Space GlucoseControl system for blood glucose control in intensive care patients - a European multicentre observational study

Bláha, J; Barteczko-Grajek, Barbara; Berezowicz, Pawel; J, Charvát; Chvojka, J; Grau, T.; Holmgren, Jonathan; Jaschinski, Ulrich; Kopecký, P; Man̆ák, Jan; Moehl, Mette; Paddle, Jonathan; Pasculli, Marcello; Petersson, Johan; Petros, Sirak; Radrizzani, D.; Singh, Vinodkumar; Starkopf, Joel

doi:10.1186/s12871-016-0175-4

Cited by 27 publications

(19 citation statements)

References 33 publications

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“…Compared to standard care, eMPC does well [183,186]. Comparisons across centers and cohorts show similar, but not identical, performance [171,185]. Workload is 14-18 measurements/day, and thus higher than standard care in most ICUs.…”

Section: Closed Loop and Decision Support To Guide Carementioning

confidence: 95%

“…Perhaps more importantly, such model-based SI [19,33,85,109,110,114,117,137,154,161,[165][166][167][168][169][170][171][172][173]] can be monitored and its level and/or variation assessed relative to condition [16,18,19,153,171,[174][175][176][177][178][179][180][181][182]. Thus, if accurate, this value offers not only the potential of good, personalised control, but also further insight into patient condition.…”

Section: Dynamic System Models Of the Metabolic Systemmentioning

confidence: 99%

“…To date, as noted previously, only the ICING and Cambridge models offer the first two levels of validation, and only the ICING model the third level. Although several model-based predictive controllers have been tested in short or limited trials, only a few have been used regularly in major trials [141,142] or as standard of care [159,171]. There are two essential control approaches taken to date:  Target to value (TTV), where GC is optimized to achieve a specified BG target value;  Target to range or risk (TTR), where GC is optimized using models of metabolic variability to provide a specified risk of hypo-and/or hyper-glycemia.…”

Section: Model Validationmentioning

confidence: 99%

“…The TTV eMPC (B. Braun, Germany) [146] is based on the Cambridge model, and has been used several trials [171,[183][184][185][186]. It also controls only insulin infusions, leaving nutrition as clinically set, and thus also does not use one potential control input in achieving desired BG levels.…”

Section: Closed Loop and Decision Support To Guide Carementioning

confidence: 99%

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

Chase

Benyó

Desaive

2019

Annual Reviews in Control

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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.

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Section: Closed Loop and Decision Support To Guide Carementioning

confidence: 95%

Section: Dynamic System Models Of the Metabolic Systemmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

Section: Closed Loop and Decision Support To Guide Carementioning

confidence: 99%

See 2 more Smart Citations

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

Chase

Benyó

Desaive

2019

Annual Reviews in Control

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“…For example, in glycaemic control, model-based assessment of insulin sensitivity relates input changes of insulin and nutrition, to expected glycemic response [ 36 , 37 , 41 , 44 , 116 – 130 ], which can be monitored and its level and/or variation assessed relative to condition [ 88 , 124 , 128 , 131 – 141 ]. Similarly, in mechanical ventilation, lung elastance determines how changes in the input “a controlled mechanical ventilation volume (pressure)” transfers to the uncontrolled or independent output pressure (volume) response [ 78 – 80 , 142 – 151 ], where this elastance is directly related to the work of breathing and ventilator driving pressure clinicians seek to reduce in titrating mechanical ventilation [ 148 , 149 , 152 – 156 ].…”

Section: Model Types and Requirementsmentioning

confidence: 99%

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

et al. 2018

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Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current “one size fits all” protocolised care to adaptive, model-based “one method fits all” personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

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