Clinical Application of Respiratory Elastance (CARE Trial) for Mechanically Ventilated Respiratory Failure Patients: A Model-based Study

Chiew, Yeong Shiong; Chase, J. Geoffrey; Arunachalam, Ganesa Ramachandran; Tan, Chee Pin; Loo, Nien Loong; Chiew, Yeong Woei; Ralib, Azrina; Nor, Mohd Basri Mat

doi:10.1016/j.ifacol.2018.11.641

Cited by 26 publications

(9 citation statements)

References 25 publications

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“…The asynchrony rate calculated using the filtering criteria is 19.9%, which agrees with manual inspection on a portion of this cohort. 15 Fluctuation, asynchronies, patient effort, and other causes of unreliable data disrupt the ability to identify accurate values of respiratory elastance and resistance. For example, for Patients 6, 11 and 12, almost half the identified breaths are removed due to unidentifiable readings or breaths with APE values far too high to be considered as unaffected breaths without asynchrony or patient effort.…”

Section: Discussionmentioning

confidence: 99%

“…This study used the airway pressure-flow data from 24 MV patients prospectively recruited at the International Islamic University Malaysia (IIUM) Medical Centre as part of an observational study under informed consent. 15 Ventilator data was recorded using the CURE soft system 59 connected to a Puritan Bennet PB980 ventilator (Covidien, Boulder, CO, USA). Airway pressure (cmH 2 O) and flow (L/min) are recorded at a sampling rate of 50 Hz.…”

Section: Methodsmentioning

confidence: 99%

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Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

et al. 2021

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While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, E rs , to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future E rs values using retrospective patient data to develop and validate a model indicating future intra-patient variability of E rs . Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future E rs values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

et al. 2021

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“…The data used in this study was obtained from 883 breathes aggregated from 7 mechanically ventilated patients admitted to the intensive care unit (ICU) between August 2017 and October 2017 at the IIUM Medical Centre, Malaysia with respiratory failure [15]. There were also 635 breathes from 5 MV patients of Christchurch Hospital, New Zealand ICU between April 2014 and November 2014 [16,17].…”

Section: Research Methods 21 Patient Data and Analysismentioning

confidence: 99%

Assessing SB effort via a non-invasive model-based method in mechanically ventilated patients in Malaysian ICU hospital

Damanhuri

Ariffin

Othman

et al. 2019

IJEECS

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Patients with Acute Respiratory Distress Syndrome (ARDS) required mechanical ventilation (MV) for breathing support. However, some MV patients encountered spontaneous breathing (SB) efforts while fully sedated which can obscure the true underlying respiratory mechanics of these patients. Thus, a model-based method is required to reconstruct the missing pressure and calculate the breathing effort that produced by the patients without additional clinical protocols or invasive procedure. In this paper, results of spontaneous breathing effort in Malaysian critically-ill patients adopting the developed pressure reconstruction model are presented. By using the pressure reconstruction model, the SB affected pressure waveform is reconstructed to approximate true respiratory mechanics and quantifies the SB effort. The SB breathing efforts were computed and compared with the results from Christchurch Hospital, New Zealand. The substitute measure of SB effort can be indicated from the difference between the reconstructed and unreconstructed pressure. Results shows that all patients from both cohorts exhibited SB effort with the highest SB effort at 11.48% for Malaysian patient and 21.07% for Christchurch patient. Overall, the well-developed non-invasive pressure reconstruction method is able to measure the SB effort produced by Malaysian MV patients that help the clinicians in selecting the optimal MV setting. This first non-invasive guidance in selecting the optimal setting of MV in Malaysia is potentially reduced the ICU cost and improve the MV management in Malaysian hospital.

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“…In this study, the CARENet system is tested using a retrospective mechanically ventilated patient dataset. The dataset consists of the airway pressure and flow data of 24 mechanically ventilated respiratory failure patients recruited in the intensive care unit of International Islamic University Malaysia Medical Centre (IIUMMC), Pahang, Malaysia from August 2017 to February 2020 [77]. The patients were ventilated using a Puritan Bennett PB980 ventilator (Medtronic, Dublin, Ireland).…”

Section: Carenet System Testing and Validation With Retrospective Datamentioning

confidence: 99%

“…This validation test approach mirrors real-time data acquisition and analysis for the system, as the data is given to the system via the DAQ module [36]. The patient data is from an observational trial (Ethics Approval: IRC666) of MV patients [77]. Overall, this approach provides a test of the main modules of CARENet to demonstrate and validate its functionality.…”

Section: A Validation Test and Datamentioning

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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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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Clinical Application of Respiratory Elastance (CARE Trial) for Mechanically Ventilated Respiratory Failure Patients: A Model-based Study

Cited by 26 publications

References 25 publications

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

Assessing SB effort via a non-invasive model-based method in mechanically ventilated patients in Malaysian ICU hospital

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

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