Patient-ventilator asynchrony

Management of Neuromuscular Blocking Agents in Critically Ill Patients with Lung Diseases

The use of neuromuscular blocking agents (NMBAs) is common in the intensive care unit (ICU). NMBAs have been used in critically ill patients with lung diseases to optimize mechanical ventilation, prevent spontaneous respiratory efforts, reduce the work of breathing and oxygen consumption, and avoid patient-ventilator asynchrony. In patients with acute respiratory distress syndrome (ARDS), NMBAs reduce the risk of barotrauma and improve oxygenation. Nevertheless, current guidelines and evidence are contrasting regarding the routine use of NMBAs. In status asthmaticus and acute exacerbation of chronic obstructive pulmonary disease, NMBAs are used in specific conditions to ameliorate patient-ventilator synchronism and oxygenation, although their routine use is controversial. Indeed, the use of NMBAs has decreased over the last decade due to potential adverse effects, such as immobilization, venous thrombosis, patient awareness during paralysis, development of critical illness myopathy, autonomic interactions, ICU-acquired weakness, and residual paralysis after cessation of NMBAs use. The aim of this review is to highlight current knowledge and synthesize the evidence for the effects of NMBAs for critically ill patients with lung diseases, focusing on patient-ventilator asynchrony, ARDS, status asthmaticus, and chronic obstructive pulmonary disease.

Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination

Objective. Understanding a patient's respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure.Approach.A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics.Main results.After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings.Significance.Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient's situation for individualization of ventilation therapy.

Effects of transcutaneous electrical diaphragmatic stimulation in critically ill elderly patients: a randomized controlled trial

BACKGROUND

Elderly patients under invasive mechanical ventilation (IMV) are more susceptible to muscle weakness. In the out-of-hospital environment, there are benefits to transcutaneous electrical diaphragmatic stimulation (TEDS), which is an easy-to-apply and low-cost technique.

OBJECTIVE

To evaluate the effect of TEDS on respiratory muscle strength, diaphragm thickness (DT), and IMV time in critically ill elderly patients.

METHODS

This was a randomized controlled trial in which patients were divided into an experimental group (EG) and a control group (CG). TEDS started 24 h after orotracheal intubation and lasted until the end of weaning. Both groups underwent the following assessments during the spontaneous breathing test after weaning from mechanical ventilation (MV): measurement of respiratory muscle strength by pressure gauge, analysis of DT by lung ultrasound, and extubation failure prevention checklist.

RESULTS

There were 23 participants in the EG and 21 in the CG. The median age was 66 (60-79) years. The mean values of the diaphragmatic thickening index in the EG and CG participants were 99.13 ± 26.75 and 66.88 ± 31.77, respectively (p = .001, Cohen's d = 1.094). The mean values of maximum inspiratory pressure in the EG and CG were 22.04 ± 3.41 and 19.34 ± 4.23 cmH2O, respectively (p = .005, Cohen's d = 0.698). The Tobin index and the integrative weaning index were similar between groups (p = .584 and p = .102, respectively). The duration of MV in the EG and CG was 6.28 ± 2.68 and 9.21 ± 2.76 days, respectively (p = .001, Cohen's d = -1.075).

CONCLUSION

Critically ill elderly patients receiving TEDS had shorter MV time, greater inspiratory muscle strength, and greater diaphragmatic contraction capacity according to their thickness fraction.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Association between ventilatory ratio and ICU mortality in interstitial lung disease patients on mechanical ventilation: A retrospective study

BACKGROUND

Ventilatory ratio (VR) is a simple bedside index of ventilatory efficiency. Interstitial lung disease (ILD) is a diverse group of diseases that causes fibrosis or inflammation of the pulmonary parenchyma, and the main clinical manifestation is hypoxemia. To date, no study has explored ventilation efficiency in patients with ILD.

OBJECTIVES

This study aimed to explore the features of VR in mechanically ventilated patients with ILD and their relationship with intensive care unit (ICU) mortality.

