Electronic cigarette exposure disrupts airway epithelial barrier function and exacerbates viral infection.

The use of electronic cigarettes (e-cigs), especially among teenagers, has reached alarming and epidemic levels, posing a significant threat to public health. However, the short- and long-term effects of vaping on the airway epithelial barrier are unclear. Airway epithelial cells are the forefront protectors from viruses and pathogens. They contain apical junctional complexes (AJCs), which include tight junctions (TJs) and adherens junctions (AJs) formed between adjacent cells. Previously, we reported respiratory syncytial virus (RSV) infection, the leading cause of acute lower respiratory infection-related hospitalization in children and high-risk adults, induces a "leaky airway" by disrupting the epithelial AJC structure and function. We hypothesized chemical components of e-cigs disrupt airway epithelial barrier and exacerbate RSV-induced airway barrier dysfunction. Using confluent human bronchial epithelial (16HBE) cells and well-differentiated normal human bronchial epithelial (NHBE) cells, we found that exposure to extract and aerosol e-cig nicotine caused a significant decrease in transepithelial electrical resistance (TEER) and the structure of the AJC even at noncytotoxic concentrations. Western blot analysis of 16HBE cells exposed to e-cig nicotine extract did not reveal significant changes in AJC proteins. Exposure to aerosolized e-cig cinnamon or menthol flavors also induced barrier disruption and aggravated nicotine-induced airway barrier dysfunction. Moreover, preexposure to nicotine aerosol increased RSV infection and the severity of RSV-induced airway barrier disruption. Our findings demonstrate that e-cig exposure disrupts the airway epithelial barrier and exacerbates RSV-induced damage. Knowledge gained from this study will provide awareness of adverse e-cig respiratory effects and positively impact the mitigation of e-cig epidemic.NEW & NOTEWORTHY Electronic cigarette (e-cig) use, especially in teens, is alarming and at epidemic proportions, threatening public health. Our study shows that e-cig nicotine exposure disrupts airway epithelial tight junctions and increases RSV-induced barrier dysfunction. Furthermore, exposure to aerosolized flavors exaggerates e-cig nicotine-induced airway barrier dysfunction. Our study confirms that individual and combined components of e-cigs deleteriously impact the airway barrier and that e-cig exposure increases susceptibility to viral infection.

Design and performance testing of a novel emergency ventilator for in-hospital use.

BACKGROUND: The rapidly evolving COVID-19 pandemic has led to increased use of critical care resources, particularly mechanical ventilators. Amidst growing concerns that the health care system could face a shortage of ventilators in the future, there is a need for an affordable, simple, easy to use, emergency stockpile ventilator. METHODS: Our team of engineers and clinicians designed and tested an emergency ventilator that uses a single limb portable ventilator circuit. The circuit is controlled by a pneumatic signal with electronic microcontroller input, using air and oxygen sources found in standard patient rooms. Ventilator performance was assessed using an IngMar ASL 5000 breathing simulator, and it was compared with a commercially available mechanical ventilator. RESULTS: The emergency ventilator provides volume control mode, intermittent mandatory ventilation and continuous positive airway pressure. It can generate tidal volumes between 300 and 800 mL with <10% error, with pressure, volume, and waveforms substantially equivalent to existing commercial ventilators. CONCLUSIONS: We describe a cost effective, safe, and easy to use ventilator that can be rapidly manufactured to address ventilator shortages in a pandemic setting. It meets basic clinical needs and can be provided for emergency use in cases requiring mechanical ventilation because of complications due to respiratory failure from infectious diseases.

Neural breathing pattern in newborn infants pre- and postextubation.

