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

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model

BACKGROUND

Ventilator dyssynchrony (VD) can worsen lung injury and is challenging to detect and quantify due to the complex variability in the dyssynchronous breaths. While machine learning (ML) approaches are useful for automating VD detection from the ventilator waveform data, scalable severity quantification and its association with pathogenesis and ventilator mechanics remain challenging.

OBJECTIVE

We develop a systematic framework to quantify pathophysiological features observed in ventilator waveform signals such that they can be used to create feature-based severity stratification of VD breaths.

METHODS

A mathematical model was developed to represent the pressure and volume waveforms of individual breaths in a feature-based parametric form. Model estimates of respiratory effort strength were used to assess the severity of flow-limited (FL)-VD breaths compared to normal breaths. A total of 93,007 breath waveforms from 13 patients were analyzed.

RESULTS

A novel model-defined continuous severity marker was developed and used to estimate breath phenotypes of FL-VD breaths. The phenotypes had a predictive accuracy of over 97% with respect to the previously developed ML-VD identification algorithm. To understand the incidence of FL-VD breaths and their association with the patient state, these phenotypes were further successfully correlated with ventilator-measured parameters and electronic health records.

CONCLUSION

This work provides a computational pipeline to identify and quantify the severity of FL-VD breaths and paves the way for a large-scale study of VD causes and effects. This approach has direct application to clinical practice and in meaningful knowledge extraction from the ventilator waveform data.

Adherence to lung protective mechanical ventilation in patients admitted to a surgical intensive care unit and the associated increased mortality

Background

The adherence rate to the lung protective ventilation (LPV) strategy, which is generally accepted as a standard practice in mechanically ventilated patients, reported in the literature is approximately 40%. This study aimed to determine the adherence rate to the LPV strategy, factors associated with this adherence, and related clinical outcomes in mechanically ventilated patients admitted to the surgical intensive care unit (SICU).

Methods

This prospective observational study was conducted in the SICU of a tertiary university-based hospital between April 2018 and February 2019. Three hundred and six adult patients admitted to the SICU who required mechanical ventilation support for more than 12 h were included. Ventilator parameters at the initiation of mechanical ventilation support in the SICU were recorded. The LPV strategy was defined as ventilation with a tidal volume of equal or less than 8 ml/kg of predicted body weight plus positive end-expiratory pressure of at least 5 cm H2O. Demographic and clinical data were recorded and analyzed.

Results

There were 306 patients included in this study. The adherence rate to the LPV strategy was 36.9%. Height was the only factor associated with adherence to the LPV strategy (odds ratio for each cm, 1.10; 95% confidence interval (CI), 1.06-1.15). Cox regression analysis showed that the LPV strategy was associated with increased 90-day mortality (hazard ratio, 1.73; 95% CI, 1.02-2.94).

Conclusion

The adherence rate to the LPV strategy among patients admitted to the SICU was modest. Further studies are warranted to explore whether the application of the LPV strategy is simply a marker of disease severity or a causative factor for increased mortality.

Agitation during anaesthetic emergence: An observational study of adult cardiac surgery patients in two Australian intensive care units

BACKGROUND

Anaesthetic emergence agitation among adult patients being recovered after open cardiac and/or thoracic aorta surgery has not been described.

OBJECTIVES

The objective of this study was to characterise emergence agitation in terms of incidence, clinical features, and consequences in a cohort of cardiac surgery patients being recovered in the intensive care unit (ICU).

METHODS

A prospective, observational pilot study was implemented. Over a 5-week period, the study was conducted in two metropolitan hospitals in Victoria, Australia. The cohort comprised all patients admitted to the ICUs aged ≥18 years, who had undergone cardiac surgery via an open sternotomy with general anaesthetic, and whose emergence was directly observed. Emergence agitation was defined as a Richmond Agitation and Sedation Scale score of ≥+2.

