Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation

Sun, Qianhui; Zhou, Cong; Chase, J. Geoffrey

doi:10.1016/j.bspc.2020.102003

Cited by 15 publications

(6 citation statements)

References 41 publications

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“…40 In contrast with this, the potential for an inappropriately high PEEP to paradoxically lead to decreased oxygen delivery to tissues has also been elucidated using modeling. 41 Detailed models have also been developed that can predict mechanical responses to altered PEEP, 42 and a desktop application named CURE Soft has been developed which can be used for real-time optimization of MV (in particular PEEP). 43 A single center RCT comparing the use of this system with the standard therapy is currently underway.…”

Section: Acute Respiratory Distress Syndromementioning

confidence: 99%

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Hannon

Mistry

Das

et al. 2022

Semin Respir Crit Care Med

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Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to “look inside” the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

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Section: Acute Respiratory Distress Syndromementioning

confidence: 99%

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Hannon

Mistry

Das

et al. 2022

Semin Respir Crit Care Med

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show abstract

“…However, while effective, this basis function model approach lacks precision in fully capturing nonlinear lung mechanics, such as the added lung volume obtained when changing PEEP. Combining the single compartment lung model and the hysteresis loop analysis (HLA) method predicted airway pressure, but required parameters identified from multiple prior low PEEP steps [39] , which is not clinically effective.…”

Section: Abbreviationsmentioning

confidence: 99%

Virtual patients for mechanical ventilation in the intensive care unit

Zhou

Chase

Knopp

et al. 2021

Computer Methods and Programs in Biomedicine

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“…Research based on PV loop has shown its promising utilization for modelling, identification and prediction of fully ventilated breath cycles [ 47 , 48 , 50 , 59 ]. More specifically, Zhou et al developed a nonlinear hysteretic lung mechanics model (HLM) combined to HLA to replicate the fully ventilated respiratory response without considering asynchrony [ 59 ].…”

Section: Introductionmentioning

confidence: 99%

Automated evaluation of typical patient–ventilator asynchronies based on lung hysteretic responses

Chen,

Zhang,

Zhou

et al. 2023

BioMed Eng OnLine

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Background Patient–ventilator asynchrony is common during mechanical ventilation (MV) in intensive care unit (ICU), leading to worse MV care outcome. Identification of asynchrony is critical for optimizing MV settings to reduce or eliminate asynchrony, whilst current clinical visual inspection of all typical types of asynchronous breaths is difficult and inefficient. Patient asynchronies create a unique pattern of distortions in hysteresis respiratory behaviours presented in pressure–volume (PV) loop. Methods Identification method based on hysteretic lung mechanics and hysteresis loop analysis is proposed to delineate the resulted changes of lung mechanics in PV loop during asynchronous breathing, offering detection of both its incidence and 7 major types. Performance is tested against clinical patient data with comparison to visual inspection conducted by clinical doctors. Results The identification sensitivity and specificity of 11 patients with 500 breaths for each patient are above 89.5% and 96.8% for all 7 types, respectively. The average sensitivity and specificity across all cases are 94.6% and 99.3%, indicating a very good accuracy. The comparison of statistical analysis between identification and human inspection yields the essential same clinical judgement on patient asynchrony status for each patient, potentially leading to the same clinical decision for setting adjustment. Conclusions The overall results validate the accuracy and robustness of the identification method for a bedside monitoring, as well as its ability to provide a quantified metric for clinical decision of ventilator setting. Hence, the method shows its potential to assist a more consistent and objective assessment of asynchrony without undermining the efficacy of the current clinical practice.

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Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation

Cited by 15 publications

References 41 publications

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Modeling Mechanical Ventilation In Silico—Potential and Pitfalls

Virtual patients for mechanical ventilation in the intensive care unit

Automated evaluation of typical patient–ventilator asynchronies based on lung hysteretic responses

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