Respiratory Mechanics

Design of an Electronic Nose System with Automatic End-Tidal Breath Gas Collection for Enhanced Breath Detection Performance

End-tidal breath gases originate deep within the lungs, and their composition is an especially accurate reflection of the body's metabolism and health status. Therefore, accurate collection of end-tidal breath gases is crucial to enhance electronic noses' performance in breath detection. Regarding this issue, this study proposes a novel electronic nose system and employs a threshold control method based on exhaled gas flow characteristics to design a gas collection module. The module monitors real-time gas flow with a flow meter and integrates solenoid valves to regulate the gas path, enabling automatic collection of end-tidal breath gas. In this way, the design reduces dead space gas contamination and the impact of individual breathing pattern differences. The sensor array is designed to detect the collected gas, and the response chamber is optimized to improve the detection stability. At the same time, the control module realizes automation of the experiment process, including control of the gas path state, signal transmission, and data storage. Finally, the system is used for breath detection. We employ classical machine learning algorithms to classify breath samples from different health conditions with a classification accuracy of more than 90%, which is better than the accuracy achieved in other studies of this type. This is due to the improved quality of the gas we extracted, demonstrating the superiority of our proposed electronic nose system.

Prognostic Significance of Driving Pressure for Initiation and Maintenance of ECMO in Patients with Severe ARDS: A Systematic Review and Meta-analysis

Introduction

In life-threatening conditions like severe acute respiratory distress syndrome (ARDS), rescue interventions like extracorporeal membrane oxygenation (ECMO) should be initiated urgently to resolve an otherwise potentially adverse clinical outcome. Driving pressure (DP) is an independent prognosticator of the survival of ARDS during mechanical ventilation. We conducted this review with the objective to identify the optimal DP for initiating ECMO in severe ARDS and to study the change in DP during ECMO strategy in survivors and non-survivors.

Materials and methods

A systematic search of EMBASE, PubMed, Cochrane Library, and SCOPUS databases was conducted from their inception to January 2024. Two investigators independently carried out the processes of literature search, study selection, data extraction, and quality assessment. The analysis was conducted using comprehensive meta-analysis software (CMA).

Results

For meta-analysis, six studies comprising 668 patients were included. In survivors, the DP at ECMO initiation was lower (mean DP = 14.56 cm H2O, 95% CI: [11.060-18.060]) than non-survivors (mean DP = 17.77 cm H2O, 95% CI: [12.935-22.607]). During ECMO, the survivors had lower DP (mean DP = 11.63 cm H2O, 95% CI: [10.070-13.195]) than non-survivors (mean DP = 14.67 cm H2O, 95% CI: [12.810-15.831]).

Conclusion

The optimum DP to initiate ECMO in severe ARDS patients on MV is 15 cm H2O. Extracorporeal membrane oxygenation reduces the intensity of MV, as reflected by a reduction in DP in both survivors and non-survivors during the ECMO by 3 cm H2O. The DP ≤ 12 cm H2O during ECMO strategy is a predictor of survival, and DP persisting ≥ 15 cm H2O on ECMO prompts the search for strategies to reduce DP.

Trial Registration

PROSPERO CRD42022327846.

How to cite this article

Todur P, Nileshwar A, Chaudhuri S, Nagendra D, Shanbhag V, Vennila J. Prognostic Significance of Driving Pressure for Initiation and Maintenance of ECMO in Patients with Severe ARDS: A Systematic Review and Meta-analysis. Indian J Crit Care Med 2025;29(2):177-185.

