Monitoring patient-ventilator asynchrony

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

Background

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

Methods

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

Results

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

Conclusions

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

Reverse triggering ? a novel or previously missed phenomenon?

BACKGROUND

Reverse triggering (RT) was described in 2013 as a form of patient-ventilator asynchrony, where patient's respiratory effort follows mechanical insufflation. Diagnosis requires esophageal pressure (Pes) or diaphragmatic electrical activity (EAdi), but RT can also be diagnosed using standard ventilator waveforms.

HYPOTHESIS

We wondered (1) how frequently RT would be present but undetected in the figures from literature, especially before 2013; (2) whether it would be more prevalent in the era of small tidal volumes after 2000.

METHODS

We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, from 1950 to 2017, with key words related to asynchrony to identify papers with figures including ventilator waveforms expected to display RT if present. Experts labelled waveforms. 'Definite' RT was identified when Pes or EAdi were in the tracing, and 'possible' RT when only flow and pressure waveforms were present. Expert assessment was compared to the author's descriptions of waveforms.

RESULTS

We found 65 appropriate papers published from 1977 to now, containing 181 ventilator waveforms. 21 cases of 'possible' RT and 25 cases of 'definite' RT were identified by the experts. 18.8% of waveforms prior to 2013 had evidence of RT. Most cases were published after 2000 (1 before vs. 45 after, p = 0.03). 54% of RT cases were attributed to different phenomena. A few cases of identified RT were already described prior to 2013 using different terminology (earliest in 1997). While RT cases attributed to different phenomena decreased after 2013, 60% of 'possible' RT remained missed.

CONCLUSION

RT has been present in the literature as early as 1997, but most cases were found after the introduction of low tidal volume ventilation in 2000. Following 2013, the number of undetected cases decreased, but RT are still commonly missed. Reverse Triggering, A Missed Phenomenon in the Literature. Critical Care Canada Forum 2019 Abstracts. Can J Anesth/J Can Anesth 67 (Suppl 1), 1-162 (2020). https://doi-org.myaccess.library.utoronto.ca/ https://doi.org/10.1007/s12630-019-01552-z .

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

Managing respiratory muscle weakness during weaning from invasive ventilation

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

Impact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study)

BACKGROUND

Patient-ventilator asynchronies are usually detected by visual inspection of ventilator waveforms but with low sensitivity, even when performed by experts in the field. Recently, estimation of the inspiratory muscle pressure (P_mus) waveforms through artificial intelligence algorithm has been proposed (Magnamed®, São Paulo, Brazil). We hypothesized that the display of these waveforms could help healthcare providers identify patient-ventilator asynchronies.

METHODS

A prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated P_mus waveform would improve the correct identification of asynchronies in simulated clinical scenarios. The primary outcome was the mean asynchrony detection rate (sensitivity). Physicians and respiratory therapists who work in intensive care units were randomized to control or intervention group. In both groups, participants analyzed pressure and flow waveforms of 49 different scenarios elaborated using the ASL-5000 lung simulator. In the intervention group the estimated P_mus waveform was displayed in addition to pressure and flow waveforms.

RESULTS

A total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the P_mus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p < 0.001). This effect remained when stratifying asynchronies by type.

CONCLUSIONS

We showed that the display of the P_mus waveform improved the ability of healthcare professionals to recognize patient-ventilator asynchronies by visual inspection of ventilator tracings. These findings require clinical validation.

TRIAL REGISTRATION

ClinicalTrials.gov: NTC05144607. Retrospectively registered 3 December 2021.

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

BACKGROUND

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

METHOD

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

RESULT

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

CONCLUSIONS

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

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

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

Mixed-integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator-induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non-invasively estimating breathing effort using only flow and pressure signals. Mixed-integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our algorithm successfully estimated the respiratory effort, resistance, and elastance of the respiratory system, resulting in more robust performance and faster solver times than a previously proposed algorithm that used quadratic programming (QP) techniques. In a numerical simulation benchmark, the worst-case errors for resistance and elastance were 25% and 23% for QP versus <0.1% and <0.1% for MIQP, whose solver times were 4.7 s and 0.5 s, respectively. This approach can estimate several breathing effort profiles and identify the respiratory system's mechanical properties in invasively ventilated critically ill patients.

