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

Setting positive end-expiratory pressure: role in diaphragm-protective ventilation

PURPOSE OF REVIEW

With mechanical ventilation, positive end-expiratory pressure (PEEP) is applied to improve oxygenation and lung homogeneity. However, PEEP setting has been hypothesized to contribute to critical illness associated diaphragm dysfunction via several mechanisms. Here, we discuss the impact of PEEP on diaphragm function, activity and geometry.

RECENT FINDINGS

PEEP affects diaphragm geometry: it induces a caudal movement of the diaphragm dome and shortening of the zone of apposition. This results in reduced diaphragm neuromechanical efficiency. After prolonged PEEP application, the zone of apposition adapts by reducing muscle fiber length, so-called longitudinal muscle atrophy. When PEEP is withdrawn, for instance during a spontaneous breathing trial, the shortened diaphragm muscle fibers may over-stretch which may lead to (additional) diaphragm myotrauma. Furthermore, PEEP may either increase or decrease respiratory drive and resulting respiratory effort, probably depending on lung recruitability. Finally, the level of PEEP can also influence diaphragm activity in the expiratory phase, which may be an additional mechanism for diaphragm myotrauma.

SUMMARY

Setting PEEP could play an important role in both lung and diaphragm protective ventilation. Both high and low PEEP levels could potentially introduce or exacerbate diaphragm myotrauma. Today, the impact of PEEP setting on diaphragm structure and function is in its infancy, and clinical implications are largely unknown.

Understanding the mechanisms of ventilator-induced lung injury using animal models

Mechanical ventilation is a life-saving therapy in several clinical situations, promoting gas exchange and providing rest to the respiratory muscles. However, mechanical ventilation may cause hemodynamic instability and pulmonary structural damage, which is known as ventilator-induced lung injury (VILI). The four main injury mechanisms associated with VILI are as follows: barotrauma/volutrauma caused by overstretching the lung tissues; atelectrauma, caused by repeated opening and closing of the alveoli resulting in shear stress; and biotrauma, the resulting biological response to tissue damage, which leads to lung and multi-organ failure. This narrative review elucidates the mechanisms underlying the pathogenesis, progression, and resolution of VILI and discusses the strategies that can mitigate VILI. Different static variables (peak, plateau, and driving pressures, positive end-expiratory pressure, and tidal volume) and dynamic variables (respiratory rate, airflow amplitude, and inspiratory time fraction) can contribute to VILI. Moreover, the potential for lung injury depends on tissue vulnerability, mechanical power (energy applied per unit of time), and the duration of that exposure. According to the current evidence based on models of acute respiratory distress syndrome and VILI, the following strategies are proposed to provide lung protection: keep the lungs partially collapsed (SaO2 > 88%), avoid opening and closing of collapsed alveoli, and gently ventilate aerated regions while keeping collapsed and consolidated areas at rest. Additional mechanisms, such as subject-ventilator asynchrony, cumulative power, and intensity, as well as the damaging threshold (stress-strain level at which tidal damage is initiated), are under experimental investigation and may enhance the understanding of VILI.

A Novel Application of Spectrograms with Machine Learning Can Detect Patient Ventilator Dyssynchrony

Patients in intensive care units are frequently supported by mechanical ventilation. There is increasing awareness of patient-ventilator dyssynchrony (PVD), a mismatch between patient respiratory effort and assistance provided by the ventilator, as a risk factor for infection, narcotic exposure, lung injury, and adverse neurocognitive effects. One of the most injurious consequences of PVD are double cycled (DC) breaths when two breaths are delivered by the ventilator instead of one. Prior efforts to identify PVD have limited efficacy. An automated method to identify PVD, independent of clinician expertise, acumen, or time, would potentially permit early, targeted treatment to avoid further harm. We performed secondary analyses of data from a clinical trial of children with acute respiratory distress syndrome. Waveforms of ventilator flow, airway pressure and esophageal manometry were annotated to identify DC breaths and underlying PVD subtypes. Spectrograms were generated from those waveforms to train Convolutional Neural Network (CNN) models in detecting DC and underlying PVD subtypes: Reverse Trigger (RT) and Inadequate Support (IS). The DC breath detection model yielded AUROC of 0.980, while the multi-target detection model for underlying dyssynchrony yielded AUROC of 0.980 (RT) and 0.976 (IS). When operating at 75% sensitivity, DC breath detection had a number needed to alert (NNA) 1.3 (99% specificity), while underlying PVD had a NNA 1.6 (98.5% specificity) for RT and NNA 4.0 (98.2% specificity) for IS. CNNs using spectrograms of ventilator waveforms can identify DC breaths and detect the underlying PVD for targeted clinical interventions.

