory distress syndrome (ARDS), but it usually requires controlled ventilation and sedation. Using neurally adjusted ventilatory assist (NAVA) or pressure support ventilation (PSV) could have additional benefits, including the use of lower sedative doses, improved patient–ventilator interaction and shortened duration of mechanical ventilation. We designed a pilot study to assess the feasibility of keeping tidal volume (V_T) at protective levels with NAVA and PSV in patients with ARDS.

Methods

We conducted a prospective randomized crossover trial in five ICUs from a university hospital in Brazil and included patients with ARDS transitioning from controlled ventilation to partial ventilatory support. NAVA and PSV were applied in random order, for 15 min each, followed by 3 h in NAVA. Flow, peak airway pressure (Paw) and electrical activity of the diaphragm (EAdi) were captured from the ventilator, and a software (Matlab, Mathworks, USA), automatically detected inspiratory efforts and calculated respiratory rate (RR) and V_T. Asynchrony events detection was based on waveform analysis.

Results

We randomized 20 patients, but the protocol was interrupted for five (25%) patients for whom we were unable to maintain V_T below 6.5 mL/kg in PSV due to strong inspiratory efforts and for one patient for whom we could not detect EAdi signal. For the 14 patients who completed the protocol, V_T was 5.8 ± 1.1 mL/kg for NAVA and 5.6 ± 1.0 mL/kg for PSV (p = 0.455) and there were no differences in RR (24 ± 7 for NAVA and 23 ± 7 for PSV, p = 0.661). Paw was greater in NAVA (21 ± 3 cmH₂O) than in PSV (19 ± 3 cmH₂O, p = 0.001). Most patients were under continuous sedation during the study. NAVA reduced triggering delay compared to PSV (p = 0.020) and the median asynchrony Index was 0.7% (0–2.7) in PSV and 0% (0–2.2) in NAVA (p = 0.6835).

Conclusions

It was feasible to keep V_T in protective levels with NAVA and PSV for 75% of the patients. NAVA resulted in similar V_T, RR and Paw compared to PSV. Our findings suggest that partial ventilatory assistance with NAVA and PSV is feasible as a protective ventilation strategy in selected ARDS patients under continuous sedation.

Trial registration ClinicalTrials.gov (NCT01519258). Registered 26 January 2012, https://clinicaltrials.gov/ct2/show/NCT01519258

Predictors of asynchronies during assisted ventilation and its impact on clinical outcomes: The EPISYNC cohort study

PURPOSE

To investigate if respiratory mechanics and other baseline characteristics are predictors of patient-ventilator asynchrony and to evaluate the relationship between asynchrony during assisted ventilation and clinical outcomes.

METHODS

We performed a prospective cohort study in patients under mechanical ventilation (MV). Baseline measurements included severity of illness and respiratory mechanics. The primary outcome was the Asynchrony Index (AI), defined as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. We recorded ventilator waveforms throughout the entire period of MV.

RESULTS

We analyzed 11,881 h of MV from 103 subjects. Median AI during the entire period of MV was 5.1% (IQR:2.6-8.7). Intrinsic PEEP was associated with AI (OR:1.72, 95%CI:1.1-2.68), but static compliance and airway resistance were not. Simplified Acute Physiology Score 3 (OR:1.03, 95%CI:1-1.06) was also associated with AI. Median AI was higher during assisted (5.4%, IQR:2.9-9.1) than controlled (2%, IQR:0.6-4.9) ventilation, and 22% of subjects had high incidence of asynchrony (AI≥10%). Subjects with AI≥10% had more extubation failure (33%) than patients with AI<10% (6%), p = .01.

CONCLUSIONS

Predictors of high incidence of asynchrony were severity of illness and intrinsic PEEP. High incidence of asynchrony was associated with extubation failure, but not mortality.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02687802.