METHODS

In this retrospective analysis, we included mechanically ventilated patients with ILD in the ICU of West China Hospital, Sichuan University, from 2013 to 2021. Demographic data and mechanical ventilation (MV) parameters within 24 h of intubation were collected. The characteristics of VR and their relationships with ICU mortality were also analyzed.

RESULTS

224 patients were included in the final analysis. There were 146 males (53.9%), and the median age was 65 years (interquartile range [IQR]54∼74). The mean value of VR was 2.22, and VR was significantly higher in nonsurvivors than in survivors (1.79 vs 2.32, P < 0.001). A high VR value was an independent risk factor for ICU mortality (odds ratio=1.602, P = 0.038) after adjustment. A high value of VR was associated with a shorter survival time after admission to ICU (hazard ratio=1.485, P = 0.006) CONCLUSIONS: VR in patients with ILD on MV was increased, and the VR of nonsurvivors within 24 h of intubation was higher than that of survivors. The high VR value within 24 h of intubation was an independent risk factor for ICU mortality after adjusting for other factors.

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

BACKGROUND AND OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Establishment and Application of a Patient-Ventilator Asynchrony Remote Network Platform for ICU Mechanical Ventilation: A Retrospective Study

BACKGROUND

In the process of mechanical ventilation, the problem of patient-ventilator asynchrony (PVA) is faced. This study proposes a self-developed remote mechanical ventilation visualization network system to solve the PVA problem.

METHOD

The algorithm model proposed in this study builds a remote network platform and achieves good results in the identification of ineffective triggering and double triggering abnormalities in mechanical ventilation.

RESULT

The algorithm has a sensitivity recognition rate of 79.89% and a specificity of 94.37%. The sensitivity recognition rate of the trigger anomaly algorithm was as high as 67.17%, and the specificity was 99.92%.

CONCLUSIONS

The asynchrony index was defined to monitor the patient's PVA. The system analyzes real-time transmission of respiratory data, identifies double triggering, ineffective triggering, and other anomalies through the constructed algorithm model, and outputs abnormal alarms, data analysis reports, and data visualizations to assist or guide physicians in handling abnormalities, which is expected to improve patients' breathing conditions and prognosis.

Respiratory Monitoring During Mechanical Ventilation: The Present and the Future

The increased application of mechanical ventilation, the recognition of its harms and the interest in individualization raised the need for an effective monitoring. An increasing number of monitoring tools and modalities were introduced over the past 2 decades with growing insight into asynchrony, lung and chest wall mechanics, respiratory effort and drive. They should be used in a complementary rather than a standalone way. A sound strategy can guide a reduction in adverse effects like ventilator-induced lung injury, ventilator-induced diaphragm dysfunction, patient-ventilator asynchrony and helps early weaning from the ventilator. However, the diversity, complexity, lack of expertise, and associated cost make formulating the appropriate monitoring strategy a challenge for clinicians. Most often, a big amount of data is fed to the clinicians making interpretation difficult. Therefore, it is fundamental for intensivists to be aware of the principle, advantages, and limits of each tool. This analytic review includes a simplified narrative of the commonly used basic and advanced respiratory monitors along with their limits and future prospective.

Monitoring the patient-ventilator asynchrony during non-invasive ventilation

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

Sedation/agitation monitoring using a wearable device for ventilated children

BACKGROUND

Developing continuous and labor-saving sedation/agitation monitoring methods in ventilated children is important to avoid undesirable events such as unplanned extubation. The existing scales are often challenging to use. We therefore aimed to evaluate the feasibility of sedation/agitation monitoring using a wearable device with a built-in accelerometer for ventilated children.