Work of Breathing for Aviators: A Missing Link in Human Performance

In this study, we explore the work of breathing (WoB) experienced by aviators during the Anti-G Straining Maneuver (AGSM) to improve pilot safety and performance. Traditional airflow models of WoB fail to adequately distinguish between breathing rate and inspiratory frequency, leading to potentially inaccurate assessments. This mismatch can have serious implications, particularly in critical flight situations where understanding the true respiratory workload is essential for maintaining performance. To address these limitations, we used a non-sinusoidal model that captures the complexities of WoB under high inspiratory frequencies and varying dead space conditions. Our findings indicate that the classical airflow model tends to underestimate WoB, particularly at elevated inspiratory frequencies ranging from 0.5 to 2 Hz, where resistive forces play a significant role and elastic forces become negligible. Additionally, we show that an increase in dead space, coupled with high-frequency breathing, elevates WoB, heightening the risk of dyspnea among pilots. Interestingly, our analysis reveals that higher breathing rates lead to a decrease in total WoB, an unexpected finding suggesting that refining breathing patterns could help pilots optimize their energy expenditure. This research highlights the importance of examining the relationship between alveolar ventilation, breathing rate, and inspiratory frequency in greater depth within realistic flight scenarios. These insights indicate the need for targeted training programs and adaptive life-support systems to better equip pilots for managing respiratory challenges in high-stress situations. Ultimately, our research lays the groundwork for enhancing respiratory support for aviators, contributing to safer and more efficient flight operations.

The Impact of Pneumoperitoneum on Mean Expiratory Flow Rate: Observational Insights from Patients with Healthy Lungs

BACKGROUND/OBJECTIVES

Surgical pneumoperitoneum (PP) significantly impacts volume-controlled ventilation, characterized by reduced respiratory compliance, elevated peak inspiratory pressure, and an accelerated expiratory phase due to an earlier onset of the airway pressure gradient. We hypothesized that this would shorten expiratory time, potentially increasing expiratory flow rate compared to pneumoperitoneum conditions. Calculations were performed to establish correlations between respiratory parameters and the mean increase in expiratory flow rate relative to baseline.

METHODS

Mechanical ventilation parameters were recorded for 67 patients both pre- and post-PP. Ventilator settings were standardized with a tidal volume of 6 mL/kg, a respiratory rate of 12 breaths per minute, a PEEP of 3 cmH2O, an inspiratory time of 2 s, and an inspiratory-to-expiratory ratio of 1:1.5 (I:E).

RESULTS

The application of PP increased both peak inspiratory pressure and mean expiratory flow rate by 28% compared to baseline levels. The elevated intra-abdominal pressure of 20 cmH2O resulted in a 34% reduction in dynamic chest compliance, a 50% increase in elastance, and a 20% increase in airway resistance. The mean expiratory flow rate increments relative to baseline showed a significant negative correlation with elastance (p = 0.0119) and a positive correlation with dynamic compliance (p = 0.0028) and resistance (p = 0.0240).

CONCLUSIONS

A PP of 20 cmH2O resulted in an increase in the mean expiratory flow rate in the conventional I:E ratio in the volume-ventilated mode. PP reduces lung and chest wall compliance by elevating the diaphragm, compressing the thoracic cavity, and increasing airway pressures. Consequently, the lungs and chest wall stiffen, requiring greater ventilatory effort and accelerating expiratory flow due to increased airway resistance and altered pulmonary mechanics. Prolonging the inspiratory phase through I:E ratio adjustment helps maintain peak inspiratory pressures closer to baseline levels, and this method enhances the safety and efficacy of mechanical ventilation in maintaining optimal respiratory function during laparoscopic surgery.

Pathophysiology and Prevention of Manual-Ventilation-Induced Lung Injury (MVILI)

Manual ventilation, most commonly with a bag-valve mask, is a form of short-term ventilation used during resuscitative efforts in emergent and out-of-hospital scenarios. However, compared to mechanical ventilation, manual ventilation is an operator-dependent skill that is less well controlled and is highly subject to providing inappropriate ventilation to the patient. This article first reviews recent manual ventilation guidelines set forth by the American Heart Association and European Resuscitation Council for providing appropriate manual ventilation parameters (e.g., tidal volume and respiratory rate) in different patient populations in the setting of cardiopulmonary resuscitation. There is then a brief review of clinical and manikin-based studies that demonstrate healthcare providers routinely hyperventilate patients during manual ventilation, particularly in emergent scenarios. A discussion of the possible mechanisms of injury that can occur during inappropriate manual hyperventilation follows, including adverse hemodynamic alterations and lung injury such as acute barotrauma, gastric regurgitation and aspiration, and the possibility of a subacute, inflammatory-driven lung injury. Together, these injurious processes are described as manual-ventilation-induced lung injury (MVILI). This review concludes with a discussion that highlights recent progress in techniques and technologies for minimizing manual hyperventilation and MVILI, with a particular emphasis on tidal-volume feedback devices.

