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Benyó B, Paláncz B, Szlávecz Á, Szabó B, Kovács K, Chase JG. Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107633. [PMID: 37343375 DOI: 10.1016/j.cmpb.2023.107633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/21/2023] [Accepted: 05/29/2023] [Indexed: 06/23/2023]
Abstract
Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.
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Affiliation(s)
- Balázs Benyó
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.
| | - Béla Paláncz
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Ákos Szlávecz
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bálint Szabó
- Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Katalin Kovács
- Department of Informatics, Széchenyi István University, Győr, Hungary
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Zhou T, Boettger M, Knopp J, Lange M, Heep A, Chase JG. Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit. Comput Biol Med 2023; 160:106808. [PMID: 37163965 DOI: 10.1016/j.compbiomed.2023.106808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023]
Abstract
Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.
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Yahia A, Szlávecz Á, Knopp JL, Norfiza Abdul Razak N, Abu Samah A, Shaw G, Chase JG, Benyo B. Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance. J Diabetes Sci Technol 2022; 16:1208-1219. [PMID: 34078114 PMCID: PMC9445352 DOI: 10.1177/19322968211018260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable. OBJECTIVE This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control. METHODS Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy. RESULTS Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors. CONCLUSIONS Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.
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Affiliation(s)
- Anane Yahia
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
- Anane Yahia, Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, 2. Magyar tudosok Blvd., Budapest, H-1117, Hungary.
| | - Ákos Szlávecz
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Jennifer L. Knopp
- Mechanical Engineering, Centre of Bio-Engineering, University of Canterbury, Christchurch, NZ
| | | | - Asma Abu Samah
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Jalan Ikram-UNITEN, Kajang, Selangor, Malaysia
| | - Geoff Shaw
- Mechanical Engineering, Centre of Bio-Engineering, University of Canterbury, Christchurch, NZ
| | - J. Geoffrey Chase
- Mechanical Engineering, Centre of Bio-Engineering, University of Canterbury, Christchurch, NZ
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
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McHugh AD, Chase JG, Knopp JL, Ormsbee JJ, Kulawiec DG, Merry TL, Murphy R, Shepherd PR, Burden HJ, Docherty PD. The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters. J Diabetes Sci Technol 2022; 16:945-954. [PMID: 33478257 PMCID: PMC9264438 DOI: 10.1177/1932296820986878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Model-based metabolic tests require accurate identification of subject-specific parameters from measured assays. Insulin assays are used to identify insulin kinetics parameters, such as general and first-pass hepatic clearances. This study assesses the impact of intravenous insulin boluses on parameter identification precision. METHOD Insulin and C-peptide data from two intravenous glucose tolerance test (IVGTT) trials of healthy adults (N = 10 × 2; denoted A and B), with (A) and without (B) insulin modification, were used to identify insulin kinetics parameters using a grid search. Monte Carlo analysis (N = 1000) quantifies variation in simulation error for insulin assay errors of 5%. A region of parameter values around the optimum was identified whose errors are within variation due to assay error. A smaller optimal region indicates more precise practical identifiability. Trial results were compared to assess identifiability and precision. RESULTS Trial B, without insulin modification, has optimal parameter regions 4.7 times larger on average than Trial A, with 1-U insulin bolus modification. Ranges of optimal parameter values between trials A and B increase from 0.04 to 0.12 min-1 for hepatic clearance and from 0.07 to 0.14 for first-pass clearance on average. Trial B's optimal values frequently lie outside physiological ranges, further indicating lack of distinct identifiability. CONCLUSIONS A small 1-U insulin bolus improves identification of hepatic clearance parameters by providing a smaller region of optimal parameter values. Adding an insulin bolus in metabolic tests can significantly improve identifiability and outcome test precision. Assay errors necessitate insulin modification in clinical tests to ensure identifiability and precision.
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Affiliation(s)
- Alexander D. McHugh
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Alexander D. McHugh, BE(Hons),
Centre for Bioengineering, Department of Mechanical Engineering,
University of Canterbury, Level 5 Civil/Mechanical Building, Private Bag 4800,
Christchurch, 8140, New Zealand.
| | - J. Geoffrey Chase
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer J. Ormsbee
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Diana G. Kulawiec
- Department of Biomedical Engineering,
Rochester Institute of Technology, Rochester, NY, USA
| | - Troy L. Merry
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular
Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Peter R. Shepherd
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Hannah J. Burden
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Paul D. Docherty
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Institute for Technical Medicine,
Furtwangen University, Villingen-Schwenningen, Germany
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McHugh AD, Chase JG, Knopp JL, Zhou T, Holder-Pearson L. Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach. J Diabetes Sci Technol 2022:19322968221085032. [PMID: 35343255 DOI: 10.1177/19322968221085032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Accurate, safe glycemic management requires reliable delivery of insulin doses. Insulin can be delivered subcutaneously for action over a longer period of time. Needle-free jet injectors provide subcutaneous (SC) delivery without requiring needle use, but the volume of insulin absorbed varies due to losses associated with the delivery method. This study employs model-based methods to determine the expected proportion of active insulin present from a needle-free SC dose. METHODS Insulin, C-peptide, and glucose assay data from a frequently sampled insulin-modified oral glucose tolerance test trial with 2U SC insulin delivery, paired with a well-validated metabolic model, predict metabolic outcomes for N = 7 healthy adults. Subject-specific nonlinear hepatic clearance profiles are modeled over time using third-order basis splines with knots located at assay times. Hepatic clearance profiles are constrained within a physiological rate of change, and relative to plasma glucose profiles. Insulin loss proportions yielding optimal insulin predictions are then identified, quantifying delivery losses. RESULTS Optimal parameter identification suggests losses of up to 22% of the nominal 2U SC dose. The degree of loss varies between subjects and between trials on the same subject. Insulin fit accuracy improves where loss greater than 5% is identified, relative to where delivery loss is not modeled. CONCLUSIONS Modeling shows needle-free SC jet injection of a nominal dose of insulin does not necessarily provide metabolic action equivalent to total dose, and this availability significantly varies between trials. By quantifying and accounting for variability of jet injection insulin doses, better glycemic management outcomes using SC jet injection may be achieved.