AIM: To describe the neural breathing pattern before and after extubation in newborn infants. METHODS: Prospective, observational study. In infants deemed ready for extubation, the diaphragm electrical activity (EAdi) was continuously recorded from 30 minute before to two hours after extubation. RESULTS: Total of 25 neonates underwent 29 extubations; 10 extubations resulted in re-intubation within 72 hours. Postextubation, there was an increase in peak EAdi (EAdi-max) and EAdi-delta (peak minus minimum EAdi) in both groups. The pre- to postextubation change in EAdi-max (8.9-11.1 µv) and EAdi-delta (6-8 µv) was less in the failure group in comparison with the change in EAdi-max (10.2-13.4 µv) and EAdi-delta (6.3-10.6 µv) in the success group, (p = 0.02 and 0.01, respectively). CONCLUSION: In our neonatal cohort, extubation failure was associated with a smaller increase in peak and delta EAdi after extubation. If confirmed, these findings indicate an important cause of extubation failure in preterm infants.

Prediction of inspired oxygen fraction for targeted arterial oxygen tension following open heart surgery in non-smoking and smoking patients.

Simple and accurate expressions describing the PaO2-FiO2 relationship in mechanically ventilated patients are lacking. The current study aims to validate a novel mathematical expression for accurate prediction of the fraction of inspired oxygen that will result in a targeted arterial oxygen tension in non-smoking and smoking patients receiving mechanical ventilation following open heart surgeries. One hundred PaO2-FiO2 data pairs were obtained from 25 non-smoking patients mechanically ventilated following open heart surgeries. One data pair was collected at each of FiO2 of 40, 60, 80, and 100% while maintaining same mechanical ventilation support settings. Similarly, another 100 hundred PaO2-FiO2 data pairs were obtained from 25 smoking patients mechanically ventilated following open heart surgeries. The utility of the new mathematical expression in accurately describing the PaO2-FiO2 relationship in these patients was assessed by the regression and Bland-Altman analyses. Significant correlations were seen between the true and estimated FiO2 values in non-smoking (r2 = 0.9424; p < 0.05) and smoking (r2 = 0.9466; p < 0.05) patients. Tight biases between the true and estimated FiO2 values for non-smoking (3.1%) and smoking (4.1%) patients were observed. Also, significant correlations were seen between the true and estimated PaO2/FiO2 ratios in non-smoking (r2 = 0.9530; p < 0.05) and smoking (r2 = 0.9675; p < 0.05) patients. Tight biases between the true and estimated PaO2/FiO2 ratios for non-smoking (-18 mmHg) and smoking (-16 mmHg) patients were also observed. The new mathematical expression for the description of the PaO2-FiO2 relationship is valid and accurate in non-smoking and smoking patients who are receiving mechanical ventilation for post cardiac surgery.

A Taxonomy for Noninvasive Modes Provided by Portable Ventilators.

The purpose of this article is to identify (by brand name) and then classify the modes available on contemporary portable ventilators used for noninvasive ventilation in the United States. We propose a formal taxonomy that identifies the modes by their control variable, breath sequence, and targeting scheme, therefore describing what the mode does. Use of this taxonomy should be helpful in finding modes with comparable functionality that cater to the specific goal of mechanical ventilation and effective ventilatory strategies for each disease state.

Effect of Ventilator Settings on Mechanical Power During Simulated Mechanical Ventilation of Patients With ARDS.