RESULTS

Fifty patients were observed. Emergence agitation occurred in 24/50 (48%) of patients. Patients with emergence agitation experienced more clinical consequences than patients with calm emergence, including a significantly greater number of episodes of airway compromise (12/24, 50%, p < 0.001); ventilator dyssynchrony (23/24, 96%, p = 0.004); and hypertension (13/24, 54%, p = 0.004). Significant treatment interference (potentially dangerous patient movements such as pulling tubes) occurred with 23/24 patients (96%, p < 0.0001). Patients who underwent emergence agitation required significantly more interventions during anaesthetic emergence than patients who underwent a calm emergence. Interventions included extra nursing measures (16/24, 67%, p = 0.001) administration of sedative and/or opioid intravenous boluses (22/24, 92%, p = 0.001) and vasoactive agents (15/24, 63%, p = 0.05).

CONCLUSIONS

In patients recovering from cardiac surgery in the ICU, emergence agitation was clinically important. Immediate interventions were required to prevent and manage complications.

Classifying multicenter approaches to invasive mechanical ventilation for infants with bronchopulmonary dysplasia using hierarchical clustering analysis

INTRODUCTION

Evidence-based ventilation strategies for infants with severe bronchopulmonary dysplasia (BPD) remain unknown. Determining whether contemporary ventilation approaches cluster as specific BPD strategies may better characterize care and enhance the design of clinical trials. The objective of this study was to test the hypothesis that unsupervised, multifactorial clustering analysis of point prevalence ventilator setting data would classify a discrete number of physiology-based approaches to mechanical ventilation in a multicenter cohort of infants with severe BPD.

METHODS

We performed a secondary analysis of a multicenter point prevalence study of infants with severe BPD treated with invasive mechanical ventilation. We clustered the cohort by mean airway pressure (MAP), positive end expiratory pressure (PEEP), set respiratory rate, and inspiratory time (Ti) using Ward's hierarchical clustering analysis (HCA).

RESULTS

Seventy-eight patients with severe BPD were included from 14 centers. HCA classified three discrete clusters as determined by an agglomerative coefficient of 0.97. Cluster stability was relatively strong as determined by Jaccard coefficient means of 0.79, 0.85, and 0.77 for clusters 1, 2, and 3, respectively. The median PEEP, MAP, rate, Ti, and PIP differed significantly between clusters for each comparison by Kruskall-Wallis testing (p < 0.0001).

CONCLUSIONS

In this study, unsupervised clustering analysis of ventilator setting data identified three discrete approaches to mechanical ventilation in a multicenter cohort of infants with severe BPD. Prospective trials are needed to determine whether these approaches to mechanical ventilation are associated with specific severe BPD clinical phenotypes and differentially modify respiratory outcomes.

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.

An Exploration of Critical Care Professionals' Strategies to Enhance Daily Implementation of the Assess, Prevent, and Manage Pain; Both Spontaneous Awakening and Breathing Trials; Choice of Analgesia and Sedation; Delirium Assess, Prevent, and Manage; Early Mobility and Exercise; and Family Engagement and Empowerment: A Group Concept Mapping Study

The goals of this exploratory study were to engage professionals from the Society for Critical Care Medicine ICU Liberation Collaborative ICUs to: 1) conceptualize strategies to enhance daily implementation of the Assess, prevent, and manage pain; Both spontaneous awakening and breathing trials; Choice of analgesia and sedation; Delirium assess, prevent, and manage; Early mobility and exercise; and Family engagement and empowerment (ABCDEF) bundle from different perspectives and 2) identify strategies to prioritize for implementation.

DESIGN

Mixed-methods group concept mapping over 8 months using an online method. Participants provided strategies in response to a prompt about what was needed for successful daily ABCDEF bundle implementation. Responses were summarized into a set of unique statements and then rated on a 5-point scale on degree of necessity (essential) and degree to which currently used.

SETTING

Sixty-eight academic, community, and federal ICUs.

PARTICIPANTS

A total of 121 ICU professionals consisting of frontline and leadership professionals.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A final set of 76 strategies (reduced from 188 responses) were suggested: education (16 strategies), collaboration (15 strategies), processes and protocols (13 strategies), feedback (10 strategies), sedation/pain practices (nine strategies), education (eight strategies), and family (five strategies). Nine strategies were rated as very essential but infrequently used: adequate staffing, adequate mobility equipment, attention to (patient's) sleep, open discussion and collaborative problem solving, nonsedation methods to address ventilator dyssynchrony, specific expectations for night and day shifts, education of whole team on interdependent nature of the bundle, and effective sleep protocol.