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Affiliation(s)
- Alexander D McHugh
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Tony Zhou
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Lui Holder-Pearson
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Abstract
BACKGROUND Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context. METHOD Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined. RESULTS Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase). CONCLUSION The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.
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Affiliation(s)
- Jennifer J. Ormsbee
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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The goldilocks problem: Nutrition and its impact on glycaemic control. Clin Nutr 2021; 40:3677-3687. [PMID: 34130014 DOI: 10.1016/j.clnu.2021.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/25/2021] [Accepted: 05/01/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Glucose intolerance and insulin resistance manifest as hyperglycaemia in intensive care, which is associated with mortality and morbidities. Glycaemic control (GC) may improve outcomes, though safe and effective control has proven elusive. Nutritional glucose intake affects blood glucose (BG) outcomes, but few protocols actively control it. This study aims to examine BG outcomes in the context of nutritional management during GC. METHODS Retrospective cohort analysis of 5 glycaemic control cohorts spanning 4 years (n = 273) from Christchurch Hospital Intensive Care Unit (ICU). GC is delivered using a single model-based protocol (STAR), with default 4.4-8.0 mmol/L target range via. modulation of insulin and nutrition. Clinical adaptations/cohorts include variations on upper target (UL-9 with 9.0 mmol/L, reducing workload and nutrition responsiveness), and insulin only (IO) with clinically set nutrition at 3 glucose concentrations (71 g/L vs. 120 and 180 g/L in the TARGET study). RESULTS Percent of BG hours in the 4.4-8.0 mmol/L range highest under standard STAR conditions (78%), and was lower at 64% under UL-9, likely due to reduced time-responsiveness of nutrition-insulin changes. By comparison, IO only resulted in 64-69% BG in range across different nutrition types. A subset of patients receiving high glucose nutrition under IO were persistently hyperglycaemic, indicating patient-specific glucose tolerance. CONCLUSION STAR GC is most effective when nutrition and insulin are modulated together with timely responsiveness to persistent hyperglycaemia. Results imply modulation of nutrition alongside insulin improves GC, particularly in patients with persistent hyperglycaemia/low glucose tolerance.
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Parente JD, Chase JG, Moeller K, Shaw GM. High Inter-Patient Variability in Sepsis Evolution: A Hidden Markov Model Analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 201:105956. [PMID: 33561709 DOI: 10.1016/j.cmpb.2021.105956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 01/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Severe sepsis and septic shock are common in the intensive care unit (ICU) and contribute significantly to cost and mortality. Early treatment is critical but is confounded by the difficulty of real-time diagnosis. This study uses hidden Markov models (HMMs) to examine whether the time evolution of sepsis can add diagnostic accuracy or value using a proven set of bio-signals. METHODS Clinical data (N=36 patients; 6071 hours), including an hourly personalised insulin sensitivity metric. A two hidden state HMM is created to discriminate diagnosed cases (Severe Sepsis, Septic Shock) from controls (SIRS, Sepsis) states. Diagnostic performance is measured by ROC curves, likelihood ratios (LHRs), sensitivity/specificity, and diagnostic odds-ratios (DOR), for a best-case resubstitution estimate and a worst-case 80/20% repeated holdout analysis. RESULTS The HMM delivered near perfect results (95% Sensitivity; 96% Specificity) for best-case resubstitution estimates, but was comparatively poor (59% Sensitivity; 61% Specificity) for worst-case repeated holdout estimations. Adding the time evolution of sepsis did not add to the accuracy of diagnosis from using the signals alone without time history. CONCLUSIONS These potentially surprising results indicate significant inter-patient variability in the time evolution of sepsis, preventing effective diagnosis in the context of the bio-signals, data, and HMM topology used. Efforts for improved real-time, early sepsis diagnosis should concentrate on the robustness and efficacy of the bio-signals and data used, as well as the level of model complexity, to create more effective real-time classifiers.
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Affiliation(s)
| | | | | | - Geoffrey M Shaw
- Otago University School of Medicine; and ICU, Christchurch Hospital; Christchurch, New Zealand.
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Chase JG, Shaw GM, Preiser JC, Knopp JL, Desaive T. Risk-Based Care: Let's Think Outside the Box. Front Med (Lausanne) 2021; 8:535244. [PMID: 33718394 PMCID: PMC7947294 DOI: 10.3389/fmed.2021.535244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 01/22/2021] [Indexed: 12/19/2022] Open
Affiliation(s)
- James Geoffrey Chase
- Centre for Bioengineering, Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, University of Otago Christchurch School of Medicine, Christchurch, New Zealand
| | | | - Jennifer L Knopp
- Centre for Bioengineering, Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, Liege, Belgium
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Uyttendaele V, Chase JG, Knopp JL, Gottlieb R, Shaw GM, Desaive T. Insulin sensitivity in critically ill patients: are women more insulin resistant? Ann Intensive Care 2021; 11:12. [PMID: 33475909 PMCID: PMC7818291 DOI: 10.1186/s13613-021-00807-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 01/12/2021] [Indexed: 02/07/2023] Open
Abstract
Background Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care. Methods Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal. Results Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent. Conclusion Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.
