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van den Boorn M, Lagerburg V, van Steen SCJ, Wedzinga R, Bosman RJ, van der Voort PHJ. The development of a glucose prediction model in critically ill patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106105. [PMID: 33979752 DOI: 10.1016/j.cmpb.2021.106105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
PURPOSE The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values. METHODS Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG). RESULTS The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions. CONCLUSION In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system.
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Affiliation(s)
- M van den Boorn
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands.
| | - V Lagerburg
- OLVG, Medical Physics, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - S C J van Steen
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Department of Endocrinology, Meibergdreef 9, Amsterdam, Netherlands
| | - R Wedzinga
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands; OLVG, Medical Physics, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - R J Bosman
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - P H J van der Voort
- University of Groningen, University Medical Center Groningen, Department of Intensive Care, Hanzeplein 2, 9713GZ Groningen, The Netherlands
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Knopp Nee Dickson JL, Lynn AM, Shaw GM, Chase JG. Safe and effective glycaemic control in premature infants: observational clinical results from the computerised STAR-GRYPHON protocol. Arch Dis Child Fetal Neonatal Ed 2019; 104:F205-F211. [PMID: 29930148 DOI: 10.1136/archdischild-2017-314072] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 04/29/2018] [Accepted: 05/12/2018] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Previous studies examine clinical outcomes of insulin therapy in neonatal intensive care units (NICUs), without first developing safe and effective control protocols. This research quantifies the safety and performance of a computerised model-based control algorithmSTAR-GRYPHON (Stochastic TARgeted Glucose Regulation sYstem to Prevent Hyper- and hypO-glycaemia in Neonates). DESIGN Retrospective observational study of glycaemic control in very/extremely low birthweight infants treated with insulin from Christchurch Women's Hospital NICU between January 2013 and June 2017. Blood glucose (BG) outcomes and control performance is compared with retrospective data (n=22) and literature. INTERVENTIONS Insulin infusion doses were calculated from 3 to 4 hourly BG measurements using a computerised model-based control algorithm, STAR-GRYPHON. MAIN OUTCOME MEASURES Mean BG, time in targeted range and incidence of hypoglycaemia. RESULTS STAR-GRYPHON (n=35) had lower mean BG concentration (7.0mmol/L vs 7.9 mmol/L), higher %BG within the 4.0-8.0 mmol/L target range (71.1% vs 50.9%) and lower %BG <4.0 mmol/L (0.6% vs 2.1%). There were only 2 BG <2.6 mmol/L (over n=2, 5.5% of patients, 0.03% of all BG outcomes), one of which may be attributed to clinical error. These results show better control to target and lower incidence of hypoglycaemia than most literature results from intensive insulin therapy protocols or study groups in children and infants. CONCLUSIONS Model-based protocols can safely and effectively control BG in very premature infants and should be used in future studies to determine the effect of insulin therapy on clinical outcomes.
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Affiliation(s)
| | - Adrienne M Lynn
- Neonatal Intensive Care Unit, Christchurch Women's Hospital, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Intensive Care Unit, Christchurch Hospital, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Chase JG, Desaive T, Bohe J, Cnop M, De Block C, Gunst J, Hovorka R, Kalfon P, Krinsley J, Renard E, Preiser JC. Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2018; 22:182. [PMID: 30071851 PMCID: PMC6091026 DOI: 10.1186/s13054-018-2110-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 06/29/2018] [Indexed: 02/06/2023]
Abstract
There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.
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Affiliation(s)
- J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA In-Silico Medicine, University of Liège, Liège, Belgium
| | - Julien Bohe
- Medical Intensive Care Unit, Lyon-Sud University Hospital, Pierre-Bénite, France
| | - Miriam Cnop
- ULB Center for Diabetes Research, and Division of Endocrinology, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Christophe De Block
- Department of Endocrinology, Diabetology and Metabolism, Antwerp University Hospital, Edegem, Belgium
| | - Jan Gunst
- Clinical Division and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium
| | - Roman Hovorka
- University of Cambridge Metabolic Research Laboratories, Level 4, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK
| | - Pierre Kalfon
- Service de Réanimation polyvalente, Hôpital Louis Pasteur, CH de Chartres, Chartres, France
| | - James Krinsley
- Division of Critical Care, Department of Medicine, Stamford Hospital, Columbia University College of Physicians and Surgeons, Stamford, CT, USA
| | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition, and Institute of Functional Genomics, CNRS, INSERM, Montpellier University Hospital, University of Montpellier, Montpellier, France
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, route de Lennik 808, 1070, Brussels, Belgium.
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