Impact of Expiratory Trigger Setting on Delayed Cycling and Inspiratory Muscle Workload

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

Background

Whether respiratory efforts and their timing can be reliably detected during pressure support ventilation using standard ventilator waveforms is unclear. This would give the opportunity to assess and improve patient–ventilator interaction without the need of special equipment.

Methods

In 16 patients under invasive pressure support ventilation, flow and pressure waveforms were obtained from proximal sensors and analyzed by three trained physicians and one resident to assess patient’s spontaneous activity. A systematic method (the waveform method) based on explicit rules was adopted. Esophageal pressure tracings were analyzed independently and used as reference. Breaths were classified as assisted or auto-triggered, double-triggered or ineffective. For assisted breaths, trigger delay, early and late cycling (minor asynchronies) were diagnosed. The percentage of breaths with major asynchronies (asynchrony index) and total asynchrony time were computed.

Results

Out of 4426 analyzed breaths, 94.1% (70.4–99.4) were assisted, 0.0% (0.0–0.2) auto-triggered and 5.8% (0.4–29.6) ineffective. Asynchrony index was 5.9% (0.6–29.6). Total asynchrony time represented 22.4% (16.3–30.1) of recording time and was mainly due to minor asynchronies. Applying the waveform method resulted in an inter-operator agreement of 0.99 (0.98–0.99); 99.5% of efforts were detected on waveforms and agreement with the reference in detecting major asynchronies was 0.99 (0.98–0.99). Timing of respiratory efforts was accurately detected on waveforms: AUC for trigger delay, cycling delay and early cycling was 0.865 (0.853–0.876), 0.903 (0.892–0.914) and 0.983 (0.970–0.991), respectively.

Conclusions

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

Supplementary Information

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

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

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

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

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

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

Patient-Ventilator Dyssynchrony in Critically Ill Patients

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

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-ventilator dyssynchrony in the intensive care unit: A practical approach to diagnosis and management

Patient-ventilator dyssynchrony or asynchrony occurs when, for any parameter of respiration, discordance exists between the patient's spontaneous effort and the ventilator's provided support. If not recognised, it may promote oversedation, prolong the duration of mechanical ventilation, create risk for lung injury, and generally confuse the clinical picture. Seven forms of dyssynchrony are common: (a) ineffective triggering; (b) autotriggering; (c) inadequate flow; (d) too much flow; (e) premature cycling; (f) delayed cycling; and (g) peak pressure apnoea. 'Reverse triggering' also occurs and may mimic premature cycling. Correct diagnosis of these phenomena often permits management by simple ventilator optimisation rather than by less desirable measures.

Neural control of pressure support ventilation improved patient-ventilator synchrony in patients with different respiratory system mechanical properties: a prospective, crossover trial

Background

Conventional pressure support ventilation (PS_P) is triggered and cycled off by pneumatic signals such as flow. Patient-ventilator asynchrony is common during pressure support ventilation, thereby contributing to an increased inspiratory effort. Using diaphragm electrical activity, neurally controlled pressure support (PS_N) could hypothetically eliminate the asynchrony and reduce inspiratory effort. The purpose of this study was to compare the differences between PS_N and PS_P in terms of patient-ventilator synchrony, inspiratory effort, and breathing pattern.

Methods

Eight post-operative patients without respiratory system comorbidity, eight patients with acute respiratory distress syndrome (ARDS) and obvious restrictive acute respiratory failure (ARF), and eight patients with chronic obstructive pulmonary disease (COPD) and mixed restrictive and obstructive ARF were enrolled. Patient-ventilator interactions were analyzed with macro asynchronies (ineffective, double, and auto triggering), micro asynchronies (inspiratory trigger delay, premature, and late cycling), and the total asynchrony index (AI). Inspiratory efforts for triggering and total inspiration were analyzed.

Results

Total AI of PS_N was consistently lower than that of PS_P in COPD (3% vs. 93%, P = 0.012 for 100% support level; 8% vs. 104%, P = 0.012 for 150% support level), ARDS (8% vs. 29%, P = 0.012 for 100% support level; 16% vs. 41%, P = 0.017 for 150% support level), and post-operative patients (21% vs. 35%, P = 0.012 for 100% support level; 15% vs. 50%, P = 0.017 for 150% support level). Improved support levels from 100% to 150% statistically increased total AI during PS_P but not during PS_N in patients with COPD or ARDS. Patients’ inspiratory efforts for triggering and total inspiration were significantly lower during PS_N than during PS_P in patients with COPD or ARDS under both support levels (P < 0.05). There was no difference in breathing patterns between PS_N and PS_P.

Conclusions

PS_N improves patient-ventilator synchrony and generates a respiratory pattern similar to PS_P independently of any level of support in patients with different respiratory system mechanical properties. PS_N, which reduces the trigger and total patient's inspiratory effort in patients with COPD or ARDS, might be an alternative mode for PS_P.

Trial Registration

ClinicalTrials.gov, NCT01979627; https://clinicaltrials.gov/ct2/show/record/NCT01979627.

Prolonged Weaning: S2k Guideline Published by the German Respiratory Society

Mechanical ventilation (MV) is an essential part of modern intensive care medicine. MV is performed in patients with severe respiratory failure caused by respiratory muscle insufficiency and/or lung parenchymal disease; that is, when other treatments such as medication, oxygen administration, secretion management, continuous positive airway pressure (CPAP), or nasal high-flow therapy have failed. MV is required for maintaining gas exchange and allows more time to curatively treat the underlying cause of respiratory failure. In the majority of ventilated patients, liberation or "weaning" from MV is routine, without the occurrence of any major problems. However, approximately 20% of patients require ongoing MV, despite amelioration of the conditions that precipitated the need for it in the first place. Approximately 40-50% of the time spent on MV is required to liberate the patient from the ventilator, a process called "weaning". In addition to acute respiratory failure, numerous factors can influence the duration and success rate of the weaning process; these include age, comorbidities, and conditions and complications acquired during the ICU stay. According to international consensus, "prolonged weaning" is defined as the weaning process in patients who have failed at least 3 weaning attempts, or require more than 7 days of weaning after the first spontaneous breathing trial (SBT). Given that prolonged weaning is a complex process, an interdisciplinary approach is essential for it to be successful. In specialised weaning centres, approximately 50% of patients with initial weaning failure can be liberated from MV after prolonged weaning. However, the heterogeneity of patients undergoing prolonged weaning precludes the direct comparison of individual centres. Patients with persistent weaning failure either die during the weaning process, or are discharged back to their home or to a long-term care facility with ongoing MV. Urged by the growing importance of prolonged weaning, this Sk2 Guideline was first published in 2014 as an initiative of the German Respiratory Society (DGP), in conjunction with other scientific societies involved in prolonged weaning. The emergence of new research, clinical study findings and registry data, as well as the accumulation of experience in daily practice, have made the revision of this guideline necessary. The following topics are dealt with in the present guideline: Definitions, epidemiology, weaning categories, underlying pathophysiology, prevention of prolonged weaning, treatment strategies in prolonged weaning, the weaning unit, discharge from hospital on MV, and recommendations for end-of-life decisions. Special emphasis was placed on the following themes: (1) A new classification of patient sub-groups in prolonged weaning. (2) Important aspects of pulmonary rehabilitation and neurorehabilitation in prolonged weaning. (3) Infrastructure and process organisation in the care of patients in prolonged weaning based on a continuous treatment concept. (4) Changes in therapeutic goals and communication with relatives. Aspects of paediatric weaning are addressed separately within individual chapters. The main aim of the revised guideline was to summarize both current evidence and expert-based knowledge on the topic of "prolonged weaning", and to use this information as a foundation for formulating recommendations related to "prolonged weaning", not only in acute medicine but also in the field of chronic intensive care medicine. The following professionals served as important addressees for this guideline: intensivists, pulmonary medicine specialists, anaesthesiologists, internists, cardiologists, surgeons, neurologists, paediatricians, geriatricians, palliative care clinicians, rehabilitation physicians, intensive/chronic care nurses, physiotherapists, respiratory therapists, speech therapists, medical service of health insurance, and associated ventilator manufacturers.