How to recognize patients at risk of self-inflicted lung injury

INTRODUCTION

Patient self-inflicted lung injury (P-SILI) has been proposed as a form of lung injury caused by strong inspiratory efforts consequent to a high respiratory drive in patients with hypoxemic acute respiratory failure (hARF). Increased respiratory drive and effort may lead to variable combinations of deleterious phenomena, such as excessive transpulmonary pressure, pendelluft, intra-tidal recruitment, local lung volutrauma, and pulmonary edema. Gas exchange and respiratory mechanics derangements further increase respiratory drive and effort, thus inducing a vicious circle. Forms of partial ventilatory support may further add to the detrimental effects of P-SILI. Since P-SILI may worsen patient outcome, strategies aimed at identifying and preventing P-SILI would be of great importance.

AREAS COVERED

We systematically searched Pubmed since inception until 15 April 2022 to review the patho-physiological mechanisms of P-SILI and the strategies to identify those patients at risk of P-SILI.

EXPERT OPINION

Although the concept of P-SILI has been increasingly supported by experimental and clinical data, no study has insofar demonstrated the efficacy of any strategy to identify it in the clinical setting. Further research is thus needed to ascertain the detrimental effects of spontaneous breathing and identify patients with hARF at high risk of developing P-SILI.

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

Background

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

Methods

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

Results

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

Conclusions

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

Neurally Adjusted Ventilatory Assist vs. Conventional Mechanical Ventilation in Adults and Children With Acute Respiratory Failure: A Systematic Review and Meta-Analysis

Background

Patient-ventilator asynchrony is a common problem in mechanical ventilation (MV), resulting in increased complications of MV. Despite there being some pieces of evidence for the efficacy of improving the synchronization of neurally adjusted ventilatory assist (NAVA), controversy over its physiological and clinical outcomes remain. Herein, we conducted a systematic review and meta-analysis to determine the relative impact of NAVA or conventional mechanical ventilation (CMV) modes on the important outcomes of adults and children with acute respiratory failure (ARF).

Methods

Qualified studies were searched in PubMed, EMBASE, Medline, Web of Science, Cochrane Library, and additional quality evaluations up to October 5, 2021. The primary outcome was asynchrony index (AI); secondary outcomes contained the duration of MV, intensive care unit (ICU) mortality, the incidence rate of ventilator-associated pneumonia, pH, and Partial Pressure of Carbon Dioxide in Arterial Blood (PaCO2). A statistical heterogeneity for the outcomes was assessed using the I ² test. A data analysis of outcomes using odds ratio (OR) for ICU mortality and ventilator-associated pneumonia incidence and mean difference (MD) for AI, duration of MV, pH, and PaCO2, with 95% confidence interval (CI), was expressed.

Results

Eighteen eligible studies (n = 926 patients) were eventually enrolled. For the primary outcome, NAVA may reduce the AI (MD = -18.31; 95% CI, -24.38 to -12.25; p < 0.001). For the secondary outcomes, the duration of MV in the NAVA mode was 2.64 days lower than other CMVs (MD = -2.64; 95% CI, -4.88 to -0.41; P = 0.02), and NAVA may decrease the ICU mortality (OR =0.60; 95% CI, 0.42 to 0.86; P = 0.006). There was no statistically significant difference in the incidence of ventilator-associated pneumonia, pH, and PaCO2 between NAVA and other MV modes.

Conclusions

Our study suggests that NAVA ameliorates the synchronization of patient-ventilator and improves the important clinical outcomes of patients with ARF compared with CMV modes.

Gas conditioning during helmet noninvasive ventilation: effect on comfort, gas exchange, inspiratory effort, transpulmonary pressure and patient-ventilator interaction

Background

There is growing interest towards the use of helmet noninvasive ventilation (NIV) for the management of acute hypoxemic respiratory failure. Gas conditioning through heat and moisture exchangers (HME) or heated humidifiers (HHs) is needed during facemask NIV to provide a minimum level of humidity in the inspired gas (15 mg H₂O/L). The optimal gas conditioning strategy during helmet NIV remains to be established.

