On the practical identifiability of a two-parameter model of pulmonary gas exchange

Riedlinger, Axel; Kretschmer, Jörn; Möller, Knut

doi:10.1186/s12938-015-0077-6

Cited by 4 publications

(2 citation statements)

References 30 publications

(29 reference statements)

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“…In addition, respiratory failure is a secondary outcome to many disease or injury states, complicating the application of MV in a broad clinical cohort. In short, for a model to have clinical relevance and widespread use it needs to be structured simply enough to ensure clinical relevance and be mathematically identifiable using information readily available at the bedside (Chase et al, 2018;Chis, Banga, & Balsa-Canto, 2011;Cobelli & DiStefano, 1980;Docherty, Chase, Lotz, & Desaive, 2011;Ljung, 1999;Riedlinger, Kretschmer, & Möller, 2015;Schranz, Docherty, Chiew, Chase, & Möller, 2012).…”

Section: Improved Dynamic Models and Methodsmentioning

confidence: 99%

Optimising mechanical ventilation through model-based methods and automation

Morton

Knopp

Chase

et al. 2019

Annual Reviews in Control

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Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload.Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

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Section: Improved Dynamic Models and Methodsmentioning

confidence: 99%

Optimising mechanical ventilation through model-based methods and automation

Morton

Knopp

Chase

et al. 2019

Annual Reviews in Control

Self Cite

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“…Finally, model-based MV research is not without challenge. It is often hindered by the need of specialised clinical protocols [ 38 , 210 – 213 ], results comparison and validation [ 62 , 214 , 215 ], data quality, and model identification [ 190 , 212 , 216 ]. Ultimately, the need for large randomised controlled trials is a further limitation.…”

Section: The Path Forward For Mechanical Ventilation and The Role Of mentioning

confidence: 99%

Biomedical engineer’s guide to the clinical aspects of intensive care mechanical ventilation

et al. 2018

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BackgroundMechanical ventilation is an essential therapy to support critically ill respiratory failure patients. Current standards of care consist of generalised approaches, such as the use of positive end expiratory pressure to inspired oxygen fraction (PEEP–FiO2) tables, which fail to account for the inter- and intra-patient variability between and within patients. The benefits of higher or lower tidal volume, PEEP, and other settings are highly debated and no consensus has been reached. Moreover, clinicians implicitly account for patient-specific factors such as disease condition and progression as they manually titrate ventilator settings. Hence, care is highly variable and potentially often non-optimal. These conditions create a situation that could benefit greatly from an engineered approach. The overall goal is a review of ventilation that is accessible to both clinicians and engineers, to bridge the divide between the two fields and enable collaboration to improve patient care and outcomes. This review does not take the form of a typical systematic review. Instead, it defines the standard terminology and introduces key clinical and biomedical measurements before introducing the key clinical studies and their influence in clinical practice which in turn flows into the needs and requirements around how biomedical engineering research can play a role in improving care. Given the significant clinical research to date and its impact on this complex area of care, this review thus provides a tutorial introduction around the review of the state of the art relevant to a biomedical engineering perspective.DiscussionThis review presents the significant clinical aspects and variables of ventilation management, the potential risks associated with suboptimal ventilation management, and a review of the major recent attempts to improve ventilation in the context of these variables. The unique aspect of this review is a focus on these key elements relevant to engineering new approaches. In particular, the need for ventilation strategies which consider, and directly account for, the significant differences in patient condition, disease etiology, and progression within patients is demonstrated with the subsequent requirement for optimal ventilation strategies to titrate for patient- and time-specific conditions.ConclusionEngineered, protective lung strategies that can directly account for and manage inter- and intra-patient variability thus offer great potential to improve both individual care, as well as cohort clinical outcomes.

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