Static Respiratory System Compliance as a Predictor of Extubation Failure in Patients with Acute Respiratory Failure

PURPOSE

Ventilator weaning protocols rely in part on objective indices to best predict extubation failure in the critically ill. We investigated static respiratory system compliance (RC) as a predictor of extubation failure, in comparison to extubation readiness using rapid shallow breathing index (RSBI).

MATERIAL AND METHODS

This was a cross-sectional, multi-institutional study of mechanically ventilated patients admitted between 12/01/2017 and 12/01/2019. All patients older than 18 years with a documented spontaneous breathing trial and extubation trial were included. RC and RSBI were calculated prior to the extubation trial. The primary outcome was extubation failure-defined as need for reintubation within 72 h from time of extubation.

RESULTS

Of the 2263 patients, 55.8% were males with a mean age of 68 years. The population consisted mostly of Caucasians (73%) and African Americans (20.4%). 274 (12.1%) patients required reintubation within 72 h. On multivariate logistic regression after adjusting for age, sex, body mass index (BMI), admission Sequential Organ Failure Assessment (SOFA) score, number of ventilator days, and the P/F ratio on the day of extubation, RC remained the strongest predictor for extubation failure at 24 h (aOR 1.45; 95% CI 1.00-2.10) and 72 h (aOR 1.58; 95% CI 1.15-2.17). There was no significant association between RSBI and extubation failure at 24 (aOR 1.00; 95% CI 0.99-1.01) or at 72 h (aOR 1.00; 95% CI 0.99-1.01).

CONCLUSION

RC measured on the day of extubation is a promising physiological discriminant to potentially risk stratify patients with acute respiratory failure for extubation readiness. We recommend further validation studies in prospective cohorts.

Linking Acute Physiology to Outcomes in the ICU: Challenges and Solutions for Research

ICU clinicians rely on bedside physiological measurements to inform many routine clinical decisions. Because deranged physiology is usually associated with poor clinical outcomes, it is tempting to hypothesize that manipulating and intervening on physiological parameters might improve outcomes for patients. However, testing these hypotheses through mathematical models of the relationship between physiology and outcomes presents a number of important methodological challenges. These models reflect the theories of the researcher and can therefore be heavily influenced by one's assumptions and background beliefs. Model building must therefore be approached with great care and forethought, because failure to consider relevant sources of measurement error, confounding, coupling, and time dependency or failure to assess the direction of causality for associations of interest before modeling may give rise to spurious results. This paper outlines the main challenges in analyzing and interpreting these models and offers potential solutions to address these challenges.

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.

Hyperinflation and its association with successful transition to home ventilator devices in infants with chronic respiratory failure and severe bronchopulmonary dysplasia

OBJECTIVE

To estimate the association between lung hyperinflation and the time to successful transition to home ventilators in infants with sBPD and chronic respiratory failure.

DESIGN/METHODS

Infants with sBPD <32 weeks' gestation who received tracheostomies were identified. Hyperinflation was the main exposure. Time from tracheostomy to successful transition to the home ventilator was the main outcome. Kaplan-Meier and multivariable Cox proportional hazards were used to estimate the relationships between hyperinflation and the main outcome.

RESULTS

Sixty-two infants were included; 26 (42%) were hyperinflated. Eleven died before transition, and 51 successfully transitioned. Hyperinflation was associated with both mortality (31% vs 8.3%, p = 0.02) and an increased duration (72 vs. 56 days) to successful transition (hazard ratio (HR) = 0.38, 95% CI: 0.19, 0.76, p = 0.006). Growth velocity was similar after tracheostomy placement.

CONCLUSIONS

In infants with chronic respiratory failure and sBPD <32 weeks' gestation, hyperinflation is related to mortality and inpatient morbidities.

Effect of patient-ventilator asynchrony on lung and diaphragmatic injury in experimental acute respiratory distress syndrome in a porcine model

BACKGROUND

Patient-ventilator asynchrony during mechanical ventilation may exacerbate lung and diaphragm injury in spontaneously breathing subjects. We investigated whether subject-ventilator asynchrony increases lung or diaphragmatic injury in a porcine model of acute respiratory distress syndrome (ARDS).