Methods

Twenty patients with acute hypoxemic respiratory failure (PaO₂/FiO₂ < 300 mmHg) underwent consecutive 1-h periods of helmet NIV (PEEP 12 cmH₂O, pressure support 12 cmH₂O) with four humidification settings, applied in a random order: double-tube circuit with HHs and temperature set at 34 °C (HH34) and 37 °C (HH37); Y-piece circuit with HME; double-tube circuit with no humidification (NoH). Temperature and humidity of inhaled gas were measured through a capacitive hygrometer. Arterial blood gases, discomfort and dyspnea through visual analog scales (VAS), esophageal pressure swings (ΔP_ES) and simplified pressure–time product (PTP_ES), dynamic transpulmonary driving pressure (ΔP_L) and asynchrony index were measured in each step.

Results

Median [IqR] absolute humidity, temperature and VAS discomfort were significantly lower during NoH vs. HME, HH34 and HH37: absolute humidity (mgH₂O/L) 16 [12–19] vs. 28 [23–31] vs. 28 [24–31] vs. 33 [29–38], p < 0.001; temperature (°C) 29 [28–30] vs. 30 [29–31] vs. 31 [29–32] vs 32. [31–33], p < 0.001; VAS discomfort 4 [2–6] vs. 6 [2–7] vs. 7 [4–8] vs. 8 [4–10], p = 0.03. VAS discomfort increased with higher absolute humidity (p < 0.01) and temperature (p = 0.007). Higher VAS discomfort was associated with increased VAS dyspnea (p = 0.001). Arterial blood gases, respiratory rate, ΔP_ES, PTP_ES and ΔP_L were similar in all conditions. Overall asynchrony index was similar in all steps, but autotriggering rate was lower during NoH and HME (p = 0.03).

Conclusions

During 1-h sessions of helmet NIV in patients with hypoxemic respiratory failure, a double-tube circuit with no humidification allowed adequate conditioning of inspired gas, optimized comfort and improved patient–ventilator interaction. Use of HHs or HME in this setting resulted in increased discomfort due to excessive heat and humidity in the interface, which was associated with more intense dyspnea.

Trail Registration Registered on clinicaltrials.gov (NCT02875379) on August 23rd, 2016.

COVID-19 ARDS: Points to Be Considered in Mechanical Ventilation and Weaning

The COVID-19 disease can cause hypoxemic respiratory failure due to ARDS, requiring invasive mechanical ventilation. Although early studies reported that COVID-19-associated ARDS has distinctive features from ARDS of other causes, recent observational studies have demonstrated that ARDS related to COVID-19 shares common clinical characteristics and respiratory system mechanics with ARDS of other origins. Therefore, mechanical ventilation in these patients should be based on strategies aiming to mitigate ventilator-induced lung injury. Assisted mechanical ventilation should be applied early in the course of mechanical ventilation by considering evaluation and minimizing factors associated with patient-inflicted lung injury. Extracorporeal membrane oxygenation should be considered in selected patients with refractory hypoxia not responding to conventional ventilation strategies. This review highlights the current and evolving practice in managing mechanically ventilated patients with ARDS related to COVID-19.

Ultrasonographic assessment of diaphragmatic contraction and relaxation properties: correlations of diaphragmatic displacement with oesophageal and transdiaphragmatic pressure

Transdiaphragmatic (Pdi) and oesophageal pressures (Pes) are useful in understanding the pathophysiology of the respiratory system. They provide insight into respiratory drive, intrinsic positive end-expiratory pressure, diaphragmatic fatigue and weaning failure.

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.

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.

Patient-ventilator asynchrony in acute brain-injured patients: a prospective observational study

Background

Patient–ventilator asynchrony is common in mechanically ventilated patients and may be related to adverse outcomes. Few studies have reported the occurrence of asynchrony in brain-injured patients. We aimed to investigate the prevalence, type and severity of patient–ventilator asynchrony in mechanically ventilated patients with brain injury.