Long-Term Mechanical Ventilation: Recommendations of the Swiss Society of Pulmonology

Long-term mechanical ventilation is a well-established treatment for chronic hypercapnic respiratory failure (CHRF). It is aimed at improving CHRF-related symptoms, health-related quality of life, survival, and decreasing hospital admissions. In Switzerland, long-term mechanical ventilation has been increasingly used since the 1980s in hospital and home care settings. Over the years, its application has considerably expanded with accumulating evidence of beneficial effects in a broad range of conditions associated with CHRF. Most frequent indications for long-term mechanical ventilation are chronic obstructive pulmonary disease, obesity hypoventilation syndrome, neuromuscular and chest wall diseases. In the current consensus document, the Special Interest Group of the Swiss Society of Pulmonology reviews the most recent scientific literature on long-term mechanical ventilation and provides recommendations adapted to the particular setting of the Swiss healthcare system with a focus on the practice of non-invasive and invasive home ventilation in adults.

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.

Comparison of Inspiratory Effort, Workload and Cycling Synchronization Between Non-Invasive Proportional-Assist Ventilation and Pressure-Support Ventilation Using Different Models of Respiratory Mechanics

Background

This study assessed lung models for the influence of respiratory mechanics and inspiratory effort on breathing pattern and simulator-ventilator cycling synchronization in non-invasive ventilation.

Material/Methods

A Respironics V60 ventilator was connected to an active lung simulator modeling mildly restrictive, severely restrictive, obstructive and mixed obstructive/restrictive profiles. Pressure-support ventilation (PSV) and proportional-assist ventilation (PAV) were set to obtain similar tidal volume (V_T). PAV was applied at flow assist (FA) 40–90% of resistance (Rrs) and volume assist (VA) 40–90% of elastance (Ers). Measurements were performed with system air leak of 25–28 L/minute. Ventilator performance and simulator-ventilator asynchrony were evaluated.

Results

At comparable V_T, PAV had slightly lower peak inspiratory flow and higher driving pressure compared with PSV. Premature cycling occurred in the obstructive, severely restrictive and mildly restrictive models. During PAV, time for airway pressure to achieve 90% of maximum during inspiration (T90) in the severely restrictive model was shorter than those of the obstructive and mixed obstructive/restrictive models and close to that measured in the PSV mode. Increasing FA level reduced inspiratory trigger workload (PTP₃₀₀) in obstructive and mixed obstructive/restrictive models. Increasing FA level decreased inspiratory time (T_I) and tended to aggravate premature cycling, whereas increasing VA level attenuated this effect.

Conclusions

PAV with an appropriate combination of FA and VA decreases work of breathing during the inspiratory phase and improves simulator-ventilator cycling synchrony. FA has greater impact than VA in the adaptation to inspiratory effort demand. High VA level might help improve cycling synchrony.

Patient-ventilator asynchrony in adult critically ill patients

INTRODUCTION

Patient-ventilator asynchrony is considered a major clinical problem for mechanically ventilated patients. It occurs during partial ventilatory support, when the respiratory muscles and the ventilator interact to contribute generating the volume output. In this review article, we consider all studies published on patient-ventilator asynchrony in the last 25 years.

EVIDENCE ACQUISITION

We selected 62 studies. The different forms of asynchrony are first defined and classified. We also describe the methods used for detecting and quantifying asynchronies. We then outline the outcome variables considered for evaluating the clinical consequences of asynchronies. The methodology for detection and quantification of patient-ventilator asynchrony are quite heterogeneous. In particular, the Asynchrony Index is calculated differently among studies.

EVIDENCE SYNTHESIS

Sixteen studies established some relationship between asynchronies and one or more clinical outcomes, such as duration of mechanical ventilation (seven studies), mortality (five studies), length of intensive care and hospital stay (four studies), patient comfort (four studies), quality of sleep (three studies), and rate of tracheotomy (three studies). In patients with severe patient-ventilator asynchrony, four of seven studies (57%) report prolonged duration of mechanical ventilation, one of five (20%) increased mortality, one of four (25%) longer intensive care and hospital lengths of stay, four of four (100%) worsened comfort, three of four (75%) deteriorated quality of sleep, and one of three (33%) increased rate of tracheotomy.

CONCLUSIONS

Given the varying outcomes considered and the erratic results, it remains unclear whether asynchronies really affects patient outcome, and the relationship between asynchronies and outcome is causative or associative.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The Pathophysiology of Respiratory Failure: Control of Breathing, Respiratory Load, and Muscle Capacity

The purpose of this review is to provide an overview on how interactions between control of breathing, respiratory load, and muscle function may lead to respiratory failure. The mechanisms involved vary according to the underlying pathology, but respiratory failure is most often the result of an imbalance between the muscular pump and the mechanical load placed upon it. Changes in respiratory drive and response to CO2 seem to be important contributors to the pathophysiology of respiratory failure. Inspiratory muscle dysfunction is also frequent but is not a mandatory prerequisite to respiratory failure since increased load may also be sufficient to precipitate it. It is crucial to recognize these interactions to be able to timeously establish patients on mechanical ventilation and adapt the ventilator settings to their respiratory system physiology.