Driving pressure in mechanical ventilation: A review

Driving pressure (∆P) is a core therapeutic component of mechanical ventilation (MV). Varying levels of ∆P have been employed during MV depending on the type of underlying pathology and severity of injury. However, ∆P levels have also been shown to closely impact hard endpoints such as mortality. Considering this, conducting an in-depth review of ∆P as a unique, outcome-impacting therapeutic modality is extremely important. There is a need to understand the subtleties involved in making sure ∆P levels are optimized to enhance outcomes and minimize harm. We performed this narrative review to further explore the various uses of ∆P, the different parameters that can affect its use, and how outcomes vary in different patient populations at different pressure levels. To better utilize ∆P in MV-requiring patients, additional large-scale clinical studies are needed.

Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance

BACKGROUND AND OBJECTIVE

Traditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response.

METHODS

We revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github.com/comp-medicine-uc/ML-lung-mechanics-UQ RESULTS: The low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability.

CONCLUSIONS

Our findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.

A cluster of neuropeptide S neurons regulates breathing and arousal

Neuropeptide S (NPS) is a highly conserved peptide found in all tetrapods that functions in the brain to promote heightened arousal; however, the subpopulations mediating these phenomena remain unknown. We generated mice expressing Cre recombinase from the Nps gene locus (NpsCre) and examined populations of NPS+ neurons in the lateral parabrachial area (LPBA), the peri-locus coeruleus (peri-LC) region of the pons, and the dorsomedial thalamus (DMT). We performed brain-wide mapping of input and output regions of NPS+ clusters and characterized expression patterns of the NPS receptor 1 (NPSR1). While the activity of all three NPS+ subpopulations tracked with vigilance state, only NPS+ neurons of the LPBA exhibited both increased activity prior to wakefulness and decreased activity during REM sleep, similar to the behavioral phenotype observed upon NPSR1 activation. Accordingly, we found that activation of the LPBA but not the peri-LC NPS+ neurons increased wake and reduced REM sleep. Furthermore, given the extended role of the LPBA in respiration and the link between behavioral arousal and breathing rate, we demonstrated that the LPBA but not the peri-LC NPS+ neuronal activation increased respiratory rate. Together, our data suggest that NPS+ neurons of the LPBA represent an unexplored subpopulation regulating breathing, and they are sufficient to recapitulate the sleep/wake phenotypes observed with broad NPS system activation.

An enhanced respiratory mechanics model based on double-exponential and fractional calculus

We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.

Full Assessment of Lung Mechanics Using Computer-Controlled, Forced Oscillation Technique

The forced oscillation technique (FOT) is a powerful and accurate method to quantify the mechanical properties of the airways and tissues of the respiratory system. Here we provide a detailed protocol for the measurement of mouse respiratory mechanical parameters. We present a procedure for mouse endotracheal intubation using a handcrafted intubation platform and confirmation module. The FlexiVentFX™ system (Scireq Inc.) is utilized for the thorough assessment of lung function with the FlexiWare™ software serving as a unit for the planning, experimentation, and analysis. The protocol has been standardized and adapted for use by our center for lung-function phenotyping of mouse models generated for the International Mouse Phenotyping Consortium (IMPC). The simplified steps, technical considerations, and integrated hardware-software demonstration make this protocol adaptable and implementable for researchers interested in using FOT for lung-function evaluation. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Support Protocol 1: Assembly of the FlexiVentFX™ system for measurements Support Protocol 2: FlexiWare database management Support Protocol 3: A guide for the construction of intubation platform and confirmation module Basic Protocol 1: Mouse endotracheal intubation Basic Protocol 2: Assessment of mouse basal lung function.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.