Methods

This prospective observational study enrolled acute brain-injured patients undergoing mechanical ventilation. Esophageal pressure monitoring was established after enrollment. Flow, airway pressure, and esophageal pressure–time waveforms were recorded for a 15-min interval, four times daily for 3 days, for visually detecting asynchrony by offline analysis. At the end of each dataset recording, the respiratory drive was determined by the airway occlusion maneuver. The asynchrony index was calculated to represent the severity. The relationship between the prevalence and the severity of asynchrony with ventilatory modes and settings, respiratory drive, and analgesia and sedation were determined. Association of severe patient–ventilator asynchrony, which was defined as an asynchrony index  ≥ 10%, with clinical outcomes was analyzed.

Results

In 100 enrolled patients, a total of 1076 15-min waveform datasets covering 330,292 breaths were collected, in which 70,156 (38%) asynchronous breaths were detected. Asynchrony occurred in 96% of patients with the median (interquartile range) asynchrony index of 12.4% (4.3%–26.4%). The most prevalent type was ineffective triggering. No significant difference was found in either prevalence or asynchrony index among different classifications of brain injury (p > 0.05). The prevalence of asynchrony was significantly lower during pressure control/assist ventilation than during other ventilatory modes (p < 0.05). Compared to the datasets without asynchrony, the airway occlusion pressure was significantly lower in datasets with ineffective triggering (p < 0.001). The asynchrony index was significantly higher during the combined use of opioids and sedatives (p < 0.001). Significantly longer duration of ventilation and hospital length of stay after the inclusion were found in patients with severe ineffective triggering (p < 0.05).

Conclusions

Patient–ventilator asynchrony is common in brain-injured patients. The most prevalent type is ineffective triggering and its severity is likely related to a long duration of ventilation and hospital stay. Prevalence and severity of asynchrony are associated with ventilatory modes, respiratory drive and analgesia/sedation strategy, suggesting treatment adjustment in this particular population.

Trial registration The study has been registered on 4 July 2017 in ClinicalTrials.gov (NCT03212482) (https://clinicaltrials.gov/ct2/show/NCT03212482).

Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review

Mortality associated with the acute respiratory distress syndrome remains unacceptably high due in part to ventilator-induced lung injury (VILI). Ventilator dyssynchrony is defined as the inappropriate timing and delivery of a mechanical breath in response to patient effort and may cause VILI. Such deleterious patient–ventilator interactions have recently been termed patient self-inflicted lung injury. This narrative review outlines the detection and frequency of several different types of ventilator dyssynchrony, delineates the different mechanisms by which ventilator dyssynchrony may propagate VILI, and reviews the potential clinical impact of ventilator dyssynchrony. Until recently, identifying ventilator dyssynchrony required the manual interpretation of ventilator pressure and flow waveforms. However, computerized interpretation of ventilator waive forms can detect ventilator dyssynchrony with an area under the receiver operating curve of >0.80. Using such algorithms, ventilator dyssynchrony occurs in 3%–34% of all breaths, depending on the patient population. Moreover, two types of ventilator dyssynchrony, double-triggered and flow-limited breaths, are associated with the more frequent delivery of large tidal volumes >10 mL/kg when compared with synchronous breaths (54% [95% confidence interval (CI), 47%–61%] and 11% [95% CI, 7%–15%]) compared with 0.9% (95% CI, 0.0%–1.9%), suggesting a role in propagating VILI. Finally, a recent study associated frequent dyssynchrony-defined as >10% of all breaths-with an increase in hospital mortality (67 vs. 23%, P = 0.04). However, the clinical significance of ventilator dyssynchrony remains an area of active investigation and more research is needed to guide optimal ventilator dyssynchrony management.

Proportional assist ventilation versus pressure support ventilation for weaning from mechanical ventilation in adults: a meta-analysis and trial sequential analysis

Background

Pressure support ventilation (PSV) is the prevalent weaning method. Proportional assist ventilation (PAV) is an assisted ventilation mode, which is recently being applied to wean the patients from mechanical ventilation. Whether PAV or PSV is superior for weaning remains unclear.

Methods

Eligible randomized controlled trials published before April 2020 were retrieved from databases. We calculated the risk ratio (RR) and mean difference (MD) with 95% confidence intervals (CIs).