Design of a new artificial breathing system for simulating the human respiratory activities

The purpose of this work is the conception and implementation of an artificial active respiratory system that allows the simulation of human respiratory activities. The system consists of two modules, mechanical and electronical. The first one represents a cylindrical lung adjustable in resistance and compliance. This lung is located inside a transparent thoracic box, connected to a piston that generates variable respiratory efforts. The parameters of the system, which are pressure, flow and volume, are measured by the second module. A computer application was developed to control the whole system, and enables the display of the parameters. A series of tests were made to evaluate the respiratory efforts, resistances and compliances. The results were compared to the bibliographical studies, allowing the validation of the proposed system.

Looking under the bonnet of patient-ventilator asynchrony during noninvasive ventilation: does it add value?

Noninvasive ventilation (NIV) has significant advantages over invasive ventilation in certain situations, such as hypercapnic respiratory failure due to an acute exacerbation of chronic obstructive pulmonary disease (COPD), acute cardiogenic pulmonary oedema, respiratory failure in immunocompromised patients and weaning from invasive ventilation in patients with COPD, in terms of reducing mortality, duration of hospital stay, the need for intubation and infectious complications [1–5].

During noninvasive ventilation it is reasonable to try to abolish clinically apparent patient-ventilator asynchronies but the use of more invasive tests to detect subtle asynchronies cannot be justifiedhttp://ow.ly/rXoA30gCm8O

Spontaneous breathing: a double-edged sword to handle with care

In acute hypoxemic respiratory failure (AHRF) and acute respiratory distress syndrome (ARDS) patients, spontaneous breathing is associated with multiple physiologic benefits: it prevents muscles atrophy, avoids paralysis, decreases sedation needs and is associated with improved hemodynamics. On the other hand, in the presence of uncontrolled inspiratory effort, severe lung injury and asynchronies, spontaneous ventilation might also worsen lung edema, induce diaphragm dysfunction and lead to muscles exhaustion and prolonged weaning. In the present review article, we present physiologic mechanisms driving spontaneous breathing, with emphasis on how to implement basic and advanced respiratory monitoring to assess lung protection during spontaneous assisted ventilation. Then, key benefits and risks associated with spontaneous ventilation are described. Finally, we propose some clinical means to promote protective spontaneous breathing at the bedside. In summary, early switch to spontaneous assisted breathing of acutely hypoxemic patients is more respectful of physiology and might yield several advantages. Nonetheless, risk of additional lung injury is not completely avoided during spontaneous breathing and careful monitoring of target physiologic variables such as tidal volume (Vt) and driving transpulmonary pressure should be applied routinely. In clinical practice, multiple interventions such as extracorporeal CO₂ removal exist to maintain inspiratory effort, Vt and driving transpulmonary pressure within safe limits but more studies are needed to assess their long-term efficacy.

Intermittent noninvasive ventilation after extubation in patients with chronic respiratory disorders: a multicenter randomized controlled trial (VHYPER)

PURPOSE

Early noninvasive ventilation (NIV) after extubation decreases the risk of respiratory failure and lowers 90-day mortality in patients with hypercapnia. Patients with chronic respiratory disease are at risk of extubation failure. Therefore, it could be useful to determine the role of NIV with a discontinuous approach, not limited to patients with hypercapnia. We assessed the efficacy of early NIV in decreasing respiratory failure after extubation in patients with chronic respiratory disorders.

METHODS

A prospective randomized controlled multicenter study was conducted. We enrolled 144 mechanically ventilated patients with chronic respiratory disorders who tolerated a spontaneous breathing trial. Patients were randomly allocated after extubation to receive either NIV (NIV group, n = 72), performed with a discontinuous approach, for the first 48 h, or conventional oxygen treatment (usual care group, n = 72). The primary endpoint was decreased respiratory failure within 48 h after extubation. Analysis was by intention to treat. This trial was registered with ClinicalTrials.gov (NCT01047852).

RESULTS

Respiratory failure after extubation was less frequent in the NIV group: 6 (8.5%) versus 20 (27.8%); p = 0.0016. Six patients (8.5%) in the NIV group versus 13 (18.1%) in the usual care group were reintubated; p = 0.09. Intensive care unit (ICU) mortality and 90-day mortality did not differ significantly between the two groups (p = 0.28 and p = 0.33, respectively). Median postrandomization ICU length of stay was lower in the usual care group: 3 days (IQR 2-6) versus 4 days (IQR 2-7; p = 0.008). Patients with hypercapnia during a spontaneous breathing trial were at risk of developing postextubation respiratory failure [adjusted odds ratio (95% CI) = 4.56 (1.59-14.00); p = 0.006] and being intubated [adjusted odds ratio (95% CI) = 3.60 (1.07-13.31); p = 0.04].

CONCLUSIONS

Early NIV performed following a sequential protocol for the first 48 h after extubation decreased the risk of respiratory failure in patients with chronic respiratory disorders. Reintubation and mortality did not differ between NIV and conventional oxygen therapy.

Onset and Offset Estimation of the Neural Inspiratory Time in Surface Diaphragm Electromyography: A Pilot Study in Healthy Subjects

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (T_i/T_tot) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdi_fse). The neural onset was detected through a dynamic threshold over the EMGdi_fse using the kernel density estimation method, while neural offset was detected by finding when the EMGdi_fse had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the T_i /T_tot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the T_i/T _tot protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of T_i/T_tot values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

A diaphragmatic electrical activity-based optimization strategy during pressure support ventilation improves synchronization but does not impact work of breathing

Background

Poor patient-ventilator synchronization is often observed during pressure support ventilation (PSV) and has been associated with prolonged duration of mechanical ventilation and poor outcome. Diaphragmatic electrical activity (Eadi) recorded using specialized nasogastric tubes is a surrogate of respiratory brain stem output. This study aimed at testing whether adapting ventilator settings during PSV using a protocolized Eadi-based optimization strategy, or Eadi-triggered and -cycled assisted pressure ventilation (or PSV_N) could (1) improve patient-ventilator interaction and (2) reduce or normalize patient respiratory effort as estimated by the work of breathing (WOB) and the pressure time product (PTP).

Methods

This was a prospective cross-over study. Patients with a known chronic pulmonary obstructive or restrictive disease, asynchronies or suspected intrinsic positive end-expiratory pressure (PEEP) who were ventilated using PSV were enrolled in the study. Four different ventilator settings were sequentially applied for 15 minutes (step 1: baseline PSV as set by the clinician, step 2: Eadi-optimized PSV to adjust PS level, inspiratory trigger, and cycling settings, step 3: step 2 + PEEP adjustment, step 4: PSV_N). The same settings as step 3 were applied again after step 4 to rule out a potential effect of time. Breathing pattern, trigger delay (T_d), inspiratory time in excess (T_iex), pressure-time product (PTP), and work of breathing (WOB) were measured at the end of each step.