Identifying and managing patient-ventilator asynchrony: An international survey

OBJECTIVE

To describe the main factors associated with proper recognition and management of patient-ventilator asynchrony (PVA).

DESIGN

An analytical cross-sectional study was carried out.

SETTING

An international study conducted in 20 countries through an online survey.

PARTICIPANTS

Physicians, respiratory therapists, nurses and physiotherapists currently working in the Intensive Care Unit (ICU).

MAIN VARIABLES OF INTEREST

Univariate and multivariate logistic regression models were used to establish associations between all variables (profession, training in mechanical ventilation, type of training program, years of experience and ICU characteristics) and the ability of HCPs to correctly identify and manage 6 PVA.

RESULTS

A total of 431 healthcare professionals answered a validated survey. The main factors associated to proper recognition of PVA were: specific training program in mechanical ventilation (MV) (OR 2.27; 95%CI 1.14-4.52; p=0.019), courses with more than 100h completed (OR 2.28; 95%CI 1.29-4.03; p=0.005), and the number of ICU beds (OR 1.037; 95%CI 1.01-1.06; p=0.005). The main factor influencing the management of PVA was the correct recognition of 6 PVAs (OR 118.98; 95%CI 35.25-401.58; p<0.001).

CONCLUSION

Identifying and managing PVA using ventilator waveform analysis is influenced by many factors, including specific training programs in MV, the number of ICU beds, and the number of recognized PVAs.

Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities

BACKGROUND

Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes.

MAIN BODY

Asynchronies occur when ventilator assistance does not match the patient's demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management.

CONCLUSIONS

Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

Effects of sedatives and opioids on trigger and cycling asynchronies throughout mechanical ventilation: an observational study in a large dataset from critically ill patients

Background

In critically ill patients, poor patient-ventilator interaction may worsen outcomes. Although sedatives are often administered to improve comfort and facilitate ventilation, they can be deleterious. Whether opioids improve asynchronies with fewer negative effects is unknown. We hypothesized that opioids alone would improve asynchronies and result in more wakeful patients than sedatives alone or sedatives-plus-opioids.

Methods

This prospective multicenter observational trial enrolled critically ill adults mechanically ventilated (MV) > 24 h. We compared asynchronies and sedation depth in patients receiving sedatives, opioids, or both. We recorded sedation level and doses of sedatives and opioids. BetterCare™ software continuously registered ineffective inspiratory efforts during expiration (IEE), double cycling (DC), and asynchrony index (AI) as well as MV modes. All variables were averaged per day. We used linear mixed-effects models to analyze the relationships between asynchronies, sedation level, and sedative and opioid doses.

Results

In 79 patients, 14,166,469 breaths were recorded during 579 days of MV. Overall asynchronies were not significantly different in days classified as sedatives-only, opioids-only, and sedatives-plus-opioids and were more prevalent in days classified as no-drugs than in those classified as sedatives-plus-opioids, irrespective of the ventilatory mode. Sedative doses were associated with sedation level and with reduced DC (p < 0.0001) in sedatives-only days. However, on days classified as sedatives-plus-opioids, higher sedative doses and deeper sedation had more IEE (p < 0.0001) and higher AI (p = 0.0004). Opioid dosing was inversely associated with overall asynchronies (p < 0.001) without worsening sedation levels into morbid ranges.

Conclusions

Sedatives, whether alone or combined with opioids, do not result in better patient-ventilator interaction than opioids alone, in any ventilatory mode. Higher opioid dose (alone or with sedatives) was associated with lower AI without depressing consciousness. Higher sedative doses administered alone were associated only with less DC.

Trial registration

ClinicalTrial.gov, NCT03451461

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2531-5) contains supplementary material, which is available to authorized users.

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

INTRODUCTION

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

TRIAL REGISTRATION NUMBER

NCT02687802; Pre-results.

Predicting Patient-ventilator Asynchronies with Hidden Markov Models

In mechanical ventilation, it is paramount to ensure the patient's ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) - z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.