• Competency
• Emergency
• Patient-Ventilator Asynchrony

• Humans
• Cross-Sectional Studies
• Ethiopia
• Clinical Competence
• Internship and Residency
• Male
• Emergency Medicine/education
• Critical Care
• Female
• Respiration, Artificial
• Adult
• Ventilators, Mechanical
• Patient-Ventilator Asynchrony

• Intensive care unit
• Knowledge
• Mechanical ventilation
• Patient-ventilator asynchrony
• Waveform analysis

• critical care
• education
• simulation
• training

• Acute respiratory distress syndrome
• Flow-limited ventilator dyssynchrony
• Mathematical model
• Statistical inference
• Ventilator dyssynchrony
• Ventilator waveform
• Ventilator-induced lung injury

• Humans
• Lung Injury
• Ventilators, Mechanical
• Lung/physiology
• Respiration, Artificial/methods

• R01 LM012734 NLM NIH HHS
• R01 LM006910 NLM NIH HHS
• R01 HL151630 NHLBI NIH HHS
• R00 HL128944 NHLBI NIH HHS
• K24 HL089223 NHLBI NIH HHS

• Adult
• Humans
• Respiration, Artificial/adverse effects
• Dyspnea/therapy
• Dyspnea/etiology
• Intensive Care Units
• Critical Care
• Pain
• Critical Illness

• Critically ill
• Dyspnoea
• Management
• Mechanical ventilation

• Adult
• Humans
• Dyspnea/etiology
• Dyspnea/therapy
• Intensive Care Units
• Medicine
• Pain
• Respiration, Artificial/adverse effects

• Acute cor pulmonale
• Acute respiratory distress syndrome
• Driving pressure
• Electrical impedance tomography
• Esophageal manometry
• Extracorporeal membrane oxygenation
• Mechanical power
• Positive end expiratory pressure

• T32HL007317 NIH HHS

• ARDS
• Acute respiratory distress syndrome
• Asynchrony
• Automation
• Detection
• Dyssynchrony
• Mechanical ventilation
• Reverse triggering

• Humans
• Artificial Intelligence
• Lung Injury
• Prospective Studies
• Neural Networks, Computer
• Respiratory Distress Syndrome/diagnosis
• Respiratory Distress Syndrome/therapy
• Respiratory Distress Syndrome/etiology
• Respiration, Artificial/adverse effects

• KL2 TR002542 NCATS NIH HHS
• UL1 TR001445 NCATS NIH HHS

• Artificial ventilation
• Interactive ventilatory support
• Mechanical ventilation
• Respiratory failure
• Respiratory diaphragm

• ICU mechanical ventilation
• double triggering
• ineffective triggering
• patient-ventilator asynchrony
• remote network platform

• critical care ultrasound
• esophageal pressure
• intensive care
• lung injury
• lung mechanics
• mechanical ventilation
• monitoring
• noninvasive ventilation

• diaphragm electrical activity
• neurally adjusted ventilatory assist (NAVA)
• non-invasive ventilation
• patient–ventilator asynchrony
• pressure support ventilation (PSV)
• proportional assist ventilation (PAV)
• ventilator waveforms

• Bi-level positive airway pressure
• Children
• Continuous positive airway pressure
• High-flow nasal cannula
• Mechanical ventilation
• Negative pressure ventilation
• Noninvasive ventilation
• Pediatric intensive care

• acute respiratory failure
• asynchrony index
• conventional mechanical ventilation
• neurally adjusted ventilatory assist
• patient-ventilator asynchrony