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Affiliation(s)
- Vincent Uyttendaele
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium. .,Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jennifer L Knopp
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
| | - Rebecca Gottlieb
- Medtronic Diabetes, 18000 Devonshire St, Northridge, CA, 91325, USA
| | - Geoffrey M Shaw
- Christchurch Hospital, Dept of Intensive Care, Christchurch, New Zealand and University of Otago, School of Medicine, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
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Uyttendaele V, Chase JG, Knopp JL, Gottlieb R, Shaw GM, Desaive T. Insulin sensitivity in critically ill patients: are women more insulin resistant? Ann Intensive Care 2021. [PMID: 33475909 DOI: 10.1186/s13613-021-00807-7.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care. METHODS Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal. RESULTS Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent. CONCLUSION Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.
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Affiliation(s)
- Vincent Uyttendaele
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium. .,Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jennifer L Knopp
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
| | - Rebecca Gottlieb
- Medtronic Diabetes, 18000 Devonshire St, Northridge, CA, 91325, USA
| | - Geoffrey M Shaw
- Christchurch Hospital, Dept of Intensive Care, Christchurch, New Zealand and University of Otago, School of Medicine, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
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Lee JWW, Chiew YS, Wang X, Tan CP, Mat Nor MB, Damanhuri NS, Chase JG. Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients. Ann Biomed Eng 2021; 49:3280-3295. [PMID: 34435276 PMCID: PMC8386681 DOI: 10.1007/s10439-021-02854-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
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, Ers, 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 Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. 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 Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.
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Affiliation(s)
- Jay Wing Wai Lee
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Yeong Shiong Chiew
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Xin Wang
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Chee Pin Tan
- grid.440425.3School of Engineering, Monash University Malaysia, 47500 Subang Jaya, Selangor Malaysia
| | - Mohd Basri Mat Nor
- grid.440422.40000 0001 0807 5654Kulliyah of Medicine, International Islamic University Malaysia, 25200 Kuantan, Pahang Malaysia
| | - Nor Salwa Damanhuri
- grid.412259.90000 0001 2161 1343Faculty of Electrical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Bukit Bertajam, Pulau Pinang Malaysia
| | - J. Geoffrey Chase
- grid.21006.350000 0001 2179 4063Center of Bioengineering, University of Canterbury, Christchurch, 8041 New Zealand
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Knopp JL, Chase JG, Shaw GM. Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU. Ther Adv Endocrinol Metab 2021; 12:20420188211012144. [PMID: 34123348 PMCID: PMC8173630 DOI: 10.1177/20420188211012144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/02/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Critical care populations experience demographic shifts in response to trends in population and healthcare, with increasing severity and/or complexity of illness a common observation worldwide. Inflammation in critical illness impacts glucose-insulin metabolism, and hyperglycaemia is associated with mortality and morbidity. This study examines longitudinal trends in insulin sensitivity across almost a decade of glycaemic control in a single unit. METHODS A clinically validated model of glucose-insulin dynamics is used to assess hour-hour insulin sensitivity over the first 72 h of insulin therapy. Insulin sensitivity and its hour-hour percent variability are examined over 8 calendar years alongside severity scores and diagnostics. RESULTS Insulin sensitivity was found to decrease by 50-55% from 2011 to 2015, and remain low from 2015 to 2018, with no concomitant trends in age, severity scores or risk of death, or diagnostic category. Insulin sensitivity variability was found to remain largely unchanged year to year and was clinically equivalent (95% confidence interval) at the median and interquartile range. Insulin resistance was associated with greater incidence of high insulin doses in the effect saturation range (6-8 U/h), with the 75th percentile of hourly insulin doses rising from 4-4.5 U/h in 2011-2014 to 6 U/h in 2015-2018. CONCLUSIONS Increasing insulin resistance was observed alongside no change in insulin sensitivity variability, implying greater insulin needs but equivalent (variability) challenge to glycaemic control. Increasing insulin resistance may imply greater inflammation and severity of illness not captured by existing severity scores. Insulin resistance reduces glucose tolerance, and can cause greater incidence of insulin saturation and resultant hyperglycaemia. Overall, these results have significant clinical implications for glycaemic control and nutrition management.
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Affiliation(s)
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
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Braithwaite SS, Barakat K, Idrees T, Qureshi F, Soetan OT. Algorithm Maxima for Intravenous Insulin Infusion. Diabetes Technol Ther 2020; 22:861-864. [PMID: 32915059 PMCID: PMC7698999 DOI: 10.1089/dia.2020.0343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Susan S. Braithwaite
- Presence Saint Joseph Hospital, Department of Medicine/Endocrinology, Chicago, Illinois, USA
- Tallhassee Memorial Healthcare, Department of Endocrinology, Tallahassee, Florida, USA
- Florida State University, College of Medicine, Tallahassee, Florida, USA
- Address correspondence to: Susan S. Braithwaite, MD, 4321 Preserve Lane, Tallahassee, FL 32317, USA
| | - Khalid Barakat
- Ascension via Christi St. Francis Hospital, Hospitalist–Sound Physicians, Wichita, Kansas, USA
| | - Thaer Idrees
- Section of Endocrinology, The University of Chicago, Chicago, Illinois, USA
| | - Faisal Qureshi
- Section of Endocrinology & Metabolism, Amita Saint Francis Hospital, Evanston, Illinois, USA
- Amita Saint Joseph Hospital, Chicago, Illinois, USA
- College of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA
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Parente JD, Chase JG, Moeller K, Shaw GM. Quantifying misclassification and bias errors due to hierarchical sepsis scores in real-time sepsis diagnosis. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abdul Razak A, Abu-Samah A, Abdul Razak NN, Jamaludin U, Suhaimi F, Ralib A, Mat Nor MB, Pretty C, Knopp JL, Chase JG. Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients. MEDICAL DEVICES-EVIDENCE AND RESEARCH 2020; 13:139-149. [PMID: 32607009 PMCID: PMC7282801 DOI: 10.2147/mder.s231856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 01/15/2020] [Indexed: 12/15/2022] Open
Abstract
Purpose This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage. Patients and Methods STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed. Results The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance. Conclusion The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.