CONCLUSIONS

In this concept mapping study, ICU professionals provided strategies that spanned a number of conceptual implementation clusters. Results can be used by ICU leaders for implementation planning to address context-specific interdisciplinary approaches to improve ABCDEF bundle implementation.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Testing of pandemic ventilators under early and agile development

Aiming to address clinical requirements subsequent to SARS-CoV-2-related pulmonary disease, multiple research groups and industry groups carried out intensive studies to develop pandemic ventilators (PDVs). In vitro testing to critically evaluate the specific performance of the developed apparatuses is an essential requirement. This study presents a test protocol which promotes a test-oriented, iterative, and agile assessment and consecutive development of such PDVs. It allows for fast identification of specific characteristics of each PDV in the individual test features. The test protocol includes an evaluation of the accuracy of control systems and instruments at changing parameters, the oxygen dynamics, and the response to trigger signals. The test environment is a mechanical lung, which allows reproducing various lung mechanics and to simulate active breathing cycles. A total of three PDVs that are under development were iteratively tested, with a Hamilton T1 as a reference. Continuous testing of the PDVs under development enables quick identification of critical application aspects that deserve further improved. Based on the present test protocol, the ventilators demonstrate a promising performance justifying continued development.

Lung Mechanics Over the Century: From Bench to Bedside and Back to Bench

Lung physiology research advanced significantly over the last 100 years. Respiratory mechanics applied to animal models of lung disease extended the knowledge of the workings of respiratory system. In human research, a better understanding of respiratory mechanics has contributed to development of mechanical ventilators. In this review, we explore the use of respiratory mechanics in basic science to investigate asthma and chronic obstructive pulmonary disease (COPD). We also discuss the use of lung mechanics in clinical care and its role on the development of modern mechanical ventilators. Additionally, we analyse some bench-developed technologies that are not in widespread use in the present but can become part of the clinical arsenal in the future. Finally, we explore some of the difficult questions that intensive care doctors still face when managing respiratory failure. Bringing back these questions to bench can help to solve them. Interaction between basic and translational science and human subject investigation can be very rewarding, as in the conceptualization of “Lung Protective Ventilation” principles. We expect this interaction to expand further generating new treatments and managing strategies for patients with respiratory disease.

Mechanosensitive Piezo1 channel activation promotes ventilator-induced lung injury via disruption of endothelial junctions in ARDS rats

OBJECTIVE

This study aimed to investigate the role of endothelial Piezo1 in mediating ventilator-induced lung injury secondary to acute respiratory distress syndrome (ARDS).

METHODS

Rats and lung endothelial cells (ECs) were transfected with Piezo1 shRNA (shPiezo1) and Piezo1 siRNA, respectively, to knock down Piezo1. Intratracheal instillation or incubation with lipopolysaccharide (LPS) was used to establish an ARDS model, and high tidal volume (HVT) ventilation or 20% cyclic stretch (CS) was administered to simulate a two-hit injury. Lung injury, alterations in lung endothelial barrier, disruption of adherens junctions (AJs), and Ca²⁺ influx were assessed.

RESULTS

Lung vascular hyperpermeability was further increased in ARDS rats following HVT ventilation, which was abrogated in shPiezo1-treated rats. 20% CS led to severer rupture of AJs following LPS stimulation as indicated by immunofluorescence staining. The internalization and degradation of VE-cadherin were blocked by knockdown of Piezo1. Additionally, 20% CS induced Piezo1 activation, manifesting as elevated intracellular Ca²⁺ concentration in LPS-treated ECs, and subsequently increased calcium-dependent calpain activity. Pharmacological inhibition of calpain or Piezo1 knockdown prevented the loss of VE-cadherin, p120-catenin, and β-catenin in ARDS rats undergoing HVT ventilation and LPS-treated ECs exposed to 20% CS.

CONCLUSION

Excessive mechanical stretch during ARDS induces the activation of Piezo1 channel and its downstream target, calpain, via Ca²⁺ influx. This results in the disassembly of endothelial AJs and further facilitates lung endothelial barrier breakdown and vascular hyperpermeability.