Bedside monitoring of lung volume available for gas exchange

Trân, Minh; Crockett, Douglas C.; Cronin, John N.; Borges, João Batista; Hedenstierna, Göran; Larsson, Anders; Farmery, Andrew D.; Formenti, Federico

doi:10.1186/s40635-020-00364-6

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…The increasing interest in personalising ventilation is likely to lead to more complex automated assessments derived from the ventilator being integrated with non-invasive imaging at the bedside over time. New devices under development can aid the assessment of recruitability 165 and overdistension 166 at the bedside by measuring intratidal fluctuations in PaO 2 . Other studies have used electroencephalography to directly measure oscillatory neural activity in response to inspiratory loading or deliberate induction of asynchrony.…”

Section: Multimodal Monitoring During Vv Ecmomentioning

confidence: 99%

State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation

Collins,

Giosa,

Camporota

et al. 2023

Perfusion

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Monitoring the patient receiving veno-venous extracorporeal membrane oxygenation (VV ECMO) is challenging due to the complex physiological interplay between native and membrane lung. Understanding these interactions is essential to understand the utility and limitations of different approaches to respiratory monitoring during ECMO. We present a summary of the underlying physiology of native and membrane lung gas exchange and describe different tools for titrating and monitoring gas exchange during ECMO. However, the most important role of VV ECMO in severe respiratory failure is as a means of avoiding further ergotrauma. Although optimal respiratory management during ECMO has not been defined, over the last decade there have been advances in multimodal respiratory assessment which have the potential to guide care. We describe a combination of imaging, ventilator-derived or invasive lung mechanic assessments as a means to individualise management during ECMO.

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Section: Multimodal Monitoring During Vv Ecmomentioning

confidence: 99%

State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation

Collins,

Giosa,

Camporota

et al. 2023

Perfusion

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“…Although considered as a "gold standard" for lung volume assessment, it is important to highlight that CT scans accurately measures the "anatomical lung volume", in contrast with MBNW technique, or other gas-dilution technique, where the lung volume measured contributes to ventilation, i.e., "ventilable lung volume" [4]. Thus, in the presence of overinflated lung areas (at high PEEP levels) the "anatomical lung volume" is expected to be different from the "ventilable lung volume" [7,28]. The current study supports previous results, since the bias observed between the tested methods was independently related to the amount of overinflation at end-expiration, it may be hypothesized that overinflated lung areas did not participate to nitrogen mixing during washin-washout, which may explain EELV underestimation at high PEEP.…”

Section: Reliability and Trending Ability Of Eelv Mbnwmentioning

confidence: 99%

Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model

Bitker

Carvalho

Reidt

et al. 2021

ICMx

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Background Personalizing mechanical ventilation requires the development of reliable bedside monitoring techniques. The multiple-breaths nitrogen washin–washout (MBNW) technique is currently available to measure end-expiratory lung volume (EELVMBNW), but the precision of the technique may be poor, with percentage errors ranging from 28 to 57%. The primary aim of the study was to evaluate the reliability of a novel MBNW bedside system using fast mainstream sensors to assess EELV in an experimental acute respiratory distress syndrome (ARDS) model, using computed tomography (CT) as the gold standard. The secondary aims of the study were: (1) to evaluate trending ability of the novel system to assess EELV; (2) to evaluate the reliability of estimated alveolar recruitment induced by positive end-expiratory pressure (PEEP) changes computed from EELVMBNW, using CT as the gold standard. Results Seven pigs were studied in 6 experimental conditions: at baseline, after experimental ARDS and during a decremental PEEP trial at PEEP 16, 12, 6 and 2 cmH2O. EELV was computed at each PEEP step by both the MBNW technique (EELVMBNW) and CT (EELVCT). Repeatability was assessed by performing replicate measurements. Alveolar recruitment between two consecutive PEEP levels after lung injury was measured with CT (VrecCT), and computed from EELV measurements (VrecMBNW) as ΔEELV minus the product of ΔPEEP by static compliance. EELVMBNW and EELVCT were significantly correlated (R2 = 0.97). An acceptable non-constant bias between methods was identified, slightly decreasing toward more negative values as EELV increased. The conversion equation between EELVMBNW and EELVCT was: EELVMBNW = 0.92 × EELVCT + 36. The 95% prediction interval of the bias amounted to ± 86 mL and the percentage error between both methods amounted to 13.7%. The median least significant change between repeated measurements amounted to 8% [CI95%: 4–10%]. EELVMBNW adequately tracked EELVCT changes over time (concordance rate amounting to 100% [CI95%: 87%–100%] and angular bias amounting to − 2° ± 10°). VrecMBNW and VrecCT were significantly correlated (R2 = 0.92). A non-constant bias between methods was identified, slightly increasing toward more positive values as Vrec increased. Conclusions We report a new bedside MBNW technique that reliably assesses EELV in an experimental ARDS model with high precision and excellent trending ability.

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“…The lung is a very crucial organ of the human body. It is situated in the thorax region and primarily responsible for respiration [ 1 ]. All the cells in the body require oxygen to carry out various activities.…”

Section: Introductionmentioning

confidence: 99%

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia

Akila¹,

Imanov²,

Almezhghwi

2023

Diagnostics

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The world’s population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and evaluation. Although human experts are primarily tasked with the diagnosis of different medical conditions, artificial intelligence (AI)-assisted diagnoses have become considerably useful in recent times. One of the critical lung infections, which requires early diagnosis and subsequent treatment to reduce the mortality rate, is pneumonia. There are different methods for obtaining a pneumonia diagnosis; however, the adoption of chest X-rays is popular since it is non-invasive. The AI systems for a pneumonia diagnosis using chest X-rays are often built on supervised machine-learning (ML) models, which require labeled datasets for development. However, collecting labeled datasets is sometimes infeasible due to constraints such as human resources, cost, and time. As such, the problem that we address in this paper is the unsupervised classification of pneumonia using unsupervised ML models including the beta-variational convolutional autoencoder (β-VCAE) and other variants, such as convolutional autoencoders (CAE), denoising convolutional autoencoders (DCAE), and sparse convolutional autoencoders (SCAE). Namely, the pneumonia classification problem is cast into an anomaly detection to develop the aforementioned ML models. The experimental results show that pneumonia can be diagnosed with high recall, precision, f1-score, and f2-score using the proposed unsupervised models. In addition, we observe that the proposed models are competitive with the state-of-the-art models, which are trained on a labeled dataset.

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Bedside monitoring of lung volume available for gas exchange

Cited by 5 publications

References 46 publications

State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation

State of the art: Monitoring of the respiratory system during veno-venous extracorporeal membrane oxygenation

Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia

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