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Affiliation(s)
| | - Asma Abu-Samah
- Department of Electrical, Electronics and Systems, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | | | - Ummu Jamaludin
- Department of Mechanical Engineering, Universiti Malaysia Pahang, Kuantan, Malaysia
| | - Fatanah Suhaimi
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Azrina Ralib
- Department of Anesthesiology, International Islamic University Malaysia, Kuantan, Malaysia
| | - Mohd Basri Mat Nor
- Intensive Care Unit, International Islamic University Medical Centre, Kuantan, Malaysia
| | - Christopher Pretty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer Laura Knopp
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - James Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Sun Q, Zhou C, Chase JG. Parameter updating of a patient-specific lung mechanics model for optimising mechanical ventilation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Davidson SM, Uyttendaele V, Pretty CG, Knopp JL, Desaive T, Chase JG. Virtual patient trials of a multi-input stochastic model for tight glycaemic control using insulin sensitivity and blood glucose data. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101896] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Parente JD, Chase JG, Möller K, Shaw GM. Kernel density estimates for sepsis classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 188:105295. [PMID: 31918193 DOI: 10.1016/j.cmpb.2019.105295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/19/2019] [Accepted: 12/21/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Severe sepsis is a leading cause of intensive care unit (ICU) admission, length of stay, mortality, and cost. systemic inflammatory response syndrome (SIRS) and organ failure due to infection define it, but also make it hard to diagnose. Early diagnosis reduces morbidity, mortality and cost, and diagnosis is often significantly delayed due to a lack of effective biomarkers. This research employs kernel density estimation (KDE) methods fusing a personalized, model-based insulin sensitivity (SI) metric with standard bedside measures of: temperature, heart rate, respiratory rate, systolic and diastolic blood pressure, and SIRS, as these measures are available hourly or more frequently. METHODS Model-based SI is a derived metric, identified using clinical data and a clinically validated metabolic model. The KDE classifier discriminates severe sepsis and septic shock from moderate sepsis using accepted consensus sepsis scores. A best case in-sample estimate, a worst case independent cross validation estimate, and an accepted .632 bootstrap estimate are calculated to assess performance using multi-level likelihood ratios, and sensitivity and specificity. Performance is assessed against clinically and statistically defined thresholds denoted for the minimum acceptable level as: "high accuracy, often providing useful information, and clinical significance," and similar definitions for greater or lesser quality. RESULTS The .632 bootstrap estimate performs near clinically defined levels of high accuracy, often providing useful information, and clinical significance based on sensitivity, specificity, and multilevel likelihood ratios. CONCLUSION AND SIGNIFICANCE The classifier created and this overall approach is useful for clinical decision making in diagnosing severe sepsis and septic shock in real time, for both case and control hours. However, improvements could be made with larger clinical data sets.
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Affiliation(s)
- Jacquelyn Dawn Parente
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.
| | - J Geoffrey Chase
- Centre of Bioengineering, University of Canterbury, Christchurch, New Zealand.
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.
| | - Geoffrey M Shaw
- Intensive Care Unit, Canterbury District Health Board, Christchurch, New Zealand.
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Uyttendaele V, Knopp JL, Shaw GM, Desaive T, Chase JG. Risk and reward: extending stochastic glycaemic control intervals to reduce workload. Biomed Eng Online 2020; 19:26. [PMID: 32349750 PMCID: PMC7191799 DOI: 10.1186/s12938-020-00771-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/17/2020] [Indexed: 01/08/2023] Open
Abstract
Background STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1–3 hourly measurement and intervention intervals. However, the average 11–12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1–3 hourly intervals to 1 to 4-, 5-, and 6-hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals. Results Extending STAR from 1–3 hourly to 1–6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4–8.0 mmol/L target band (from 83 to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper- (15% to 18% BG > 8.0 mmol/L) and hypo- (2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates. Conclusions The modest increased risk of hyper- and hypo-glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically.
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Affiliation(s)
- Vincent Uyttendaele
- GIGA-In Silico Medicine, University of Liège, Allée Du 6 Août 19, Bât. B5a, 4000, Liège, Belgium. .,Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Dept of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.,School of Medicine, University of Otago, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In Silico Medicine, University of Liège, Allée Du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
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Uyttendaele V, Knopp JL, Pirotte M, Morimont P, Lambermont B, Shaw GM, Desaive T, Chase JG. STAR-Liège Clinical Trial Interim Results: Safe and Effective Glycemic Control for All. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:277-280. [PMID: 31945895 DOI: 10.1109/embc.2019.8856303] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
While the benefits of glycemic control for critically ill patients are increasingly demonstrated, the ability to deliver safe, effective control to intermediate target ranges is widely debated due to the increased risk of hypoglycemia. This study analyzes interim clinical trial results of the fully computerized model-based Stochastic TARgeted (STAR) glycemic control framework at the University Hospital of Liège, Belgium. Patients with dysglycemia were randomly assigned to the full version of STAR, modulating both insulin and nutrition inputs, or STAR-IO, an insulin only version of STAR. Both arms target the normoglycemic 80-145 mg/dL (4.4-8.0 mmol/L) band. Results are further compared to retrospective data from 20 patients under the standard unit protocol targeting a higher 100-150 mg/dL (5.6-8.3 mmol/L) band. Much higher time in target band is provided under the full version of STAR, with similar safety and significantly lower incidence of mild hyperglycemia (blood glucose > 145 mg/dL or 8.0 mmol/L) and severe hyperglycemia (blood glucose > 180 mg/dL or 10.0 mmol/L). As a result, lower median blood glucose levels are safely and consistently achieved with lower glycemic variability, suggesting STAR's potential to improve clinical outcomes. These interim results show the possibility to achieve safe, effective control for all patients using STAR, and suggest glycemic control to lower targets could be beneficial.