Results

Eleven patients were enrolled in the study. Eadi-optimized PSV reduced T_d without altering T_iex in comparison with baseline PSV. PSV_N reduced T_d and T_iex in comparison with baseline and Eadi-optimized PSV. Respiratory pattern did not change during the four steps. The improvement in patient-ventilator interaction did not lead to changes in WOB or PTP.

Conclusions

Eadi-optimized PSV allows improving patient ventilator interaction but does not alter patient effort in patients with mild asynchrony.

Trial registration

Clinicaltrials.gov identifier: NCT 02067403. Registered 7 February 2014.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1599-z) contains supplementary material, which is available to authorized users.

Paediatric ED BiPAP continuous quality improvement programme with patient analysis: 2005-2013

OBJECTIVE

In paediatric moderate-to-severe asthmatics, there is significant bronchospasm, airway obstruction, air trapping causing severe hyperinflation with more positive intraplural pressure preventing passive air movement. These effects cause an increased respiratory rate (RR), less airflow and shortened inspiratory breath time. In certain asthmatics, aerosols are ineffective due to their inadequate ventilation. Bilevel positive airway pressure (BiPAP) in acute paediatric asthmatics can be an effective treatment. BiPAP works by unloading fatigued inspiratory muscles, a direct bronchodilation effect, offsetting intrinsic PEEP and recruiting collapsed alveoli that reduces the patient's work of breathing and achieves their total lung capacity quicker. Unfortunately, paediatric emergency department (PED) BiPAP is underused and quality analysis is non-existent. A PED BiPAP Continuous Quality Improvement Program (CQIP) from 2005 to 2013 was evaluated using descriptive analytics for the primary outcomes of usage, safety, BiPAP settings, therapeutics and patient disposition.

INTERVENTIONS

PED BiPAP CQIP descriptive analytics.

SETTING

Academic PED.

PARTICIPANTS

1157 patients.

INTERVENTIONS

A PED BiPAP CQIP from 2005 to 2013 for the usage, safety, BiPAP settings, therapeutic response parameters and patient disposition was evaluated using descriptive analytics.

PRIMARY AND SECONDARY OUTCOMES

Safety, usage, compliance, therapeutic response parameters, BiPAP settings and patient disposition.

RESULTS

1157 patients had excellent compliance without complications. Only 6 (0.5%) BiPAP patients were intubated. BiPAP median settings: IPAP 18 (16,20) cm H₂O range 12-28; EPAP 8 cmH₂O (8,8) range 6-10; inspiratory-to-expiratory time (I:E) ratio 1.75 (1.5,1.75). Pediatric Asthma Severity score and RR decreased (p<0.001) while tidal volume increased (p<0.001). Patient disposition: 325 paediatric intensive care units (PICU), 832 wards, with 52 of these PED ward patients were discharged home with only 2 hours of PED BiPAP with no returning to the PED within 72 hours.

CONCLUSIONS

BiPAP is a safe and effective therapeutic option for paediatric patients with asthma presenting to a PED or emergency department. This BiPAP CQIP showed significant patient compliance, no complications, improved therapeutics times, very low intubations and decreased PICU admissions. CQIP analysis demonstrated that using a higher IPAP, low EPAP with longer I:E optimises the patient's BiPAP settings and showed a significant improvement in PAS, RR and tidal volume. BiPAP should be considered as an early treatment in the PED severe or non-responsive moderate asthmatics.

Patient-Ventilator Interactions

Ventilatory muscle fatigue is a reversible loss of the ability to generate force or velocity of contraction in response to increased elastic and resistive loads. Mechanical ventilation should provide support without imposing additional loads from the ventilator (dys-synchrony). Interactive breaths optimize this relationship but require that patient effort and the ventilator response be synchronous during breath initiation, flow delivery, and termination. Proper delivery considers all 3 phases and uses clinical data, ventilator graphics, and sometimes a trial-and-error approach to optimize patient-ventilator interactions. Newer modes optimize interactions but await good clinical outcome data before routine use.

Noninvasive Ventilation

Noninvasive ventilation (NIV) has assumed a prominent role in the treatment of patients with both hypoxemic and hypercapnic acute respiratory failure (ARF). The main theoretic advantages of NIV include avoiding side effects and complications associated with endotracheal intubation, improving patient comfort, and preserving airway defense mechanisms. Factors that affect the success of NIV in patients with ARF are clinicians' expertise, selection of patient, choice of interface, selection of ventilator setting, proper monitoring, and patient motivation. Advances in the understanding of the physiologic aspects of using NIV through different interfaces and ventilator modalities have improved patient-machine interaction, thus enhancing favorable NIV outcome.

Assisted mechanical ventilation: the future is now!

Assisted ventilation is a highly complex process that requires an intimate interaction between the ventilator and the patient. The complexity of this form of ventilation is frequently underappreciated by the bedside clinician. In assisted mechanical ventilation, regardless of the specific mode, the ventilator's gas delivery pattern and the patient's breathing pattern must match near perfectly or asynchrony between the patient and the ventilator occurs. Asynchrony can be categorized into four general types: flow asynchrony; trigger asynchrony; cycle asynchrony; and mode asynchrony. In an article recently published in BMC Anesthesiology, Hodane et al. have demonstrated reduced asynchrony during assisted ventilation with Neurally Adjusted Ventilatory Assist (NAVA) as compared to pressure support ventilation (PSV). These findings add to the growing volume of data indicating that modes of ventilation that provide proportional assistance to ventilation - e.g., NAVA and Proportional Assist Ventilation (PAV) - markedly reduce asynchrony. As it becomes more accepted that the respiratory center of the patient in most circumstances is the most appropriate determinant of ventilatory pattern and as the negative outcome effects of patient-ventilator asynchrony become ever more recognized, we can expect NAVA and PAV to become the preferred modes of assisted ventilation!

Neural versus pneumatic control of pressure support in patients with chronic obstructive pulmonary diseases at different levels of positive end expiratory pressure: a physiological study

INTRODUCTION

Intrinsic positive end-expiratory pressure (PEEPi) is a "threshold" load that must be overcome to trigger conventional pneumatically-controlled pressure support (PSP) in chronic obstructive pulmonary disease (COPD). Application of extrinsic PEEP (PEEPe) reduces trigger delays and mechanical inspiratory efforts. Using the diaphragm electrical activity (EAdi), neurally controlled pressure support (PSN) could hypothetically eliminate asynchrony and reduce mechanical inspiratory effort, hence substituting the need for PEEPe. The primary objective of this study was to show that PSN can reduce the need for PEEPe to improve patient-ventilator interaction and to reduce both the "pre-trigger" and "total inspiratory" neural and mechanical efforts in COPD patients with PEEPi. A secondary objective was to evaluate the impact of applying PSN on breathing pattern.