Breathing asynchronies are mismatches between the requests of mechanically ventilated subjects and the support provided by mechanical ventilators. The most widespread technique in identifying these pathological conditions is the visual analysis of the intra-tracheal pressure and flow time-trends. This work considers a recently introduced pressure-flow representation technique and investigates whether it can help nurses in the early detection of anomalies that can represent asynchronies. Twenty subjects—ten Intensive Care Unit (ICU) nurses and ten persons inexperienced in medical practice—were asked to find asynchronies in 200 breaths pre-labeled by three experts. The new representation increases significantly the detection capability of the subjects—average sensitivity soared from 0.622 to 0.905—while decreasing the classification time—from 1107.0 to 567.1 s on average—at the price of a not statistically significant rise in the number of wrong identifications—specificity average descended from 0.589 to 0.52. Moreover, the differences in experience between the nurse group and the inexperienced group do not affect the sensitivity, specificity, or classification times. The pressure-flow diagram significantly increases sensitivity and decreases the response time of early asynchrony detection performed by nurses. Moreover, the data suggest that operator experience does not affect the identification results. This outcome leads us to believe that, in emergency contexts with a shortage of nurses, intensive care nurses can be supplemented, for the sole identification of possible respiratory asynchronies, by inexperienced staff.

• Breath representation
• ICU monitoring
• Mechanical ventilator
• Respiratory asynchronies

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.

• automatic adjustment system
• cycling-off
• patient-ventilator asynchrony
• pressure support ventilation
• trigger

Ineffective triggering is frequent during pressure support ventilation (PSV) and may persist despite ventilator adjustment, leading to refractory asynchrony. We aimed to assess the effect of proportional assist ventilation with load-adjustable gain factors (PAV+) on the occurrence of refractory ineffective triggering.

Observational assessment followed by prospective cross-over physiological study.

Academic medical ICU.

Ineffective triggering was detected during PSV by a twice-daily inspection of the ventilator’s screen. The impact of pressure support level (PSL) adjustments on the occurrence of asynchrony was recorded. Patients experiencing refractory ineffective triggering, defined as persisting asynchrony at the lowest tolerated PSL, were included in the physiological study.

Physiological study: Flow, airway, and esophageal pressures were continuously recorded during 10 min under PSV with the lowest tolerated PSL, and then under PAV+ with the gain adjusted to target a muscle pressure between 5 and 10 cmH₂O.

Primary endpoint was the comparison of asynchrony index between PSV and PAV+ after PSL and gain adjustments.

Among 36 patients identified having ineffective triggering under PSV, 21 (58%) exhibited refractory ineffective triggering. The lowest tolerated PSL was higher in patients with refractory asynchrony as compared to patients with non-refractory ineffective triggering. Twelve out of the 21 patients with refractory ineffective triggering were included in the physiological study. The median lowest tolerated PSL was 17 cmH₂O [12–18] with a PEEP of 7 cmH₂O [5–8] and FiO₂ of 40% [39–42]. The median gain during PAV+ was 73% [65–80]. The asynchrony index was significantly lower during PAV+ than PSV (2.7% [1.0–5.4] vs. 22.7% [10.3–40.1], p < 0.001) and consistently decreased in every patient with PAV+. Esophageal pressure–time product (PTPes) did not significantly differ between the two modes (107 cmH₂O/s/min [79–131] under PSV vs. 149 cmH₂O/s/min [129–170] under PAV+, p = 0.092), but the proportion of PTPes lost in ineffective triggering was significantly lower with PAV+ (2 cmH₂O/s/min [1–6] vs. 8 cmH₂O/s/min [3–30], p = 0.012).

Among patients with ineffective triggering under PSV, PSL adjustment failed to eliminate asynchrony in 58% of them (21 of 36 patients). In these patients with refractory ineffective triggering, switching from PSV to PAV+ significantly reduced or even suppressed the incidence of asynchrony.

The online version contains supplementary material available at 10.1186/s13613-021-00935-0.

• Ineffective triggering
• Mechanical ventilation
• Patient
• Proportional assist ventilation
• Ventilator asynchrony

• acute respiratory distress syndrome
• mathematical model
• statistical inference
• ventilator waveform
• ventilator-induced lung injury

• K23 HL145011 NHLBI NIH HHS
• R00 HL128944 NHLBI NIH HHS
• R01 HL151630 NHLBI NIH HHS
• National Institutes of Health

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.