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22
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Yahia A, Benyo B, Chase JG. Clinical application scenarios to handle insulin resistance and high endogenous glucose production for intensive care patients. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.ifacol.2020.12.650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Davidson S, Pretty C, Uyttendaele V, Knopp J, Desaive T, Chase JG. Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 182:105043. [PMID: 31470221 DOI: 10.1016/j.cmpb.2019.105043] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 07/28/2019] [Accepted: 08/20/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output. METHODS This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1525 patients. RESULTS Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G. CONCLUSIONS Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required.
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Affiliation(s)
- Shaun Davidson
- Department of Mechanical Engineering, University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, Christchurch 8041, New Zealand.
| | - Chris Pretty
- Department of Mechanical Engineering, University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, Christchurch 8041, New Zealand
| | | | - Jennifer Knopp
- Department of Mechanical Engineering, University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, Christchurch 8041, New Zealand
| | - Thomas Desaive
- GIGA-Cardiovascular Sciences, University of Liège, Liège, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, Christchurch 8041, New Zealand
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3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation. Biomed Eng Online 2019; 18:102. [PMID: 31640720 PMCID: PMC6805453 DOI: 10.1186/s12938-019-0720-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 10/09/2019] [Indexed: 01/08/2023] Open
Abstract
Background The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach. Results In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4–6.5 mmol/L range. Conclusions This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.
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Abu-Samah A, Knopp JL, Abdul Razak NN, Razak AA, Jamaludin UK, Mohamad Suhaimi F, Md Ralib A, Mat Nor MB, Chase JG, Pretty CG. Model-based glycemic control in a Malaysian intensive care unit: performance and safety study. MEDICAL DEVICES-EVIDENCE AND RESEARCH 2019; 12:215-226. [PMID: 31239792 PMCID: PMC6551612 DOI: 10.2147/mder.s187840] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 01/30/2019] [Indexed: 01/08/2023] Open
Abstract
Background: Stress-induced hyperglycemia is common in critically ill patients. A few forms of model-based glycemic control have been introduced to reduce this phenomena and among them is the automated STAR protocol which has been used in the Christchurch and Gyulá hospitals' intensive care units (ICUs) since 2010. Methods: This article presents the pilot trial assessment of STAR protocol which has been implemented in the International Islamic University Malaysia Medical Centre (IIUMMC) Hospital ICU since December 2017. One hundred and forty-two patients who received STAR treatment for more than 20 hours were used in the assessment. The initial results are presented to discuss the ability to adopt and adapt the model-based control framework in a Malaysian environment by analyzing its performance and safety. Results: Overall, 60.7% of blood glucose measurements were in the target band. Only 0.78% and 0.02% of cohort measurements were below 4.0 mmol/L and 2.2 mmol/L (the limitsfor mild and severe hypoglycemia, respectively). Treatment preference-wise, the clinical staff were favorable of longer intervention options when available. However, 1 hourly treatments were still used in 73.7% of cases. Conclusion: The protocol succeeded in achieving patient-specific glycemic control while maintaining safety and was trusted by nurses to reduce workload. Its lower performance results, however, give the indication for modification in some of the control settings to better fit the Malaysian environment.
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Affiliation(s)
- Asma Abu-Samah
- Institute of Energy Infrastructure, Universiti Tenaga Nasional, Kajang, 43000, Malaysia
| | - Jennifer Launa Knopp
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand
| | | | | | | | | | - Azrina Md Ralib
- Advanced Medical and Dental Institute, Universiti Sains Islam Malaysia, Kepala Batas, 13200, Malaysia
| | - Mohd Basri Mat Nor
- Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, 25200, Malaysia
| | - James Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, 8041, New Zealand
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Abstract
PURPOSE OF REVIEW Critically ill patients usually develop hyperglycemia, which is associated with adverse outcome. Controversy exists whether the relationship is causal or not. This review summarizes recent evidence regarding glucose control in the ICU. RECENT FINDINGS Despite promising effects of tight glucose control in pioneer randomized controlled trials, the benefit has not been confirmed in subsequent multicenter studies and one trial found potential harm. This discrepancy could be explained by methodological differences between the trials rather than by a different case mix. Strategies to improve the efficacy and safety of tight glucose control have been developed, including the use of computerized treatment algorithms. SUMMARY The ideal blood glucose target remains unclear and may depend on the context. As compared with tolerating severe hyperglycemia, tight glucose control is well tolerated and effective in patients receiving early parenteral nutrition when provided with a protocol that includes frequent, accurate glucose measurements and avoids large glucose fluctuations. All patient subgroups potentially benefit, with the possible exception of patients with poorly controlled diabetes, who may need less aggressive glucose control. It remains unclear whether tight glucose control is beneficial or not in the absence of early parenteral nutrition.
