Virtual patients for mechanical ventilation in the intensive care unit

Zhou, Cong; Chase, J. Geoffrey; Knopp, Jennifer L.; Sun, Qianhui; Tawhai, Merryn H.; Möller, Knut; Heines, Serge J. H.; Bergmans, Dennis C. J. J.; Shaw, Geoffrey M.; Desaive, Thomas

doi:10.1016/j.cmpb.2020.105912

Cited by 51 publications

(43 citation statements)

References 69 publications

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“…While there exist more descriptive lung mechanics models, 47 , 60 , 69 the single compartment linear lung model was selected as it forms the basis for all other models, and is readily identifiable using linear and nonlinear basis functions while providing very good predictive accuracy. 16 , 47 , 65 Furthermore, the respiratory elastance and resistance identified using the model has shown to be clinically relevant and similar to those identified from other models.…”

Section: Discussionmentioning

confidence: 99%

“…However, E rs evolves significantly with time, patient condition, and ventilator settings, such as PEEP. 8 , 17 , 47 , 69 A better understanding of the variability of E rs between patients and over time is vital to enable selection of optimal patient-specific MV settings at any current time. Ideally, a deterministic model capturing the entirety of pulmonary mechanics would allow accurate prediction of future lung parameters, if it was accurate and able to be identified, neither of which has been demonstrated due identifiability and validation issues.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…In recent years, technology and wireless communication advances have contributed significantly to the potential of model-based methods in providing patient-specific mechanical ventilation treatment [18], [20]. A range of realtime tools have been developed to capture patient data [32]- [35].…”

Section: Related Workmentioning

confidence: 99%

“…2) Retrospective mode: This mode allows analysis of retrospective patient data. Virtual patients can be simulated in this mode for research purposes [10], [19], [20]. It also enables user training or testing.…”

Section: B Data Acquisition System and Data Storage Systemmentioning

confidence: 99%

“…Model-based methods are a growing means of personalising care [13]- [16]. While many models can successfully capture lung dynamics [17], very few can accurately predict the pulmonary response over time [10], [18]- [20]. In addition, these model-based works are performed retrospectively, and there is a need of a platform to enable real-time MV data monitoring, processing and to provide decision support information to the clinicians.…”

Section: Introductionmentioning

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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