METHODS

Twelve intubated and mechanically ventilated COPD patients with PEEPi ≥ 5 cm H2O underwent comparisons of PSP and PSN at different levels of PEEPe (at 0 %, 40 %, 80 %, and 120 % of static PEEPi, for 12 minutes at each level on average), at matching peak airway pressure. We measured flow, airway pressure, esophageal pressure, and EAdi, and analyzed neural and mechanical efforts for triggering and total inspiration. Patient-ventilator interaction was analyzed with the NeuroSync index.

RESULTS

Mean airway pressure and PEEPe were comparable for PSP and PSN at same target levels. During PSP, the NeuroSync index was 29 % at zero PEEPe and improved to 21 % at optimal PEEPe (P < 0.05). During PSN, the NeuroSync index was lower (<7 %, P < 0.05) regardless of PEEPe. Both pre-trigger (P < 0.05) and total inspiratory mechanical efforts (P < 0.05) were consistently higher during PSP compared to PSN at same PEEPe. The change in total mechanical efforts between PSP at PEEPe0% and PSN at PEEPe0% was not different from the change between PSP at PEEPe0% and PSP at PEEPe80%.

CONCLUSION

PSN abolishes the need for PEEPe in COPD patients, improves patient-ventilator interaction, and reduces the inspiratory mechanical effort to breathe.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02114567 . Registered 04 November 2013.

Increased diaphragmatic contribution to inspiratory effort during neurally adjusted ventilatory assistance versus pressure support: an electromyographic study

BACKGROUND

Neurally adjusted ventilatory assist (NAVA), regulated exclusively by the electromyographic activity (EA) of the diaphragm (EAdi), could affect the distribution of neural drive to the various inspiratory muscles. The objective of this study was to compare EAdi, EA of the scalene (EAscal), and EA of the alae nasi (EAan), according to the ventilatory mode and assist level in 12 mechanically ventilated patients.

METHODS

Seven assist levels of pressure support ventilation (PSV) and NAVA were sequentially applied. EAdi, EAscal, and EAan were quantified and expressed as a percentage of their maximum values. The relative contributions of extradiaphragmatic muscles to inspiratory efforts were assessed by calculating EAscal/EAdi and EAan/EAdi ratios. Three assist levels for each of the two ventilatory modes that resulted in EAdi values of 80 to 100%, 60 to 80%, and 40 to 60% were assigned to three groups (N1, N2, and N3). Results are expressed as median and interquartile range.

RESULTS

EA of inspiratory muscles decreased during PSV and NAVA (P < 0.0001). Although EAdi remained constant within groups (P = 0.9), EAscal was reduced during NAVA compared with PSV in N1 and N3 (65% [62 to 64] and 27% [18 to 34] in NAVA vs. 90% [81 to 100] and 49% [40 to 55] in PSV, P = 0.007). Altogether, EAscal/EAdi and EAan/EAdi ratios were lower in NAVA than PSV (0.7 [0.6 to 0.7] and 0.7 [0.6 to 0.8] in NAVA vs. 0.9 [0.8 to 1.1] and 0.9 [0.7 to 1.1] in PSV, P < 0.05).

CONCLUSIONS

NAVA and PSV both reduced extradiaphragmatic inspiratory muscle activity, in proportion to the level of assistance. Compared with PSV, NAVA resulted in a predominant contribution of the diaphragm to inspiratory effort.

Non-invasive ventilation for very old patients with limitations to respiratory care in half-open geriatric ward: experience on a consecutive cohort of patients

INTRODUCTION

A leading role for non-invasive ventilation (NIV), as comfort treatment or palliative care, is actually recognized for very old patients suffering from ARF. NIV was frequently used in both ICU and respiratory ICU (RICUs) for very old patients and it is associated with a reduced rate of endotracheal intubations and mortality. This study aims to evaluate the effects of NIV, performed in a setting of half-open geriatric ward with family support, in a cohort of very old patients with ARF and DNI decision.

METHODS

A consecutive cohort of 20 very old patients with DNI decision was admitted in our 26-bed geriatric ward during a 6 months' period. DNI decision was obtained in emergency room with an intensive care physician supported by a psychologist. Pressure support ventilation was the first choice of NIV. NIV has been performed by three adequately trained geriatricians, with one of them experienced in ICU, and in close collaboration with intensive care physicians. Arterial blood gases, to assess the response to ventilation, were obtained after 1, 6 and 12 h. NIV settings were modified according to arterial blood gas analyses or respiratory fatigue, if needed.

RESULTS

Therefore, 75% of patients were discharged home and 12 out of 20 patients had home respiratory support. PaO2/FiO2 ratio and pH increased while PaCO2 decreased during the 12 h of NIV with statistical significance. At the admission, alive patients had PaCO2 significantly lower than dead patients. After 12 h, alive patients had a better pH than dead patients. Dead patients experienced more complication than survivors.

CONCLUSION

Very old DNI patients with ARF could be treated with NIV in half-open geriatric ward with trained physicians and nurses. The presence of family members may improve patients' comfort and reduce anxiety level even at the end of life. Further studies are needed to address the effective role of NIV in very old patients with DNI decisions.

Patient-ventilator interactions. Implications for clinical management

Assisted/supported modes of mechanical ventilation offer significant advantages over controlled modes in terms of ventilator muscle function/recovery and patient comfort (and sedation needs). However, assisted/supported breaths must interact with patient demands during all three phases of breath delivery: trigger, target, and cycle. Synchronous interactions match ventilator support with patient demands; dyssynchronous interactions do not. Dyssynchrony imposes high pressure loads on ventilator muscles, promoting muscle overload/fatigue and increasing sedation needs. On current modes of ventilation there are a number of features that can monitor and enhance synchrony. These include adjustments of the trigger variable, the use of pressure versus fixed flow targeted breaths, and a number of manipulations of the cycle variable. Clinicians need to know how to use these modalities and monitor them properly, especially understanding airway pressure and flow graphics. Future strategies are emerging that have theoretical appeal but they await good clinical outcome studies before they become commonplace.