BACKGROUND: In recent years, mechanical power (MP) has emerged as an important concept that can significantly impact outcomes from mechanical ventilation. Several individual components of ventilatory support such as tidal volume (VT), breathing frequency, and PEEP have been shown to contribute to the extent of MP delivered from a mechanical ventilator to patients in respiratory distress/failure. The aim of this study was to identify which common individual setting of mechanical ventilation is more efficient in maintaining safe and protective levels of MP using different modes of ventilation in simulated subjects with ARDS. METHODS: We used an interactive mathematical model of ventilator output during volume control ventilation (VCV) with either constant inspiratory flow (VCV-CF) or descending ramp inspiratory flow, as well as pressure control ventilation (PCV). MP values were determined for simulated subjects with mild, moderate, and severe ARDS; and whenever MP > 17 J/min, VT, breathing frequency, or PEEP was manipulated independently to bring back MP to ≤ 17 J/min. Finally, the optimum VT-breathing frequency combinations for MP = 17 J/min were determined with all 3 modes of ventilation. RESULTS: VCV-CF always resulted in the lowest MPs while PCV resulted in highest MPs. Reductions in VT were the most efficient for maintaining safer and protective MP. At targeted MPs of 17 J/min and maximized minute ventilation, the optimum VT-breathing frequency combinations were 250-350 mL for VT and 32-35 breaths/min for breathing frequency in mild ARDS, 200-350 mL for VT and 34-40 breaths/min for breathing frequency in moderate ARDS, and 200-300 mL for VT and 37-45 breaths/min for breathing frequency for severe ARDS. CONCLUSIONS: VCV-CF resulted in the lowest MP. VT was the most efficient for maintaining safe and protective MP in a mathematical simulation of subjects with ARDS. In the context of maintaining low and safe MPs, ventilatory strategies with lower-than-normal VT and higher-than-normal breathing frequency will need to be implemented in patients with ARDS.

The Complexities of Mechanical Ventilation: Toppling the Tower of Babel.

The exponential increase in the complexity of ventilator technology has created a growing knowledge gap that hinders education, research, and ultimately the quality of patient care. This gap is best addressed with a standardized approach to educating clinicians, just as education for basic and advanced life support classes is standardized. We have developed such a program, called Standardized Education for Ventilatory Assistance (SEVA), based on a formal taxonomy for modes of mechanical ventilation. The SEVA program is a progressive system of 6 sequential courses starting from an assumption of no prior knowledge and proceeding to full mastery of advanced techniques. The vision of the program is to provide a unique platform for standardizing training by unifying the concepts of physics, physiology, and technology of mechanical ventilation. The mission is to use both online and in-person simulation-based instruction that has both self-directed and instructor-led components to elevate the skills of health care providers to the mastery level. The first 3 levels of SEVA are free and open to the public. We are developing mechanisms to offer the other levels. Spinoffs of the SEVA program include a free smartphone app that classifies virtually all modes on all ventilators used in the United States (Ventilator Mode Map), a free biweekly online training sessions focusing on waveform interpretation (SEVA-VentRounds), and modifications to the electronic health care record system for entering and charting ventilator orders.

Automatic Apnea Time Adjustments During Ventilation With Automode.

BACKGROUND: Automode is a feature on Servo ventilators that automatically switches between mandatory and spontaneous breaths. Spontaneous breaths suppress mandatory breaths until apnea. The period from the last spontaneous breath to the first mandatory breath is automatically adjusted by a calculated apnea time limit based on a maximum apnea time setting, the mandatory breathing frequency setting, and the spontaneous breath count. The purpose of this study was to validate the apnea time algorithm by using simulated mechanical ventilation. METHODS: A Servo-u ventilator was connected to an ASL 5000 breathing simulator. Ventilator settings were the following: Automode (pressure control to pressure support), pressure control = 10 cm H2O; pressure support = 5 cm H2O; PEEP = 10 cm H2O; breathing frequency = 10, 12, 15, 20 breaths/min; maximum apnea time = 7 and 12 s. Simulator settings were the following: resistance = 10 cm H2O/L/s; compliance = 35 mL/cm H2O; flow trigger model: frequency = 20 breaths/min, trigger flow = 10 L/min, trigger duration = 800 ms. Flow waveforms were recorded, and the observed apnea time limit was compared with the calculated value. The outcome variable was error, defined as the difference between observed and calculated apnea times expressed as a percentage. RESULTS: The observed apnea time limit ranged from 3 to 12 s, depending on the mandatory frequency and the spontaneous breath count. The average error ranged from -2 to 0%. CONCLUSIONS: The measured apnea time for simulated ventilation settings was within 2% of calculated times. Automode allowed a spontaneous frequency lower than expected based on the mandatory frequency.

The Evolution of Intermittent Mandatory Ventilation.