METHODS

This prospective observational pilot study included children aged 15 years or less, admitted to the pediatric intensive care unit on mechanical ventilation after cardiac catheterization between December 2021 and April 2022. The wearable device with a built-in accelerometer was attached to either of the upper limbs, and accelerations due to upper limb movements were measured for 2 h after admission or until extubation, whichever was earliest. Accelerations were measured at 0.02 s intervals, with the mean acceleration calculated for each 1 min interval. The State Behavioral Scale (SBS) was completed at 1 min intervals, with the SBS score (-1, 0, 1, or 2) compared with the mean acceleration.

RESULTS

The study included 20 children with a median age of 12 months. The mean accelerations and SBS scores were positively correlated (Kendall's τ, 0.22; p < 0.001), with an increase in the median (interquartile range) acceleration from an SBS score of -1 through 2, as follows: SBS -1, 0.200 (0.151-0.232) m/s2 ; SBS 0, 0.202 (0.190-0.235) m/s2 ; SBS, 1, 0.312 (0.236-0.427) m/s2 ; SBS 2, 0.455 (0.332-0.517) m/s2 . No adverse events were observed.

CONCLUSIONS

This study showed that continuous, labor-saving sedation/agitation monitoring of ventilated children was feasible using a wearable device with a built-in accelerometer.

Asynchronies during invasive mechanical ventilation: narrative review and update

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview

BACKGROUND

Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare.

OBJECTIVE

This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML).

METHOD

Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research.

RESULTS

A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11-20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction.

CONCLUSIONS

The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.

Calcium Channel Blocker Intoxication: A Critical Care Transport Perspective

Calcium channel blockers (CCBs) have seen an increase in rate of non-therapeutic exposure that is both accidental and intentional in nature. Patients experiencing the toxic effects of a CCB overdose are resource intensive and can quickly outstrip the capabilities of local health systems, necessitating transfer to larger tertiary or quaternary care centers. We present a case of intentional non-dihydropyridine CCB overdose and toxicity in a 20-year-old patient requiring initial stabilization at a referring critical access emergency department with continuation of treatment and support during a 60-minute rotor wing transport from the referring hospital to an academic quaternary care center. Emphasis is placed on the unique challenges in resuscitation and ongoing critical care administration during the transport phase of care. Proper stabilization of patients, planning, and consideration of potential problems associated with transport can help minimize stresses and risk of the transport, improving the outcome of extremely ill patients even under challenging circumstances.

Outcomes of ventilatory asynchrony in patients with inspiratory effort

Objective

To identify the relationship of patient-ventilator asynchrony with the level of sedation and hemogasometric and clinical results.

Methods

This was a prospective study of 122 patients admitted to the intensive care unit who underwent > 24 hours of invasive mechanical ventilation with inspiratory effort. In the first 7 days of ventilation, patient-ventilator asynchrony was evaluated daily for 30 minutes. Severe patient-ventilator asynchrony was defined as an asynchrony index > 10%.

Results

A total of 339,652 respiratory cycles were evaluated in 504 observations. The mean asynchrony index was 37.8% (standard deviation 14.1 - 61.5%). The prevalence of severe patient-ventilator asynchrony was 46.6%. The most frequent patient-ventilator asynchronies were ineffective trigger (13.3%), autotrigger (15.3%), insufficient flow (13.5%), and delayed cycling (13.7%). Severe patient-ventilator asynchrony was related to the level of sedation (ineffective trigger: p = 0.020; insufficient flow: p = 0.016; premature cycling: p = 0.023) and the use of midazolam (p = 0.020). Severe patient-ventilator asynchrony was also associated with hemogasometric changes. The persistence of severe patient-ventilator asynchrony was an independent risk factor for failure of the spontaneous breathing test, ventilation time, ventilator-associated pneumonia, organ dysfunction, mortality in the intensive care unit, and length of stay in the intensive care unit.

Conclusion

Patient-ventilator asynchrony is a frequent disorder in critically ill patients with inspiratory effort. The patient’s interaction with the ventilator should be optimized to improve hemogasometric parameters and clinical results. Further studies are required to confirm these results.

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

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.