Results

Seven articles, involving 634 patients, met the selection criteria. Compared to PSV, PAV was associated with a significantly higher rate of weaning success (fixed-effect RR 1.16; 95% CI 1.07–1.26; I² = 0.0%; trial sequential analysis-adjusted CI 1.03–1.30), and the trial sequential monitoring boundary for benefit was crossed. Compared to PSV, PAV was associated with a lower proportion of patients requiring reintubation (RR 0.49; 95% CI 0.28–0.87; I² = 0%), a shorter ICU length of stay (MD − 1.58 (days), 95% CI − 2.68 to − 0.47; I² = 0%), and a shorter mechanical ventilation duration (MD − 40.26 (hours); 95% CI − 66.67 to − 13.84; I² = 0%). There was no significant difference between PAV and PSV with regard to mortality (RR 0.66; 95% CI 0.42–1.06; I² = 0%) or weaning duration (MD − 0.01 (hours); 95% CI − 1.30–1.28; I² = 0%).

Conclusion

The results of the meta-analysis suggest that PAV is superior to PSV in terms of weaning success, and the statistical power is confirmed using trial sequential analysis.

Graphical abstract

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.

Monitoring of Respiratory Muscle Function in Critically Ill Children

OBJECTIVES

This review discusses the different techniques used at the bedside to assess respiratory muscle function in critically ill children and their clinical applications.

DATA SOURCES

A scoping review of the medical literature on respiratory muscle function assessment in critically ill children was conducted using the PubMed search engine.

STUDY SELECTION

We included all scientific, peer-reviewed studies about respiratory muscle function assessment in critically ill children, as well as some key adult studies.

DATA EXTRACTION

Data extracted included findings or comments about techniques used to assess respiratory muscle function.

DATA SYNTHESIS

Various promising physiologic techniques are available to assess respiratory muscle function at the bedside of critically ill children throughout the disease process. During the acute phase, this assessment allows a better understanding of the pathophysiological mechanisms of the disease and an optimization of the ventilatory support to increase its effectiveness and limit its potential complications. During the weaning process, these physiologic techniques may help predict extubation success and therefore optimize ventilator weaning.

CONCLUSIONS

Physiologic techniques are useful to precisely assess respiratory muscle function and to individualize and optimize the management of mechanical ventilation in children. Among all the available techniques, the measurements of esophageal pressure and electrical activity of the diaphragm appear particularly helpful in the era of individualized ventilatory management.

Effect of Neurally Adjusted Ventilatory Assist on Patient-Ventilator Interaction in Mechanically Ventilated Adults: A Systematic Review and Meta-Analysis

OBJECTIVES

Patient-ventilator asynchrony is common among critically ill patients undergoing mechanical ventilation and has been associated with adverse outcomes. Neurally adjusted ventilatory assist is a ventilatory mode that may lead to improved patient-ventilator synchrony. We conducted a systematic review to determine the impact of neurally adjusted ventilatory assist on patient-ventilator asynchrony, other physiologic variables, and clinical outcomes in adult patients undergoing invasive mechanical ventilation in comparison with conventional pneumatically triggered ventilatory modes.

DATA SOURCES

We searched Medline, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central, CINAHL, Scopus, Web of Science, conference abstracts, and ClinicalTrials.gov until July 2018.

STUDY SELECTION

Two authors independently screened titles and abstracts for randomized and nonrandomized controlled trials (including crossover design) comparing the occurrence of patient-ventilator asynchrony between neurally adjusted ventilatory assist and pressure support ventilation during mechanical ventilation in critically ill adults. The asynchrony index and severe asynchrony (i.e., asynchrony index > 10%) were the primary outcomes.

DATA EXTRACTION

Two authors independently extracted study characteristics and outcomes and assessed risk of bias of included studies.

DATA SYNTHESIS

Of 11,139 unique citations, 26 studies (522 patients) met the inclusion criteria. Sixteen trials were included in the meta-analysis using random effects models through the generic inverse variance method. In several different clinical scenarios, the use of neurally adjusted ventilatory assist was associated with significantly reduced asynchrony index (mean difference, -8.12; 95% CI, -11.61 to -4.63; very low quality of evidence) and severe asynchrony (odds ratio, 0.42; 95% CI, 0.23-0.76; moderate quality of evidence) as compared with pressure support ventilation. Furthermore, other measurements of asynchrony were consistently improved during neurally adjusted ventilatory assist.