Intermittent mandatory ventilation (IMV) is one kind of breath sequence used to classify a mode of ventilation. IMV is defined as the ability for spontaneous breaths (patient triggered and patient cycled) to exist between mandatory breaths (machine triggered or machine cycled). Over the course of more than a century, IMV has evolved into 4 distinct varieties, each with its own advantages and disadvantages in serving the goals of mechanical ventilation (ie, safety, comfort, and liberation). The purpose of this paper is to describe the evolution of IMV, review relevant supporting evidence, and discuss the rationales for each of the 4 varieties. Also included is a brief overview of the background information required for a proper perspective of the purpose and design of the innovations. Understanding these different forms of IMV is essential to recognizing the similarities and differences among many dozens of different modes of ventilation. This recognition is important for clinical application, education of caregivers, and research in mechanical ventilation.

Bench Assessment of Work of Breathing During a Spontaneous Breathing Trial on Zero Pressure Support and Zero PEEP Compared to T-Piece.

BACKGROUND: Analysis of observational data suggests that both a T-piece and zero pressure support ventilation (PSV) and zero PEEP impose work of breathing (WOB) during a spontaneous breathing trial (SBT) similar to what a patient experiences after extubation. The aim of our study was to compare the WOB imposed by the T-piece with zero PSV and zero PEEP. We also compared the difference in WOB when using zero PSV and zero PEEP on 3 different ventilators. METHODS: This study was conducted by using a breathing simulator that simulated 3 lung models (ie, normal, moderate ARDS, and COPD). Three ventilators were used and set to zero PSV and zero PEEP. The outcome variable was WOB expressed as mJ/L of tidal volume. RESULTS: An analysis of variance showed that WOB was statistically different between the T-piece and zero PSV and zero PEEP on all the ventilators (Servo-i, Servo-u, and Carescape R860). The absolute difference was lowest for the Carescape R860, which increased WOB by 5-6%, whereas the highest for Servo-u, which reduced the WOB by 15-21%. CONCLUSIONS: Work may be imposed or reduced during spontaneous breathing on zero PSV and zero PEEP when compared to T-piece. The unpredictable nature of how zero PSV and zero PEEP behaves on different ventilators makes it an imprecise SBT modality in the context of assessing extubation readiness.

Esophageal Pressure Measurement: A Primer.

Over the last decade, the literature exploring clinical applications for esophageal manometry in critically ill patients has increased. New mechanical ventilators and bedside monitors allow measurement of esophageal pressures easily at the bedside. The bedside clinician can now evaluate the magnitude and timing of esophageal pressure swings to evaluate respiratory muscle activity and transpulmonary pressures. The respiratory therapist has all the tools to perform these measurements to optimize mechanical ventilation delivery. However, as with any measurement, technique, fidelity, and accuracy are paramount. This primer highlights key knowledge necessary to perform measurements and highlights areas of both uncertainty and ongoing development.

Bedside Assessment of Lung Recruitability: Making Sense of the Recruitment-to-Inflation Ratio.

Determination of optimum PEEP levels remains an elusive goal. One factor is the recruitability of the lung, yet this is another difficult determination. Recently, a simple bedside technique, called the recruitment-to-inflation ratio, has been described and validated by comparison to the dual pressure-volume curve method. We describe the prior research and concepts of lung mechanics leading up to this metric and develop some background mathematics that help clinicians understand its meaning.

Physiologic Markers of Disease Severity in ARDS.

Despite its significant limitations, the PaO2 /FIO2 remains the standard tool to classify disease severity in ARDS. Treatment decisions and research enrollment have depended on this parameter for over 50 years. In addition, several variables have been studied over the past few decades, incorporating other physiologic considerations such as ventilation efficiency, lung mechanics, and right-ventricular performance. This review describes the strengths and limitations of all relevant parameters, with the goal of helping us better understand disease severity and possible future treatment targets.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

BACKGROUND: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing. OBJECTIVE: We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. METHODS: We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). RESULTS: We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. CONCLUSION: The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

Tidal volume variability during airway pressure release ventilation: case summary and theoretical analysis.