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Morton SE, Knopp JL, Chase JG, Docherty P, Howe SL, Möller K, Shaw GM, Tawhai M. Optimising mechanical ventilation through model-based methods and automation. ANNUAL REVIEWS IN CONTROL 2019; 48:369-382. [PMID: 36911536 PMCID: PMC9985488 DOI: 10.1016/j.arcontrol.2019.05.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/09/2019] [Accepted: 05/01/2019] [Indexed: 06/11/2023]
Abstract
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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Affiliation(s)
- Sophie E Morton
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Paul Docherty
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Sarah L Howe
- Department of Mechanical Engineering, University of Canterbury, New Zealand
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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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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Stewart KW, Chase JG, Pretty CG, Shaw GM. Nutrition delivery, workload and performance in a model-based ICU glycaemic control system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:9-18. [PMID: 30415721 DOI: 10.1016/j.cmpb.2018.09.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 08/20/2018] [Accepted: 09/10/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Hyperglycaemia is commonplace in the adult intensive care unit (ICU), and has been associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a model-based GC protocol that uniquely maintains normoglycaemia by changing both insulin and nutrition interventions, and has been proven effective in controlling blood glucose (BG) in the ICU. However, most ICU GC protocols only change insulin interventions, making the variable nutrition aspect of STAR less clinically desirable. This paper compares the performance of STAR modulating only insulin, with three simpler alternative nutrition protocols in clinically evaluated virtual trials. METHODS Alternative nutrition protocols are fixed nutrition rate (100% caloric goal), CB (Cahill et al. best) stepped nutrition rate (60%, 80% and 100% caloric goal for the first 3 days of GC, and 100% thereafter) and SLQ (STAR lower quartile) stepped nutrition rate (65%, 75% and 85% caloric goal for the first 3 days of GC, and 85% thereafter). Each nutrition protocol is simulated with the STAR insulin protocol on a 221 patient virtual cohort, and GC performance, safety and total intervention workload are assessed. RESULTS All alternative nutrition protocols considerably reduced total intervention workload (14.6-19.8%) due to reduced numbers of nutrition changes. However, only the stepped nutrition protocols achieved similar GC performance to the current variable nutrition protocol. Of the two stepped nutrition protocols, the SLQ nutrition protocol also improved GC safety, almost halving the number of severe hypoglycaemic cases (5 vs. 9, P = 0.42). CONCLUSIONS Overall, the SLQ nutrition protocol was the best alternative to the current variable nutrition protocol, but either stepped nutrition protocol could be adapted by STAR to reduce workload and make it more clinically acceptable, while maintaining its proven performance and safety.
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Affiliation(s)
- Kent W Stewart
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Christopher G Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand.
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Abstract
BACKGROUND This study investigates blood glucose (BG) measurement interpolation techniques to represent intermediate BG dynamics, and the effect resampling of retrospective BG data has on key glycemic control (GC) performance results. GC protocols in the ICU have varying BG measurement intervals ranging from 0.5 to 4 hours. Sparse data pose problems, particularly in comparing GC performance or model fitting, and thus interpolation is required. METHODS Retrospective data from SPRINT in Christchurch Hospital Intensive Care Unit (ICU) (2005-2007) were used to analyze several interpolation techniques. Piecewise linear, spline, and cubic interpolation functions, which force interpolation through measured data, as well as 1st and 2nd Order B-spline basis functions, are used to identify the interpolated trace. Dense data were thinned to increase sparsity and obtain measurements (Hidden Measurements) for comparison after interpolation. Performance is assessed based on error in capturing hidden measurements. Finally, the effect of minutely versus hourly sampling of the interpolated trace on key GC performance statistics was investigated using retrospective data received from STAR GC in Christchurch Hospital ICU, New Zealand (2011-2015). RESULTS All of the piecewise functions performed considerably better than the fitted interpolation functions. Linear piecewise interpolation performed the best having a mean RMSE 0.39 mmol/L, within 2 standard deviations of the BG sensor error. Minutely sampled BG resulted in significantly different key GC performance values when compared to raw sparse BG measurements. CONCLUSION Linear piecewise interpolation provides the best estimate of intermediate BG dynamics and all analyses comparing GC protocol performance should use minutely linearly interpolated BG data.
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Affiliation(s)
- Kent W. Stewart
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
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Uyttendaele V, Knopp JL, Stewart KW, Desaive T, Benyó B, Szabó-Némedi N, Illyés A, Shaw GM, Chase JG. A 3D insulin sensitivity prediction model enables more patient-specific prediction and model-based glycaemic control. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.032] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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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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Jamaludin UK, M Suhaimi F, Abdul Razak NN, Md Ralib A, Mat Nor MB, Pretty CG, Humaidi L. Performance of Stochastic Targeted Blood Glucose Control Protocol by virtual trials in the Malaysian intensive care unit. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:149-155. [PMID: 29903481 DOI: 10.1016/j.cmpb.2018.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 02/26/2018] [Accepted: 03/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Blood glucose variability is common in healthcare and it is not related or influenced by diabetes mellitus. To minimise the risk of high blood glucose in critically ill patients, Stochastic Targeted Blood Glucose Control Protocol is used in intensive care unit at hospitals worldwide. Thus, this study focuses on the performance of stochastic modelling protocol in comparison to the current blood glucose management protocols in the Malaysian intensive care unit. Also, this study is to assess the effectiveness of Stochastic Targeted Blood Glucose Control Protocol when it is applied to a cohort of diabetic patients. METHODS Retrospective data from 210 patients were obtained from a general hospital in Malaysia from May 2014 until June 2015, where 123 patients were having comorbid diabetes mellitus. The comparison of blood glucose control protocol performance between both protocol simulations was conducted through blood glucose fitted with physiological modelling on top of virtual trial simulations, mean calculation of simulation error and several graphical comparisons using stochastic modelling. RESULTS Stochastic Targeted Blood Glucose Control Protocol reduces hyperglycaemia by 16% in diabetic and 9% in nondiabetic cohorts. The protocol helps to control blood glucose level in the targeted range of 4.0-10.0 mmol/L for 71.8% in diabetic and 82.7% in nondiabetic cohorts, besides minimising the treatment hour up to 71 h for 123 diabetic patients and 39 h for 87 nondiabetic patients. CONCLUSION It is concluded that Stochastic Targeted Blood Glucose Control Protocol is good in reducing hyperglycaemia as compared to the current blood glucose management protocol in the Malaysian intensive care unit. Hence, the current Malaysian intensive care unit protocols need to be modified to enhance their performance, especially in the integration of insulin and nutrition intervention in decreasing the hyperglycaemia incidences. Improvement in Stochastic Targeted Blood Glucose Control Protocol in terms of uen model is also a must to adapt with the diabetic cohort.