• airway pressure–time waveform
• assisted ventilator mode
• autotriggering
• cycling dyssynchrony
• double triggering
• dyssynchrony
• flow dyssynchrony
• flow–time waveform
• ineffective efforts
• patient–ventilator interaction
• reverse triggering

Patient–ventilator asynchrony (PVA) is a common problem in patients undergoing invasive mechanical ventilation (MV) in the intensive care unit (ICU), and may accelerate lung injury and diaphragm mis-contraction. The impact of PVA on clinical outcomes has not been systematically evaluated. Effective interventions (except for closed-loop ventilation) for reducing PVA are not well established.

We performed a systematic review and meta-analysis to investigate the impact of PVA on clinical outcomes in patients undergoing MV (Part A) and the effectiveness of interventions for patients undergoing MV except for closed-loop ventilation (Part B). We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, ClinicalTrials.gov, and WHO-ICTRP until August 2020. In Part A, we defined asynchrony index (AI) ≥ 10 or ineffective triggering index (ITI) ≥ 10 as high PVA. We compared patients having high PVA with those having low PVA.

Eight studies in Part A and eight trials in Part B fulfilled the eligibility criteria. In Part A, five studies were related to the AI and three studies were related to the ITI. High PVA may be associated with longer duration of mechanical ventilation (mean difference, 5.16 days; 95% confidence interval [CI], 2.38 to 7.94; n = 8; certainty of evidence [CoE], low), higher ICU mortality (odds ratio [OR], 2.73; 95% CI 1.76 to 4.24; n = 6; CoE, low), and higher hospital mortality (OR, 1.94; 95% CI 1.14 to 3.30; n = 5; CoE, low). In Part B, interventions involving MV mode, tidal volume, and pressure-support level were associated with reduced PVA. Sedation protocol, sedation depth, and sedation with dexmedetomidine rather than propofol were also associated with reduced PVA.

PVA may be associated with longer MV duration, higher ICU mortality, and higher hospital mortality. Physicians may consider monitoring PVA and adjusting ventilator settings and sedatives to reduce PVA. Further studies with adjustment for confounding factors are warranted to determine the impact of PVA on clinical outcomes.

Trial registration protocols.io (URL: https://www.protocols.io/view/the-impact-of-patient-ventilator-asynchrony-in-adu-bsqtndwn, 08/27/2020).

The online version contains supplementary material available at 10.1186/s40560-021-00565-5.

• Asynchrony index
• ICU
• Ineffective triggering
• Mechanical ventilation
• Mortality
• Patient–ventilator interaction

• Humans
• Tidal Volume/physiology
• Middle Aged
• Male
• Female
• Respiration, Artificial
• Retrospective Studies
• Aged
• Hypoxia/mortality
• Hypoxia/physiopathology
• Hospital Mortality
• Intensive Care Units
• Pressure

• UH3 HL140144 NHLBI NIH HHS
• R25 HL126140 NHLBI NIH HHS
• UG3 HL140144 NHLBI NIH HHS
• R61 HL151254 NHLBI NIH HHS
• R01 AI135108 NIAID NIH HHS
• U01 HL128954 NHLBI NIH HHS
• R33 HL151254 NHLBI NIH HHS
• R21 AG059202 NIA NIH HHS
• Flinn Foundation
• nih

Ventilator auto-triggering is associated with poor outcomes. Herein, we present a case of delayed tracheal extubation after cardiac surgery due to cardiogenic auto-triggering.

A 73-year-old male with chronic constrictive pericarditis underwent radical pericardiectomy. After confirming hemodynamic stability, we conducted spontaneous breathing trial (SBT) with a flow-trigger sensitivity of 1 L/min. As his respiratory rate (RR) increased to more than 60 breaths/min and tidal volume decreased to less than 100 mL, this SBT was considered a failure. Next morning, SBT was reperformed and the result was unchanged. However, we noticed that his heart rate and RR were the same and suspected auto-triggering caused by cardiogenic oscillations. We changed ventilator mode from flow triggering to pressure triggering of −2 cmH₂O and he was uneventfully extubated.