[Ventilator modes and settings during non-invasive ventilation: effects on respiratory events and implications for their identification. 2011]

Compared with invasive ventilation, non-invasive ventilation (NIV) has two unique characteristics: its non-hermetic nature and the fact that the ventilator-lung assembly cannot be considered as a single-compartment model because of the presence of variable resistance represented by the upper airways. When NIV is initiated, the ventilator settings are determined empirically based on clinical evaluation and blood gas variations. However, NIV is predominantly applied during sleep. Consequently, to assess overnight patient-machine "agreement" and efficacy of ventilation, more specific and sophisticated monitoring is needed. The effectiveness of NIV might therefore be more correctly assessed by sleep studies than by daytime assessment. The simplest monitoring can be done from flow and pressure curves from the mask or the ventilator circuit. Examination of these tracings can give useful information to evaluate if the settings chosen by the operator were the right ones for that patient. However, as NIV allows a large range of ventilatory parameters and settings, it is mandatory to have information about this to better understand patient-ventilator interaction. Ventilatory modality, mode of triggering, pressurization slope, use or not of positive end expiratory pressure and type of exhalation as well as ventilator performances may all have physiological consequences. Leaks and upper airway resistance variations may, in turn, modify these patterns. This article discusses the equipment available for NIV, analyses the effect of different ventilator modes and settings and of exhalation and connecting circuits on ventilatory traces and gives the background necessary to understand their impact on nocturnal monitoring of NIV.

Automated detection of patient-ventilator asynchrony: new tool or new toy?

Although severe patient-ventilator asynchrony is frequent during invasive and non-invasive mechanical ventilation, diagnosing such asynchronies usually requires the presence at the bedside of an experienced clinician to assess the tracings displayed on the ventilator screen, thus explaining why evaluating patient-ventilator interaction remains a challenge in daily clinical practice. In the previous issue of Critical Care, Sinderby and colleagues present a new automated method to detect, quantify, and display patient-ventilator interaction. In this validation study, the automatic method is as efficient as experts in mechanical ventilation. This promising system could help clinicians extend their knowledge about patient-ventilator interaction and further improve assisted mechanical ventilation.

Ventilatory failure, ventilator support, and ventilator weaning

The development of acute ventilatory failure represents an inability of the respiratory control system to maintain a level of respiratory motor output to cope with the metabolic demands of the body. The level of respiratory motor output is also the main determinant of the degree of respiratory distress experienced by such patients. As ventilatory failure progresses and patient distress increases, mechanical ventilation is instituted to help the respiratory muscles cope with the heightened workload. While a patient is connected to a ventilator, a physician's ability to align the rhythm of the machine with the rhythm of the patient's respiratory centers becomes the primary determinant of the level of rest accorded to the respiratory muscles. Problems of alignment are manifested as failure to trigger, double triggering, an inflationary gas-flow that fails to match inspiratory demands, and an inflation phase that persists after a patient's respiratory centers have switched to expiration. With recovery from disorders that precipitated the initial bout of acute ventilatory failure, attempts are made to discontinue the ventilator (weaning). About 20% of weaning attempts fail, ultimately, because the respiratory controller is unable to sustain ventilation and this failure is signaled by development of rapid shallow breathing. Substantial advances in the medical management of acute ventilatory failure that requires ventilator assistance are most likely to result from research yielding novel insights into the operation of the respiratory control system.

[Ventilation strategies for chronic obstructive pulmonary disease]

Chronic obstructive pulmonary disease (COPD) is considered to be one of the most frequent pulmonary diseases in industrialized countries. Non-invasive ventilation (NIV) is the first choice therapy in acute exacerbations of chronic hypercapnic respiratory failure (AE-COPD). Effective delivery of NIV requires a specialized interdisciplinary team with sufficient monitoring. NIV is delivered as assisted positive pressure ventilation where high inspiratory flow and peak pressure are required. The external positive end expiratory pressure (PEEP) should be adjusted to the intrinsic PEEP. Criteria of success are improvement in the clinical, especially neurological condition as well as improvement of pH and PaCO(2). Patients with a pH between 7.25 and 7.35 have demonstrated most benefit from NIV. In cases of patients not responding to NIV endotracheal intubation should be initiated in a timely manner. Assisted ventilation modes are preferred over controlled ventilation modes in intubated COPD patients. Settings of respirators have to be aimed at a reduction of intrinsic PEEP and dynamic hyperinflation. This includes sufficient external PEEP, long expiration times and low respiratory frequencies even allowing for permissive hypercapnia.

Optimizing patient-ventilator synchrony during invasive ventilator assist in children and infants remains a difficult task*

OBJECTIVES

To document and compare the prevalence of asynchrony events during invasive-assisted mechanical ventilation in pressure support mode and in neurally adjusted ventilatory assist in children.

DESIGN

Prospective, randomized, and crossover study.

SETTING

Pediatric and Neonatal Intensive Care Unit, University Hospital of Geneva, Switzerland.

PATIENTS

Intubated and mechanically ventilated children, between 4 weeks and 5 years old.

INTERVENTIONS

Two consecutive ventilation periods (pressure support and neurally adjusted ventilatory assist) were applied in random order. During pressure support, three levels of expiratory trigger setting were compared: expiratory trigger setting as set by the clinician in charge (PSinit), followed by a 10% (in absolute values) increase and decrease of the clinician's expiratory trigger setting. The pressure support session with the least number of asynchrony events was defined as PSbest. Therefore, three periods were compared: PSinit, PSbest, and neurally adjusted ventilatory assist. Asynchrony events, trigger delay, and inspiratory time in excess were quantified for each of them.

MEASUREMENTS AND MAIN RESULTS

Data from 19 children were analyzed. Main asynchrony events during PSinit were autotriggering (3.6 events/min [0.7-8.2]), ineffective efforts (1.2/min [0.6-5]), and premature cycling (3.5/min [1.3-4.9]). Their number was significantly reduced with PSbest: autotriggering 1.6/min (0.2-4.9), ineffective efforts 0.7/min (0-2.6), and premature cycling 2/min (0.1-3.1), p < 0.005 for each comparison. The median asynchrony index (total number of asynchronies/triggered and not triggered breaths ×100) was significantly different between PSinit and PSbest: 37.3% [19-47%] and 29% [24-43%], respectively, p < 0.005). With neurally adjusted ventilatory assist, all types of asynchrony events except double-triggering and inspiratory time in excess were significantly reduced resulting in an asynchrony index of 3.8% (2.4-15%) (p < 0.005 compared to PSbest).

CONCLUSIONS

Asynchrony events are frequent during pressure support in children despite adjusting the cycling off criteria. Neurally adjusted ventilatory assist allowed for an almost ten-fold reduction in asynchrony events. Further studies should determine the clinical impact of these findings.