CONCLUSIONS

Neurally adjusted ventilatory assist improves patient-ventilator synchrony; however, its effects on clinical outcomes remain uncertain. Randomized controlled trials are needed to determine whether the physiologic efficiency of neurally adjusted ventilatory assist affects patient-important outcomes in critically ill adults.

The Control of Breathing During Mechanical Ventilation

On mechanical ventilation, the lungs is subjected to the action of 2 pumps with independent control systems. The patient's control system responds to actions of the ventilator via programmed mechanisms that alter respiratory output in response to lung volume changes; blood gas tensions; and, in conscious patients, to ventilator-induced changes that are different from those expected by the patient's control system. By contrast, the ventilator responds to the patient's actions according to operational characteristics of the specific ventilator mode. That patient and ventilator responses are not coordinated results in complex breathing patterns that may adversely affect the clinical outcome.

Patient-ventilator asynchrony in conventional ventilation modes during short-term mechanical ventilation after cardiac surgery: randomized clinical trial

INTRODUCTION AND AIM

Studies regarding asynchrony in patients in the cardiac postoperative period are still only a few. The main objective of our study was to compare asynchronies incidence and its index (AI) in 3 different modes of ventilation (volume-controlled ventilation [VCV], pressure-controlled ventilation [PCV] and pressure-support ventilation [PSV]) after ICU admission for postoperative care.

METHODS

A prospective parallel randomised trialin the setting of a non-profitable hospital in Brazil. The participants were patients scheduled for cardiac surgery. Patients were randomly allocated to VCV or PCV modes of ventilation and later both groups were transitioned to PSV mode.

RESULTS

All data were recorded for 5 minutes in each of the three different phases: T1) in assisted breath, T2) initial spontaneous breath and T3) final spontaneous breath, a marking point prior to extubation. Asynchronies were detected and counted by visual inspection method by two independent investigators. Reliability, inter-rater agreement of asynchronies, asynchronies incidence, total and specific asynchrony indexes (AIt and AIspecific) and odds of AI ≥10% weighted by total asynchrony were analysed. A total of 17 patients randomly allocated to the VCV (n=9) or PCV (n=8) group completed the study. High inter-rated agreement for AIt (ICC 0.978; IC95%, 0,963-0.987) and good reliability (r=0.945; p<0.001) were found. Eighty-two % of patients presented asynchronies, although only 7% of their total breathing cycles were asynchronous. Early cycling and double triggering had the highest rates of asynchrony with no difference between groups. The highest odds of AI ≥10% were observed in VCV regardless the phase: OR 2.79 (1.36-5.73) in T1 vs T2, p=0.005; OR 2.61 (1.27-5.37) in T1 vs T3, p=0.009 and OR 4.99 (2.37-10.37) in T2 vs T3, p<0.001.

CONCLUSIONS

There was a high incidence of breathing asynchrony in postoperative cardiac patients, especially when initially ventilated in VCV. VCV group had a higher chance of AI ≥10% and this chance remained high in the following PSV phases.

Identifying and managing patient-ventilator asynchrony: An international survey

OBJECTIVE

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

DESIGN

An analytical cross-sectional study was carried out.

SETTING

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

PARTICIPANTS

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

MAIN VARIABLES OF INTEREST

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

MAIN BODY

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

CONCLUSIONS

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

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.

Predicting Patient-ventilator Asynchronies with Hidden Markov Models

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

Flow and airway pressure analysis for detecting ineffective effort during mechanical ventilation in neuromuscular patients