We experienced a case of delayed tracheal extubation after cardiac surgery due to cardiogenic auto-triggering. Auto-triggering can be reduced by changing ventilator trigger mode.

• Cardiogenic oscillation
• Delayed tracheal extubation
• Ventilator auto-triggering

• respiratory distress syndrome
• respiratory tract diseases
• respiration
• outcomes research
• respiratory signs and symptoms
• risk factors

Patient–ventilator asynchrony in Saudi Arabia practices is common, and more emphasis on how to mitigate such a clinical problem is needed. This letter is intended to shed the light on the current national evidence of patient–ventilator asynchrony and how to step ahead for better patients' ventilation management.

• Ventilator
• Asynchrony
• Critical care
• Saudi Arabia
• Double triggering
• Respiratory

• bucking
• patient-ventilator synchrony
• synchrony
• vent dyssynchrony
• vent synchrony
• waveform

• ARDS
• COVID-19
• emergency
• low-cost
• severe acute respiratory syndrome
• ventilator

Early diagnosis of shock is a predetermining factor for a good prognosis in intensive care. An elevated central venous to arterial PCO₂ difference (∆PCO₂) over 0.8 kPa (6 mm Hg) is indicative of low blood flow states. Disturbances around the time of blood sampling could result in inaccurate calculations of ∆PCO₂, thereby misrepresenting the patient status. This study aimed to determine the influences of acute changes in ventilation on ∆PCO₂ and understand its clinical implications.

To investigate the isolated effects of changes in ventilation on ∆PCO₂, eight pigs were studied in a prospective observational cohort. Arterial and central venous catheters were inserted following anaesthetisation. Baseline ventilator settings were titrated to achieve an EtCO₂ of 5±0.5 kPa (V_T = 8 mL/kg, Freq = 14 ± 2/min). Blood was sampled simultaneously from both catheters at baseline and 30, 60, 90, 120, 180 and 240 s after a change in ventilation. Pigs were subjected to both hyperventilation and hypoventilation, wherein the respiratory frequency was doubled or halved from baseline. ∆PCO₂ changes from baseline were analysed using repeated measures ANOVA with post-hoc analysis using Bonferroni’s correction.

∆PCO₂ at baseline for all pigs was 0.76±0.29 kPa (5.7±2.2 mm Hg). Following hyperventilation, there was a rapid increase in the ∆PCO₂, increasing maximally to 1.35±0.29 kPa (10.1±2.2 mm Hg). A corresponding decrease in the ∆PCO₂ was seen following hypoventilation, decreasing maximally to 0.23±0.31 kPa (1.7±2.3 mm Hg). These changes were statistically significant from baseline 30 s after the change in ventilation.

Disturbances around the time of blood sampling can rapidly affect the PCO₂, leading to inaccurate calculations of the ∆PCO₂, resulting in misinterpretation of patient status. Care should be taken when interpreting blood gases, if there is doubt as to the presence of acute and transient changes in ventilation.

• not applicable

• Artificial
• Intensive care units
• Intermittent positive-pressure ventilation
• Patient–ventilator asynchrony
• Positive-pressure respiration
• Respiration

Knowledge of ventilator waveforms is important for clinicians working with children requiring mechanical ventilation. This review covers the basics of how to interpret and use data from ventilator waveforms in the pediatric intensive care unit.

Patient-ventilator asynchrony (PVA) is a common finding in pediatric patients and observed in approximately one-third of ventilator breaths. PVA is associated with worse outcomes including increased length of mechanical ventilation, increased length of stay, and increased mortality. Identification of PVA is possible with a thorough knowledge of ventilator waveforms.