Airway pressure-release ventilation (APRV) is used in the management of patients with severe or refractory respiratory failure. In addition to reversal of inspiratory-expiratory ratios, this pressure control mode also allows unrestricted spontaneous breathing. The spontaneous tidal volume (V(T)), as well as the V(T) resulting from transition between the high and low airway pressures, is uncontrolled. There are limited data on the within-patient variation of actual V(T) and the safety of these modes. The authors present a patient with severe ARDS who was managed with biphasic modes (APRV and bi-level positive airway pressure). Serial V(T) measurements showed that V(T) ranged from 4 to 12 mL/kg predicted body weight. Computed tomography scan images and chest radiographs obtained before and following APRV showed lung parenchyma changes that may be related to ventilator-induced lung injury. We also present a mathematical model that is useful for simulating APRV and demonstrating the issues related to volume delivery for mandatory breaths during the transition between the 2 pressure levels. A key finding of this analysis is the interdependence of release volume, autoPEEP, and the T(low) time setting. Furthermore, it is virtually impossible to target a specific P(aCO(2)) with a desired level V(T) and autoPEEP in a passive model, emphasizing the importance of spontaneous breathing with this mode. This case report suggests caution when using these modes, and that end-inspiratory lung volumes and V(T) should be limited to avoid lung injury. The important point of this case study and model analysis is that the application of APRV is more complex than it appears to be. It requires a lot more knowledge and skill than may be apparent from descriptions in the literature.

Airway pressure release ventilation: what do we know?

Airway pressure release ventilation (APRV) is inverse ratio, pressure controlled, intermittent mandatory ventilation with unrestricted spontaneous breathing. It is based on the principle of open lung approach. It has many purported advantages over conventional ventilation, including alveolar recruitment, improved oxygenation, preservation of spontaneous breathing, improved hemodynamics, and potential lung-protective effects. It has many claimed disadvantages related to risks of volutrauma, increased work of breathing, and increased energy expenditure related to spontaneous breathing. APRV is used mainly as a rescue therapy for the difficult to oxygenate patients with acute respiratory distress syndrome (ARDS). There is confusion regarding this mode of ventilation, due to the different terminology used in the literature. APRV settings include the "P high," "T high," "P low," and "T low". Physicians and respiratory therapists should be aware of the different ways and the rationales for setting these variables on the ventilators. Also, they should be familiar with the differences between APRV, biphasic positive airway pressure (BIPAP), and other conventional and nonconventional modes of ventilation. There is no solid proof that APRV improves mortality; however, there are ongoing studies that may reveal further information about this mode of ventilation. This paper reviews the different methods proposed for APRV settings, and summarizes the different studies comparing APRV and BIPAP, and the potential benefits and pitfalls for APRV.

The effect of targeting scheme on tidal volume delivery during volume control mechanical ventilation.

BACKGROUND: Technological advances have increased ventilator mode complexity and risk of operator error. OBJECTIVE: To compare differences in volume control (VC) ventilation with set-point and dual targeting. Two hypotheses were tested: tidal volume (V(T)) delivery is different with VC using set-point versus dual targeting during active versus passive breathing; VC with dual targeting delivers V(T) similar to pressure support ventilation (PS) with active breathing. METHODS: The Ingmar Medical ASL 5000 lung model simulated pulmonary mechanics of an adult patient with ARDS during active and passive ventilation. Resistance was standardized at 10 cm H(2)O/L/s and compliance at 32 mL/cm H(2)O. Active breathing was simulated by setting the frequency (f) = 26 breaths/min, and adjusting the muscle pressure (P(mus)) to produce a V(T) of 384 mL. VC was initiated with the Puritan Bennett 840 (set-point targeting) and the Servo-i (dual targeting) at V(T) = 430 mL, mandatory f = 15 breaths/min, and PEEP = 10 cm H(2)O. During PS, cycle threshold was set to 30% and peak inspiratory pressure adjusted to produce a V(T) similar to that delivered during VC. Expiratory V(T) was collected on 10 consecutive breaths during active and passive breathing with VC and PS. Mean V(T) differences (active vs passive model) were compared using analysis of variance. Statistical significance was established at P < .05. RESULTS: The mean ± SD V(T) difference varied with targeting schemes: VC set-point = 37.3 ± 3.5 mL, VC-dual = 77.1 ± 3.3 mL, and PS = 406.1 ± 1.5 mL (P < .001). Auto-triggering occurred during VC set-point with the active model. CONCLUSIONS: Dual targeting during VC allows increased V(T), compared to set-point, but not as much as PS.

