Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

Major, Vincent J.; Corbett, Simon; Redmond, Daniel P.; Beatson, Alex; Glassenbury, Daniel; Chiew, Yeong Shiong; Pretty, Christopher G.; Desaive, Thomas; Szlávecz, Ákos; Benyó, Balázs; Shaw, Geoffrey M.; Chase, J. Geoffrey

doi:10.1016/j.bspc.2015.07.007

Cited by 28 publications

(10 citation statements)

References 16 publications

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“…5(a) shows Patient 18 was ventilated in a volume-controlled mode with a ramp flow profile and consistent tidal volume. However, as Patient 18 experienced some form of asynchronous breathing, their maximum airway pressure, PIP, decreases to -11 cmH2O when asynchronous reverse triggering [57], [80], [81] occurred at the end of the breath. This decrease affects the resulting model-identified respiratory mechanics, and thus, within an hour, a wider range of MV parameters are observed.…”

Section: B Validation Test Resultsmentioning

confidence: 99%

“…Thus, patient-specific condition will vary and evolve significantly over time, and the MV treatment should thus also be patient-specific and equally time-varying, as well [13], [84]. Moreover, a patient can also exhibit spontaneous breathing and asynchrony, which alters airway pressure and affects identification of model-based patient-specific lung mechanics parameters [80]. Thus, there is a need for a system to allow continuous monitoring and feedback to help guide MV treatment [84].…”

Section: Discussionmentioning

confidence: 99%

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Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Chiew

Wang

et al. 2021

IEEE Access

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The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patientspecific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective modelbased monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patientventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, realtime collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

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Section: B Validation Test Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Chiew

Wang

et al. 2021

IEEE Access

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“…Its disadvantage is the requirement for several breaths with unchanging ventilator settings which may not be available 4 during major MV pressure and flow profile changes during recruitment manoeuvres. The details of this method can be found in [20,27,28].…”

Section: Pressure Reconstruction By Combining Breathsmentioning

confidence: 99%

“…In recent studies, several models were developed to capture or reduce the effects of asynchronous efforts or spontaneous breathing on the airway pressure waveform, to improve respiratory mechanics monitoring. For example, Major et al [20] introduced a method by pooling airway pressure to reduce the asynchronous effects for better respiratory mechanics estimations. Redmond et al [21] proposed a polynomial model to capture patient efforts during volume controlled ventilation modes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Redmond

Chiew

Major

et al. 2019

Computer Methods and Programs in Biomedicine

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Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

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“…Our research interests covered a wider range of processes relevant to the behavior and treatment of the human body, including retina function identification [87], breath cycles [100], and glucose absorption [101].…”

Section: Modelling and Control Of Physiological Processesmentioning

confidence: 99%

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

Vajk¹,

Harsányi

Poppe

et al. 2016

Period. Polytech. Elec. Eng. Comp. Sci.

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PrefaceThroughout László Vajta, Dean of BME VIK János Levendovszky, Deputy Dean of BME VIK Department of Automation and Applied InformaticsDepartment of Automation and Applied Informatics (AUT) is one of the largest and dynamically developing departments at BME with diverse scope competences such as control theory, embedded systems, software modelling, applied software development, Internet of Things, power electronics, mechatronics, and many others. The department's main activities are education, research and development. Training versatile electrical and software engineers with solid practical knowledge is our top priority. Our profile also includes developing high quality software and hardware solutions for industry partners. All of our activities are backed by strong research background.In 2015, the department has organized the 21 st European Wireless (EW) Conference. The EW 2015 conference hosted more than 140 participants from 30 different countries. The 2015 edition of EW was aimed at addressing a key theme on "5G and beyond."The next sections summarize the key research results of the department in year 2015. IoT and Smart CityThe Internet of Things (IoT) is transforming the surrounding physical objects into an ecosystem of information that enriches our everyday life. The IoT represents the convergence of advances in miniaturization, wireless connectivity, and increased data storage, and is driven by various sensors. Application and service development methods and frameworks are required to support the realization of solutions covering data collection, transmission, data processing, analysis, reporting, and advanced querying. Our SensorHUB framework URBMOBI is a mobile environmental sensor that (i) provides temporally and spatially distributed environmental data, (ii) fulfills the need for monitoring at various places without the costs for a large number of fixed measurement stations, (iii) integrates small and precise sensors in a system that can be operated on buses, trams, or other vehicles, (iv) concentrates on urban heat and thermal comfort, and (v) aims at providing climate services and integration with real-time climate models.utilizes the state-of-the-art open source technologies and provides a unified tool chain for IoT related application and ser-URBMOBI project has been worked out between 2013 and 2015 by the following consortium: RWTH Aachen University (Germany), Netherlands Organization for Applied Scientific Research TNO (Netherlands), ARIA Technologies (France), Budapest University of Technology and Economics (Hungary), and Meteorological and Environmental Earth Observation (Italy).The SOLSUN (Sustainable Outdoor Lighting & Sensory Urban Networks) project is about to demonstrate how intelligent city infrastructure can be created in a cost-effective and sustainable way by reusing existing street lighting as the communications backbone. We apply different technologies and methods to reduce energy consumption at the same time as turning streetlights into nodes on a scalable network that is al...

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Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

Cited by 28 publications

References 16 publications

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

BME VIK Annual Research Report on Electrical Engineering and Computer Science 2015

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