Virtual Patients and Virtual Cohorts: A New Way to Think About the Design and Implementation of Personalized ICU Treatments

Chase, J. Geoffrey; Desaive, Thomas; Preiser, J.-C.

doi:10.1007/978-3-319-27349-5_35

Cited by 22 publications

(19 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To date, only the model-based STAR and eMPC protocols consider nutrition explicitly [97][98][99][100], and only STAR modulates nutrition delivery in addition to insulin [98][99][100]. STAR and eMPC also directly identify patient-speci c SI using virtual patient models [101,102], and can thus manage the entire trade-off in Figures 4-6 directly.…”

Section: Discussionmentioning

confidence: 99%

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

Chase¹,

Knopp²

2020

Preprint

View full text Add to dashboard Cite

Background Critically ill patients frequently experience stress-induced hyperglycaemia, leading to increased morbidity and mortality. Glycaemic control (GC) with insulin therapy alone has proven difficult, due to significant inter- and intra- patient variability in response to insulin therapy. This study reviews the problem and analyses the impact of physiological dynamics and patient variability on outcome glycemia. Methods A graphical model of metabolic dynamics is used to analyse the impact of fundamental glucose flux dynamics on insulin and nutrition administration in the context of maintaining a glycemic goal. It is used to delineate the limits of ability in controlling insulin and/or nutrition administration to achieve safe, effective glycemic control in critical illness in the presence of low insulin sensitivity and high insulin sensitivity variability. Results Insulin saturation limits insulin-mediated glucose uptake. At low insulin sensitivity, maintaining a glycemic target level requires reduced nutrition administration due to saturated insulin-mediated glucose uptake. Metabolic insulin sensitivity variability leads to insulin-mediated glucose uptake variability, requiring reduced nutrition administration at low insulin sensitivity and higher insulin doses to mitigate the risk of hypo- and hyper- glycemia. Conclusions This work reviews the clinical glycemic control problem using a graphically-based physiological analysis to show the need to control nutrition administration, along with insulin, to achieve safe, effective control. These reductions are necessary for highly insulin resistant patients, a condition typically occurring early in ICU stay. Glycemic control should directly control nutrition in addition to insulin to optimise all avenues of glucose flux and thus ensure safe, effective glycemic control.

show abstract

Section: Discussionmentioning

confidence: 99%

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

Chase¹,

Knopp²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Equally, and as noted, countries and health care systems are increasingly unable to pay for increasing demand and costs of medical care [79]. Computational physiological models and model-based control thus offer an important opportunity to personalise care, and improve costs and productivity, by combining clinical data with system identification methods to generate "virtual patient" [80][81][82][83][84][85][86][87] models to represent patients directly, the first step towards automating care.…”

Section: State Of the Artmentioning

confidence: 99%

Glycemic control in the intensive care unit: A control systems perspective

Chase

Benyó

Desaive

2019

Annual Reviews in Control

View full text Add to dashboard Cite

Computers and automation have revolutionised quality and productivity in many industries, but not in medicine. Healthcare costs are thus growing beyond the ability of society to pay for them, multiplied by the impact of increasingly aging populations, and specifically in the demand placed on intensive care unit (ICU) services. Glycemic control is a core ICU therapy with the potential to reduce both mortality and cost, which makes it an area where greater personalisation and automation could play a leading role in improving productivity and care. This review presents the background to the problem and the main issues arising in this area of care from a control systems technology perspective. It then presents a vision of a more automated future with specific goals in the areas of dynamic systems modeling, system identification and control. These areas are then given a state of the art review, mixing both medicine and control systems perspectives. It is concluded by specific recommendations for the field, where control systems expertise can be leveraged to best advantage.

show abstract

“…While realistic, it may not hold for a given structure, impacting the benefit obtained from the resulting IDA and risk assessment when degradation is included. There is thus a need for what is effectively a "virtual structure" with predictive capacity for future events, but built off identified current values, similar to biomedical engineering "virtual patients" [41][42][43][44]. Thus, a more complex smooth hysteretic model, such as a Bouc-Wen model [45,46] could be used to capture the continuous change of plasticity in the future work.…”

Section: Stiffness Degradation Analysismentioning

confidence: 99%

A combined SHM/IDA method for assessing collapse capacity and risk in subsequent ground motions

Fitzjohn

Zhou

Chase

2019

J Civil Struct Health Monit

View full text Add to dashboard Cite

Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines Hysteresis Loop Analysis (HLA) structural health monitoring (SHM) and Incremental Dynamic Analysis (IDA) methods to identify and then analyse collapse capacity and probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010-2011 had significant post-event aftershocks.

show abstract

Virtual Patients and Virtual Cohorts: A New Way to Think About the Design and Implementation of Personalized ICU Treatments

Cited by 22 publications

References 40 publications

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

Understanding insulin and nutrition administration in glycemic control with the right side of the brain

Glycemic control in the intensive care unit: A control systems perspective

A combined SHM/IDA method for assessing collapse capacity and risk in subsequent ground motions

Contact Info

Product

Resources

About