Determining the basis for a taxonomy of mechanical ventilation.

BACKGROUND: Mechanical ventilation technology has evolved rapidly over the last 30 years. One consequence is the creation of an unmanageable number of names to describe modes of ventilation. The proliferation of names makes education of end users difficult, potentially compromising the quality of patient care. OBJECTIVE: To determine if stakeholders are familiar enough with published constructs related to modes of mechanical ventilation to form a basis for a consensus, by surveying the medical, education, and business communities. The hypotheses tested were: there is concordance (> 50%) on 10 basic constructs related to modes; concordance with the basic constructs varies among stakeholders according to professional training and professional activity; and concordance varies among the set of constructs. METHODS: The survey was distributed through an Internet-based tool to 2,994 physicians, respiratory therapists, nurses, engineers, and others involved with mechanical ventilation. Hypotheses were tested with chi-square, with P < .05 considered significant. RESULTS: The response rate was 15%. Respondents were 55% respiratory therapists, 35% physicians, 3% nurses, 1% engineers, and 5% other professionals. There was an 82% concordance with the 10 constructs (P < .001). Respiratory therapists showed the highest degree of concordance (84%) and "other profession" showed the lowest (79%) (P = .006). No significant difference (P = .07) in concordance was observed when data were grouped by professional activity. Concordance differed significantly among the survey questions (P < .001). CONCLUSIONS: Survey results indicate that respondents were either familiar with or amenable to the previously published literature that the survey constructs represented. The degree of familiarity and concordance with these constructs represents a sufficient basis for attempting to formalize a taxonomy. Further analysis of the pattern of concordance among the constructs will inform future educational and consensus building efforts.

The Effect of Inspiratory Effort on Circuit Compensation for Volume-Targeted Modes.

BACKGROUND: Critical-care ventilators provide patient circuit compensation (CC) to counteract the loss of volume due to patient circuit compliance. No studies show the effect of inspiratory efforts (indicating maximal value of the muscle pressure waveforms [Pmax]) on CC function. The goal of this study was to determine how Pmax affects volume delivery with or without CC for both volume control continuous mandatory ventilation with set-point targeting scheme (VC-CMVs) and pressure control continuous mandatory ventilation with adaptive targeting scheme (PC-CMVa) modes on the Servo-u ventilator. METHODS: A breathing simulator was programmed to represent an adult with moderate ARDS with different Pmax. It was connected to a ventilator set to VC-CMVs or PC-CMVa. The change in tidal volume (ΔVT) was defined as the difference between VT with CC on versus off. VT error was defined as the difference between the simulator displayed VT and the set VT with CC on versus off. RESULTS: For both VC-CMVs and PC-CMVa modes, ΔVT decreased as Pmax increased. The VT error decreased as Pmax increas-ed for VC-CMVs. In contrast, VT error increased on PC-CMVa mode as Pmax increased and peaked 39.0% for Pmax = 15 cm H2O. For both modes, the difference in VT errors for CC on versus CC off decreased as Pmax increased. CONCLUSIONS: CC corrected the delivered VT for volume lost due to compression in the patient circuit as expected. This compensation volume decreases as airway pressure drops due to patient Pmax.