Iterative integral parameter identification of a respiratory mechanics model

Schranz, Christoph; Docherty, Paul; Chiew, Yeong Shiong; Möller, Knut; Chase, J. Geoffrey

doi:10.1186/1475-925x-11-38

Cited by 33 publications

(11 citation statements)

References 33 publications

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“…14,18,20,21 The integral method used to determine the value of the 3 insulin secretion rates U [1][2][3] has similarly been widely employed. 14,23,26,28 The evaluation methods used have seen widespread use, and are widely accepted means of validation.…”

Section: Discussionmentioning

confidence: 99%

Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay

Davidson

Docherty

Chase

2014

J Diabetes Sci Technol

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Type 2 diabetes mellitus (T2DM) has been declared an epidemic by the World Health Organization. 1 Treatment of T2DM individuals places considerable economic strain on health care systems worldwide. 2-4 However, it is hypothesized that early detection of T2DM can prompt intervention to delay the onset of symptoms, 5,6 and thus potentially reduce costs and improve quality of life. Insulin sensitivity (SI) is widely regarded as one of the better measures of the progression of an individual from normal glucose tolerance (NGT) to T2DM. 7-9 However, accurate determination of SI usually requires tests that are relatively clinically intense and expensive. 10 The "gold standard" test used to determine SI is the hyper-insulinemic euglycemic clamp test (EIC), which typically takes place over 2.5 to 4 hours, and requires a large number of blood samples (at least 20) to be drawn. 11 Hence other diagnostic tests are recommended in clinical practice. 12 One such test is the 2-hour Oral Glucose Tolerance Test (OGTT), which typically assesses glucose levels 2 hours after ingestion of a glucose drink. If the 2-hour glucose is in excess of 200 mg/dl a preliminary diagnoses of Type 2 Diabetes can be made. 12 However, due to the lack of an SI measurement, this process can only diagnose someone who already has T2DM, or, at lower glycemic thresholds, someone who is already displaying impaired glucose tolerance. Thus, it is not able to estimate increased risk and could not effectively prompt early preventative measures. Means do exist to approximate SI based on OGTT data. The Matsuda Index requires 4 blood samples at t = 0, 30, 60, and 120 to be taken and assayed for both glucose and insulin. 13 As such, it is far more expensive and clinically intensive than the HOMA or 2-hour OGTT 532490D STXXX10.

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Section: Discussionmentioning

confidence: 99%

Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay

Davidson

Docherty

Chase

2014

J Diabetes Sci Technol

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“…Airway pressure, flow and volume can be used to identify E rs and R rs using integral-based parameter identification 17 , 26 , 53 in an identifiable problem, 25 where E rs is defined as the average elastance of a single breath. 16 …”

Section: Methodsmentioning

confidence: 99%

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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“…If t s and t f are known, the airway pressure is a linear combination of volume, flow, time and time squared. This enables the model parameters E, R, a, b, and c to be identified by multiple linear regression [9]. The model can then be formulated as follows:…”

Section: Methodsmentioning

confidence: 99%

“…In this study an iterative approach is then used [9], where the identified a, b, and c are used to find t s and t f from the roots of the quadratic function.…”

Section: Methodsmentioning

confidence: 99%

A polynomial model of patient-specific breathing effort during controlled mechanical ventilation

Redmond

Docherty

Chiew

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Patient breathing efforts occurring during controlled ventilation causes perturbations in pressure data, which cause erroneous parameter estimation in conventional models of respiratory mechanics. A polynomial model of patient effort can be used to capture breath-specific effort and underlying lung condition. An iterative multiple linear regression is used to identify the model in clinical volume controlled data. The polynomial model has lower fitting error and more stable estimates of respiratory elastance and resistance in the presence of patient effort than the conventional single compartment model. However, the polynomial model can converge to poor parameter estimation when patient efforts occur very early in the breath, or for long duration. The model of patient effort can provide clinical benefits by providing accurate respiratory mechanics estimation and monitoring of breath-to-breath patient effort, which can be used by clinicians to guide treatment.

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Iterative integral parameter identification of a respiratory mechanics model

Cited by 33 publications

References 33 publications

Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay

Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay

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

A polynomial model of patient-specific breathing effort during controlled mechanical ventilation

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