METHODS

ARDS was induced in adult female pigs by lung lavage and injurious ventilation before mechanical ventilation by pressure assist-control for 12 h. Mechanically ventilated pigs were randomised to breathe spontaneously with or without induced subject-ventilator asynchrony or neuromuscular block (n=7 per group). Subject-ventilator asynchrony was produced by ineffective, auto-, or double-triggering of spontaneous breaths. The primary outcome was mean alveolar septal thickness (where thickening of the alveolar wall indicates worse lung injury). Secondary outcomes included distribution of ventilation (electrical impedance tomography), lung morphometric analysis, inflammatory biomarkers (gene expression), lung wet-to-dry weight ratio, and diaphragmatic muscle fibre thickness.

RESULTS

Subject-ventilator asynchrony (median [interquartile range] 28.8% [10.4] asynchronous breaths of total breaths; n=7) did not increase mean alveolar septal thickness compared with synchronous spontaneous breathing (asynchronous breaths 1.0% [1.6] of total breaths; n=7). There was no difference in mean alveolar septal thickness throughout upper and lower lung lobes between pigs randomised to subject-ventilator asynchrony vs synchronous spontaneous breathing (87.3-92.2 μm after subject-ventilator asynchrony, compared with 84.1-95.0 μm in synchronised spontaneous breathing;). There were also no differences between groups in wet-to-dry weight ratio, diaphragmatic muscle fibre thickness, atelectasis, lung aeration, or mRNA expression levels for inflammatory cytokines pivotal in ARDS pathogenesis.

CONCLUSIONS

Subject-ventilator asynchrony during spontaneous breathing did not exacerbate lung injury and dysfunction in experimental porcine ARDS.

Simulation-based Assessment to Measure Proficiency in Mechanical Ventilation among Residents

Background

Mechanical ventilation (MV) skills are essential for clinicians caring for critically ill patients, yet few training programs use structured curricula and appropriate assessments. Objective structured clinical exams (OSCEs) have been used to assess clinical competency in many areas, but there are no OSCE models focused on MV.

Objective

To develop and validate a simulation-based assessment (SBA) with an OSCE structure to assess baseline MV competence among residents and identify knowledge gaps.

Methods

We developed an SBA using a lung simulator and a mechanical ventilator, and an OSCE structure, with six clinical scenarios in MV. We included internal medicine residents at the beginning of their rotation in the respiratory intensive care unit (ICU) of a university-affiliated hospital. A subset of residents was also evaluated with a validated multiple-choice exam (MCE) at the beginning and at the end of the ICU rotation. Scores on both assessments were normalized to range from 0 to 10. We used Cronbach’s α coefficient to assess reliability and Spearman correlation to estimate the correlation between the SBA and the MCE.

Results

We included 80 residents, of whom 42 also completed the MCE examinations. The final version of the SBA had 32 items, and the Cronbach’s α coefficient was 0.72 (95% confidence interval [CI], 0.64–0.81). The average SBA score was 6.2 ± 1.3, and performance was variable across items, with 80% correctly adjusting initial ventilatory settings and only 12% correctly identifying asynchrony. The MCE had 24 questions, and the average score was 7.6 ± 2.4 at the beginning of the rotation and 8.2 ± 2.3 at the end of the rotation (increase of 0.6 points; 95% CI, 0.30–0.90; P < 0.001). There was moderate correlation between the SBA and the MCE (rho = 0.41; P = 0.002).

Conclusion

We developed and validated an objective structured assessment on MV using a pulmonary simulator and a mechanical ventilator addressing the main competencies in MV. The performance of residents in the SBA at the beginning of an ICU rotation was lower than the performance in MCE, highlighting the need for greater emphasis on practical skills in MV during residency.

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

OBJECTIVES

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

DESIGN

Registry-based, real-world study.

BACKGROUND

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

SUBJECTS

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

INTERVENTIONS

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

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Patient-ventilator asynchrony, impact on clinical outcomes and effectiveness of interventions: a systematic review and meta-analysis

Background

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.

Methods

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.

Results

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.

Conclusions

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).

Supplementary Information

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

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.

Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

OBJECTIVES

Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis.

DESIGN

Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours.

SETTING

The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019.

PATIENTS

Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours.

CONCLUSIONS

This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.

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).

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

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

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

Background

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

Methods

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

Results

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

Conclusions

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

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

Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm

Background and objectives: Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics. Methods: The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs). Results: A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90; p < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13; p < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates. Conclusions: Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH₂O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.