Ineffective efforts (IEs) are among the most common types of patient–ventilator asynchrony. The objective of this study is to validate IE detection during expiration using pressure and flow signals, with respiratory effort detection by esophageal pressure (Pes) measurement as the reference, in patients with neuromuscular diseases (NMDs). We included 10 patients diagnosed with chronic respiratory failure related to NMD. Twenty-eight 5-minute recordings of daytime ventilation were studied for IE detection. Standard formulas were used to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of IE detection using pressure and flow signals compared to Pes measurement. Mean sensitivity and specificity of flow and pressure signal-based IE detection versus Pes measurement were 97.5% ± 5.3% and 91.4% ± 13.7%, respectively. NPV was 98.1% ± 8.2% and PPV was 67.6% ± 33.8%. Spearman’s rank correlation coefficient indicated a moderately significant correlation between frequencies of IEs and controlled cycles (ρ = 0.50 and p = 0.01). Among respiratory cycles, 311 (11.2%) were false-positive IEs overall. Separating false-positive IEs according to their mechanisms, we observed premature cycling in 1.2% of cycles, delayed ventilator triggering in 0.1%, cardiac contraction in 9.2%, and upper airway instability during expiration in 0.3%. Using flow and pressure signals to detect IEs is a simple and rapid method that produces adequate data to support clinical decisions.

Patient-ventilator asynchrony

Patient-v entilator asynchrony (PVA) is a mismatch between the patient, regarding time, flow, volume, or pressure demands of the patient respiratory system, and the ventilator, which supplies such demands, during mechanical ventilation (MV). It is a common phenomenon, with incidence rates ranging from 10% to 85%. PVA might be due to factors related to the patient, to the ventilator, or both. The most common PVA types are those related to triggering, such as ineffective effort, auto-triggering, and double triggering; those related to premature or delayed cycling; and those related to insufficient or excessive flow. Each of these types can be detected by visual inspection of volume, flow, and pressure waveforms on the mechanical ventilator display. Specific ventilatory strategies can be used in combination with clinical management, such as controlling patient pain, anxiety, fever, etc. Deep sedation should be avoided whenever possible. PVA has been associated with unwanted outcomes, such as discomfort, dyspnea, worsening of pulmonary gas exchange, increased work of breathing, diaphragmatic injury, sleep impairment, and increased use of sedation or neuromuscular blockade, as well as increases in the duration of MV, weaning time, and mortality. Proportional assist ventilation and neurally adjusted ventilatory assist are modalities of partial ventilatory support that reduce PVA and have shown promise. This article reviews the literature on the types and causes of PVA, as well as the methods used in its evaluation, its potential implications in the recovery process of critically ill patients, and strategies for its resolution.

Patient-ventilator asynchrony

Patient-v entilator asynchrony (PVA) is a mismatch between the patient, regarding time, flow, volume, or pressure demands of the patient respiratory system, and the ventilator, which supplies such demands, during mechanical ventilation (MV). It is a common phenomenon, with incidence rates ranging from 10% to 85%. PVA might be due to factors related to the patient, to the ventilator, or both. The most common PVA types are those related to triggering, such as ineffective effort, auto-triggering, and double triggering; those related to premature or delayed cycling; and those related to insufficient or excessive flow. Each of these types can be detected by visual inspection of volume, flow, and pressure waveforms on the mechanical ventilator display. Specific ventilatory strategies can be used in combination with clinical management, such as controlling patient pain, anxiety, fever, etc. Deep sedation should be avoided whenever possible. PVA has been associated with unwanted outcomes, such as discomfort, dyspnea, worsening of pulmonary gas exchange, increased work of breathing, diaphragmatic injury, sleep impairment, and increased use of sedation or neuromuscular blockade, as well as increases in the duration of MV, weaning time, and mortality. Proportional assist ventilation and neurally adjusted ventilatory assist are modalities of partial ventilatory support that reduce PVA and have shown promise. This article reviews the literature on the types and causes of PVA, as well as the methods used in its evaluation, its potential implications in the recovery process of critically ill patients, and strategies for its resolution.

Mechanical Ventilation: State of the Art

Mechanical ventilation is the most used short-term life support technique worldwide and is applied daily for a diverse spectrum of indications, from scheduled surgical procedures to acute organ failure. This state-of-the-art review provides an update on the basic physiology of respiratory mechanics, the working principles, and the main ventilatory settings, as well as the potential complications of mechanical ventilation. Specific ventilatory approaches in particular situations such as acute respiratory distress syndrome and chronic obstructive pulmonary disease are detailed along with protective ventilation in patients with normal lungs. We also highlight recent data on patient-ventilator dyssynchrony, humidified high-flow oxygen through nasal cannula, extracorporeal life support, and the weaning phase. Finally, we discuss the future of mechanical ventilation, addressing avenues for improvement.