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Affiliation(s)
- Ummu K Jamaludin
- Universiti Malaysia Pahang, Faculty of Mechanical Engineering, 26600 Pekan, Pahang, Malaysia.
| | - Fatanah M Suhaimi
- Universiti Sains Malaysia, Advanced Medical and Dental Institute, 13200 Bertam, Kepala Batas, Penang, Malaysia
| | - Normy Norfiza Abdul Razak
- Universiti Tenaga Nasional, College of Engineering, Putrajaya Campus, 43000 Kajang, Selangor, Malaysia
| | - Azrina Md Ralib
- International Islamic University Malaysia, Kuliyyah of Medicine, 25200 Kuantan, Pahang, Malaysia
| | - Mohd Basri Mat Nor
- International Islamic University Malaysia, Kuliyyah of Medicine, 25200 Kuantan, Pahang, Malaysia
| | - Christopher G Pretty
- University of Canterbury, Department of Mechanical Engineering, Private Bag 4800, Christchurch 8041, New Zealand
| | - Luqman Humaidi
- Universiti Malaysia Pahang, Faculty of Mechanical Engineering, 26600 Pekan, Pahang, Malaysia
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Stewart KW, Pretty CG, Shaw GM, Chase JG. Creating smooth SI. B-spline basis function representations of insulin sensitivity. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Braithwaite SS, Clark LP, Idrees T, Qureshi F, Soetan OT. Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion. Curr Diab Rep 2018; 18:26. [PMID: 29582176 DOI: 10.1007/s11892-018-0994-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier. RECENT FINDINGS Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.
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Affiliation(s)
- Susan Shapiro Braithwaite
- , 1135 Ridge Road, Wilmette, IL, 60091, USA.
- Endocrinology Consults and Care, S.C, 3048 West Peterson Ave, Chicago, IL, 60659, USA.
| | - Lisa P Clark
- Presence Saint Francis Hospital, 355 Ridge Ave, Evanston, IL, 60202, USA
| | - Thaer Idrees
- Presence Saint Joseph Hospital, 2900 N. Lakeshore Dr, Chicago, IL, 60657, USA
| | - Faisal Qureshi
- Presence Saint Joseph Hospital, 2800 N Sheridan Road Suite 309, Chicago, IL, 60657, USA
| | - Oluwakemi T Soetan
- Presence Saint Joseph Hospital, 2900 N. Lakeshore Dr, Chicago, IL, 60657, USA
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Abstract
PURPOSE OF REVIEW We discuss key studies that have set the scene for the debate on the efficacy and safety of tight glycemic control in critically ill patients, highlighting important differences among them, and describe the ensuing search towards strategies for safer glucose control. RECENT FINDINGS Differences in level of glycemic control, glucose measurement and insulin administration, expertise, and nutritional management may explain the divergent outcomes of the landmark studies on tight glycemic control in critical illness. Regarding strategies towards safer glucose control, several computerized algorithms have shown promise, but lack validation in adequately powered outcome studies. Real-time continuous glucose monitoring and closed loop blood glucose control systems are not up to the task yet due to technical challenges, though recent advances are promising. Alternatives for insulin have only been investigated in small feasibility studies. Severe hyperglycemia in critically ill patients generally is not tolerated anymore, but the optimal blood glucose target may depend on the specific patient and logistic context.
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Affiliation(s)
- Ilse Vanhorebeek
- Clinical Division and Laboratory of Intensive Care Medicine, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - Jan Gunst
- Clinical Division and Laboratory of Intensive Care Medicine, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Greet Van den Berghe
- Clinical Division and Laboratory of Intensive Care Medicine, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. 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. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
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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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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Stewart KW, Chase JG, Pretty CG, Shaw GM. Nutrition delivery of a model-based ICU glycaemic control system. Ann Intensive Care 2018; 8:4. [PMID: 29330610 PMCID: PMC5768573 DOI: 10.1186/s13613-017-0351-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/29/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Hyperglycaemia is commonplace in the adult intensive care unit (ICU), associated with increased morbidity and mortality. Effective glycaemic control (GC) can reduce morbidity and mortality, but has proven difficult. STAR is a proven, effective model-based ICU GC protocol that uniquely maintains normo-glycaemia by changing both insulin and nutrition interventions to maximise nutrition in the context of GC in the 4.4-8.0 mmol/L range. Hence, the level of nutrition it provides is a time-varying estimate of the patient-specific ability to take up glucose. METHODS First, the clinical provision of nutrition by STAR in Christchurch Hospital, New Zealand (N = 221 Patients) is evaluated versus other ICUs, based on the Cahill et al. survey of 158 ICUs. Second, the inter- and intra- patient variation of nutrition delivery with STAR is analysed. Nutrition rates are in terms of percentage of caloric goal achieved. RESULTS Mean nutrition rates clinically achieved by STAR were significantly higher than the mean and best ICU surveyed, for the first 3 days of ICU stay. There was large inter-patient variation in nutrition rates achieved per day, which reduced overtime as patient-specific metabolic state stabilised. Median intra-patient variation was 12.9%; however, the interquartile range of the mean per-patient nutrition rates achieved was 74.3-98.2%, suggesting patients do not deviate much from their mean patient-specific nutrition rate. Thus, the ability to tolerate glucose intake varies significantly between, rather than within, patients. CONCLUSIONS Overall, STAR's protocol-driven changes in nutrition rate provide higher nutrition rates to hyperglycaemic patients than those of 158 ICUs from 20 countries. There is significant inter-patient variability between patients to tolerate and uptake glucose, where intra-patient variability over stay is much lower. Thus, a best nutrition rate is likely patient specific for patients requiring GC. More importantly, these overall outcomes show high nutrition delivery and safe, effective GC are not exclusive and that restricting nutrition for GC does not limit overall nutritional intake compared to other ICUs.