Ventilator waveforms are graphical descriptions of how a breath is delivered to a patient. These include three scalars (flow versus time, volume versus time, and pressure versus time) and two loops (pressure-volume and flow-volume). Thorough understanding of both scalars and loops, and their characteristic appearances, is essential to being able to evaluate a patient’s respiratory mechanics and interaction with the ventilator.

• Graphics
• Loops
• Pediatric
• Scalars
• Ventilation
• Waveforms

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

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

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

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

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

• critical care
• fellowship education
• mechanical ventilation
• waveform analysis

Patient–ventilator asynchrony is associated with increased morbidity and mortality. A direct causative relationship between Patient–ventilator asynchrony and adverse clinical outcome have yet to be demonstrated. It is hypothesized that during trigger errors excessive pleural pressure swings are generated, contributing to increased work-of-breathing and self-inflicted lung injury. The objective of this study was to determine the additional work-of-breathing and pleural pressure swings caused by trigger errors in mechanically ventilated children.

Prospective observational study in a tertiary paediatric intensive care unit in an university hospital. Patients ventilated > 24 h and < 18 years old were studied. Patients underwent a 5-min recording of the ventilator flow–time, pressure–time and oesophageal pressure–time scalar. Pressure–time–product calculations were made as a proxy for work-of-breathing. Oesophageal pressure swings, as a surrogate for pleural pressure swings, during trigger errors were determined.

Nine-hundred-and-fifty-nine trigger errors in 28 patients were identified. The additional work-of-breathing caused by trigger errors showed great variability among patients. The more asynchronous breaths were present the higher the work-of-breathing of these breaths. A higher spontaneous breath rate led to a lower amount of trigger errors. Patient–ventilator asynchrony was not associated with prolonged duration of mechanical ventilation or paediatric intensive care stay.

The additional work-of-breathing caused by trigger errors in ventilated children can take up to 30–40% of the total work-of-breathing. Trigger errors were less common in patients breathing spontaneously and those able to generate higher pressure–time–product and pressure swings.

Not applicable.

• Ineffective triggering
• Mechanical ventilation
• Paediatric
• Patient self-inflicted-lung injury
• Patient–ventilator asynchrony
• Work-of-breathing

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.

• Acute respiratory distress syndrome
• patient self-inflicted lung injury
• ventilator dyssynchrony
• ventilator-induced lung injury

• Critical care
• equipment design
• eye movement measurements
• user computer interface
• ventilator

• Equipment Design
• Humans
• Respiration, Artificial/methods
• Task Performance and Analysis
• Ventilators, Mechanical
• Workload

• tongji medical college, huazhong university of science and technology

• Critical Care/methods
• Critical Illness
• Deep Sedation/methods
• Humans
• Hypnotics and Sedatives/administration & dosage
• Maximal Respiratory Pressures
• Mental Disorders
• Prognosis
• Respiration, Artificial/adverse effects
• Respiratory Mechanics/drug effects
• Ventilators, Mechanical

• auto-triggering
• cardiogenic oscillations
• flow-triggering
• hemoperitoneum

• Asynchrony
• Critical care
• Respiratory
• Ventilator
• Waveform analysis

• Critical Care
• Female
• Humans
• Physicians
• Respiration, Artificial
• Ventilators, Mechanical

• Automatic monitoring
• Machine learning
• Mechanical ventilator
• Respiratory asynchrony

• Artificial respiration
• Interactive ventilatory support
• Mechanical ventilators
• Respiratory insufficiency
• Respiratory mechanics
• Ventilator-induced lung injury

• Asynchronies
• Big data
• Cognitive
• Critically ill
• Heart lung interaction
• ICU
• Mechanical ventilation
• Outcome
• Patient-ventilator interaction
• Psychological disorders

• Asynchronous breathing
• Model-based methods
• Patient effort
• Respiratory mechanics

• Biostatistics
• Critical Care
• Databases, Factual
• Humans
• Models, Statistical
• Respiratory Function Tests/standards
• Respiratory Insufficiency/physiopathology
• Respiratory Mechanics/physiology