Clinical review: Helmet and non-invasive mechanical ventilation in critically ill patients

Non-invasive mechanical ventilation (NIV) has proved to be an excellent technique in selected critically ill patients with different forms of acute respiratory failure. However, NIV can fail on account of the severity of the disease and technical problems, particularly at the interface. The helmet could be an alternative interface compared to face mask to improve NIV success. We performed a clinical review to investigate the main physiological and clinical studies assessing the efficacy and related issues of NIV delivered with a helmet. A computerized search strategy of MEDLINE/PubMed (January 2000 to May 2012) and EMBASE (January 2000 to May 2012) was conducted limiting the search to retrospective, prospective, nonrandomized and randomized trials. We analyzed 152 studies from which 33 were selected, 12 physiological and 21 clinical (879 patients). The physiological studies showed that NIV with helmet could predispose to CO₂ rebreathing and increase the patients' ventilator asynchrony. The main indications for NIV were acute cardiogenic pulmonary edema, hypoxemic acute respiratory failure (community-acquired pneumonia, postoperative and immunocompromised patients) and hypercapnic acute respiratory failure. In 9 of the 21 studies the helmet was compared to a face mask during either continous positive airway pressure or pressure support ventilation. In eight studies oxygenation was similar in the two groups, while the intubation rate was similar in four and lower in three studies for the helmet group compared to face mask group. The outcome was similar in six studies. The tolerance was better with the helmet in six of the studies. Although these data are limited, NIV delivered by helmet could be a safe alternative to the face mask in patients with acute respiratory failure.

Neurally adjusted ventilatory assist vs pressure support ventilation for noninvasive ventilation during acute respiratory failure: a crossover physiologic study

BACKGROUND

Patient-ventilator asynchrony is common during noninvasive ventilation (NIV) with pressure support ventilation (PSV). We examined the effect of neurally adjusted ventilatory assist (NAVA) delivered through a facemask on synchronization in patients with acute respiratory failure (ARF).

METHODS

This was a prospective, physiologic, crossover study of 13 patients with ARF (median Pa(O(2))/F(IO(2)), 196 [interquartile range (IQR), 142-225]) given two 30-min trials of NIV with PSV and NAVA in random order. Diaphragm electrical activity (EAdi), neural inspiratory time (T(In)), trigger delay (Td), asynchrony index (AI), arterial blood gas levels, and patient discomfort were recorded.

RESULTS

There were significantly fewer asynchrony events during NAVA than during PSV (10 [IQR, 5-14] events vs 17 [IQR, 8-24] events, P = .017), and the occurrence of severe asynchrony (AI > 10%) was also less under NAVA (P = .027). Ineffective efforts and delayed cycling were significantly less with NAVA (P < .05 for both). NAVA was also associated with reduced Td (0 [IQR, 0-30] milliseconds vs 90 [IQR, 30-130] milliseconds, P < .001) and inspiratory time in excess (10 [IQR, 0-28] milliseconds vs 125 [IQR, 20-312] milliseconds, P < .001), but T(In) was similar under PSV and NAVA. The EAdi signal to its maximal value was higher during NAVA than during PSV ( P = .017). There were no significant differences in arterial blood gases or patient discomfort under PSV and NAVA.

CONCLUSION

In view of specific experimental conditions, our comparison of PSV and NAVA indicated that NAVA significantly reduced severe patient-ventilator asynchrony and resulted in similar improvements in gas exchange during NIV for ARF.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT01426178; URL: www.clinicaltrials.gov.

Clinical review: Respiratory monitoring in the ICU - a consensus of 16

Monitoring plays an important role in the current management of patients with acute respiratory failure but sometimes lacks definition regarding which 'signals' and 'derived variables' should be prioritized as well as specifics related to timing (continuous versus intermittent) and modality (static versus dynamic). Many new techniques of respiratory monitoring have been made available for clinical use recently, but their place is not always well defined. Appropriate use of available monitoring techniques and correct interpretation of the data provided can help improve our understanding of the disease processes involved and the effects of clinical interventions. In this consensus paper, we provide an overview of the important parameters that can and should be monitored in the critically ill patient with respiratory failure and discuss how the data provided can impact on clinical management.

Polysomnography in stable COPD under non-invasive ventilation to reduce patient-ventilator asynchrony and morning breathlessness

BACKGROUND

Stable severe chronic obstructive pulmonary disease (COPD) patients with chronic hypercapnic respiratory failure treated by nocturnal bi-level positive pressure non-invasive ventilation (NIV) may experience severe morning deventilation dyspnea. We hypothesised that in these patients, progressive hyperinflation, resulting from inappropriate ventilator settings, leads to patient-ventilator asynchrony (PVA) with a high rate of unrewarded inspiratory efforts and morning discomfort.

METHODS

Polysomnography (PSG), diaphragm electromyogram and transcutaneous capnography (PtcCO(2)) under NIV during two consecutive nights using baseline ventilator settings on the first night, then, during the second night, adjustment of ventilator parameters under PSG with assessment of impact of settings changes on sleep, patient-ventilator synchronisation, morning arterial blood gases and morning dyspnea.

RESULTS

Eight patients (61 ± 8 years, FEV(1) 30 ± 8% predicted, residual volume 210 ± 30% predicted) were included. In all patients, pressure support was decreased during setting adjustments, as well as tidal volume, while respiratory rate increased without any deleterious effect on nocturnal PtcCO(2) or morning PaCO(2). PVA index, initially high (40 ± 30%) during the baseline night, decreased significantly after adjusting ventilator settings (p = 0.0009), as well as subjective perception of PVA leaks, and morning dyspnea while quality of sleep improved.

CONCLUSION

The subgroup of COPD patients treated by home NIV, who present marked deventilation dyspnea and unrewarded efforts may benefit from adjustment of ventilator settings under PSG or polygraphy.

Patient-ventilator asynchrony during noninvasive ventilation: a bench and clinical study

BACKGROUND

Different kinds of ventilators are available to perform noninvasive ventilation (NIV) in ICUs. Which type allows the best patient-ventilator synchrony is unknown. The objective was to compare patient-ventilator synchrony during NIV between ICU, transport—both with and without the NIV algorithm engaged—and dedicated NIV ventilators.

METHODS

First, a bench model simulating spontaneous breathing efforts was used to assess the respective impact of inspiratory and expiratory leaks on cycling and triggering functions in 19 ventilators. Second, a clinical study evaluated the incidence of patient-ventilator asynchronies in 15 patients during three randomized, consecutive, 20-min periods of NIV using an ICU ventilator with and without its NIV algorithm engaged and a dedicated NIV ventilator. Patient-ventilator asynchrony was assessed using flow, airway pressure, and respiratory muscles surface electromyogram recordings.

RESULTS

On the bench, frequent auto-triggering and delayed cycling occurred in the presence of leaks using ICU and transport ventilators. NIV algorithms unevenly minimized these asynchronies, whereas no asynchrony was observed with the dedicated NIV ventilators in all except one. These results were reproduced during the clinical study: The asynchrony index was significantly lower with a dedicated NIV ventilator than with ICU ventilators without or with their NIV algorithm engaged (0.5% [0.4%-1.2%] vs 3.7% [1.4%-10.3%] and 2.0% [1.5%-6.6%], P < .01), especially because of less auto-triggering.

CONCLUSIONS

Dedicated NIV ventilators allow better patient-ventilator synchrony than ICU and transport ventilators, even with their NIV algorithm. However, the NIV algorithm improves, at least slightly and with a wide variation among ventilators, triggering and/or cycling off synchronization.

Neurally adjusted ventilatory assist (NAVA) improves patient-ventilator interaction during non-invasive ventilation delivered by face mask

PURPOSE

To determine if, compared to pressure support (PS), neurally adjusted ventilatory assist (NAVA) reduces patient-ventilator asynchrony in intensive care patients undergoing noninvasive ventilation with an oronasal face mask.

METHODS

In this prospective interventional study we compared patient-ventilator synchrony between PS (with ventilator settings determined by the clinician) and NAVA (with the level set so as to obtain the same maximal airway pressure as in PS). Two 20-min recordings of airway pressure, flow and electrical activity of the diaphragm during PS and NAVA were acquired in a randomized order. Trigger delay (T(d)), the patient's neural inspiratory time (T(in)), ventilator pressurization duration (T(iv)), inspiratory time in excess (T(iex)), number of asynchrony events per minute and asynchrony index (AI) were determined.

RESULTS

The study included 13 patients, six with COPD, and two with mixed pulmonary disease. T(d) was reduced with NAVA: median 35 ms (IQR 31-53 ms) versus 181 ms (122-208 ms); p = 0.0002. NAVA reduced both premature and delayed cyclings in the majority of patients, but not the median T(iex) value. The total number of asynchrony events tended to be reduced with NAVA: 1.0 events/min (0.5-3.1 events/min) versus 4.4 events/min (0.9-12.1 events/min); p = 0.08. AI was lower with NAVA: 4.9 % (2.5-10.5 %) versus 15.8 % (5.5-49.6 %); p = 0.03. During NAVA, there were no ineffective efforts, or late or premature cyclings. PaO(2) and PaCO(2) were not different between ventilatory modes.

CONCLUSION

Compared to PS, NAVA improved patient ventilator synchrony during noninvasive ventilation by reducing T(d) and AI. Moreover, with NAVA, ineffective efforts, and late and premature cyclings were absent.

Clinical review: Update on neurally adjusted ventilatory assist--report of a round-table conference

Conventional mechanical ventilators rely on pneumatic pressure and flow sensors and controllers to detect breaths. New modes of mechanical ventilation have been developed to better match the assistance delivered by the ventilator to the patient's needs. Among these modes, neurally adjusted ventilatory assist (NAVA) delivers a pressure that is directly proportional to the integral of the electrical activity of the diaphragm recorded continuously through an esophageal probe. In clinical settings, NAVA has been chiefly compared with pressure-support ventilation, one of the most popular modes used during the weaning phase, which delivers a constant pressure from breath to breath. Comparisons with proportional-assist ventilation, which has numerous similarities, are lacking. Because of the constant level of assistance, pressure-support ventilation reduces the natural variability of the breathing pattern and can be associated with asynchrony and/or overinflation. The ability of NAVA to circumvent these limitations has been addressed in clinical studies and is discussed in this report. Although the underlying concept is fascinating, several important questions regarding the clinical applications of NAVA remain unanswered. Among these questions, determining the optimal NAVA settings according to the patient's ventilatory needs and/or acceptable level of work of breathing is a key issue. In this report, based on an investigator-initiated round table, we review the most recent literature on this topic and discuss the theoretical advantages and disadvantages of NAVA compared with other modes, as well as the risks and limitations of NAVA.

Effort-adapted modes of assisted breathing

PURPOSE OF REVIEW

New developments in mechanical ventilation have focused on increasing the patient's control of the ventilator by implementing information on lung mechanics and respiratory drive. Effort-adapted modes of assisted breathing are presented and their potential advantages are discussed.

RECENT FINDINGS

Adaptive support ventilation, proportional assist ventilation with load adjustable gain factors and neurally adjusted ventilatory assist are ventilatory modes that follow the concept of adapting the assist to a defined target, instantaneous changes in respiratory drive or lung mechanics. Improved patient ventilator interaction, sufficient unloading of the respiratory muscles and increased comfort have been recently associated with these ventilator modalities. There are, however, scarce data with regard to outcome improvement, such as length of mechanical ventilation, ICU stay or mortality (commonly accepted targets to demonstrate clinical superiority).

SUMMARY

Within recent years, a major step forward in the evolution of assisted (effort-adapted) modes of mechanical ventilation was accomplished. There is growing evidence that supports the physiological concept of closed-loop effort-adapted assisted modes of mechanical ventilation. However, at present, the translation into a clear outcome benefit remains to be proven. In order to fill the knowledge gap that impedes the broader application, larger randomized controlled trials are urgently needed. However, with clearly proven drawbacks of conventional assisted modes such as pressure support ventilation, it is probably about time to leave these modes introduced decades ago behind.

Optimization of ventilator setting by flow and pressure waveforms analysis during noninvasive ventilation for acute exacerbations of COPD: a multicentric randomized controlled trial

Introduction

The analysis of flow and pressure waveforms generated by ventilators can be useful in the optimization of patient-ventilator interactions, notably in chronic obstructive pulmonary disease (COPD) patients. To date, however, a real clinical benefit of this approach has not been proven.

Methods

The aim of the present randomized, multi-centric, controlled study was to compare optimized ventilation, driven by the analysis of flow and pressure waveforms, to standard ventilation (same physician, same initial ventilator setting, same time spent at the bedside while the ventilator screen was obscured with numerical data always available). The primary aim was the rate of pH normalization at two hours, while secondary aims were changes in PaCO₂, respiratory rate and the patient's tolerance to ventilation (all parameters evaluated at baseline, 30, 120, 360 minutes and 24 hours after the beginning of ventilation). Seventy patients (35 for each group) with acute exacerbation of COPD were enrolled.

Results

Optimized ventilation led to a more rapid normalization of pH at two hours (51 vs. 26% of patients), to a significant improvement of the patient's tolerance to ventilation at two hours, and to a higher decrease of PaCO₂ at two and six hours. Optimized ventilation induced physicians to use higher levels of external positive end-expiratory pressure, more sensitive inspiratory triggers and a faster speed of pressurization.

Conclusions

The analysis of the waveforms generated by ventilators has a significant positive effect on physiological and patient-centered outcomes during acute exacerbation of COPD. The acquisition of specific skills in this field should be encouraged.

Trial registration

ClinicalTrials.gov NCT01291303.