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Affiliation(s)
- Kent W. Stewart
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140 New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140 New Zealand
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8140 New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
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Zhou T, Dickson JL, Shaw GM, Chase JG. Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study. J Diabetes Sci Technol 2018; 12:7-19. [PMID: 29103302 PMCID: PMC5761989 DOI: 10.1177/1932296817738791] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) technology has become more prevalent in the intensive care unit (ICU), offering potential benefits of increased safety and reduced workload in glycemic control (GC). The drift and higher point accuracy errors of CGM devices over traditional intermittent blood glucose (BG) measures have so far limited their application in the ICU. This study delineates the trade-offs of performance, safety and workload that CGM sensors provide in GC protocols. METHODS Clinical data from 236 patients were used for clinically validated virtual trials. A CGM-enabled version of the STAR GC protocol was used to evaluate the use of guard rails and rolling windows. Safety was assessed through percentage of patients who had a severe hypoglycemic episode (BG < 40 mg/dl) as well as percentage of resampled BG < 72 mg/dl. Performance was assessed as percentage of resampled measurements in the 80-126 mg/dl and the 80-144 mg/dl target bands. Workload was measured by number of manual BG measures per day. RESULTS CGM-enabled versions of STAR decreased the number of required blood draws by up to 74%, while maintaining performance (76.6% BG measurements in the 80-126 mg/dl range vs 62.8% clinically, 87.9% in the 80-144 mg/dl range vs 83.7% clinically) and maintaining patient safety (1.13% of patients experienced a severe hypoglycemic event vs 0.85% clinically, 1.37% of BG measurements were less than 72 mg/dl vs 0.51% clinically). CONCLUSION CGM sensor traces were reproduced in virtual trials to guide GC. Existing GC protocols such as STAR may need to be adjusted only slightly to gain the benefits of the increased temporal measurements of CGM sensors, through which workload may be significantly decreased while maintaining GC performance and safety.
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Affiliation(s)
- Tony Zhou
- Department of Mechanical Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
- Tony Zhou, BE, Department of Mechanical Engineering, University of Canterbury, 20 Kirkwood Ave, Riccarton, Christchurch, Canterbury 8041, New Zealand.
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch School of Medicine and Health Science, University of Otago, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, Canterbury, New Zealand
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Abstract
PURPOSE OF REVIEW We reviewed the strategies associated with hypoglycemia risk reduction among critically ill non-pregnant adult patients. RECENT FINDINGS Hypoglycemia in the ICU has been associated with increased mortality in a number of studies. Insulin dosing and glucose monitoring rules, response to impending hypoglycemia, use of computerization, and attention to modifiable factors extrinsic to insulin algorithms may affect the risk for hypoglycemia. Recurring use of intravenous (IV) bolus doses of insulin in insulin-resistant cases may reduce reliance upon higher IV infusion rates. In order to reduce the risk for hypoglycemia in the ICU, caregivers should define responses to interruption of continuous carbohydrate exposure, incorporate transitioning strategies upon initiation and interruption of IV insulin, define modifications of antihyperglycemic therapy in the presence of worsening renal function or chronic kidney disease, and anticipate the effects traceable to other medications and substances. Institutional and system-wide quality improvement efforts should assign priority to hypoglycemia prevention.
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Affiliation(s)
- Susan Shapiro Braithwaite
- , 1135 Ridge Road, Wilmette, IL, 60091, USA.
- Endocrinology Consults and Care, S.C, 3048 West Peterson Ave, Chicago, IL, 60659, USA.
| | - Dharmesh B Bavda
- Presence Saint Joseph Hospital-Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA
| | - Thaer Idrees
- Presence Saint Joseph Hospital-Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA
| | - Faisal Qureshi
- , 2800 N Sheridan Road Suite 309, Chicago, IL, 60657, USA
| | - Oluwakemi T Soetan
- Presence Saint Joseph Hospital-Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA
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Chase JG, Dickson JL. Traversing the valley of glycemic control despair. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017; 21:237. [PMID: 28882190 PMCID: PMC5590151 DOI: 10.1186/s13054-017-1824-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - Jennifer L Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
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Dubois J, Van Herpe T, van Hooijdonk RT, Wouters R, Coart D, Wouters P, Van Assche A, Veraghtert G, De Moor B, Wauters J, Wilmer A, Schultz MJ, Van den Berghe G, Mesotten D. Software-guided versus nurse-directed blood glucose control in critically ill patients: the LOGIC-2 multicenter randomized controlled clinical trial. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017; 21:212. [PMID: 28806982 PMCID: PMC5557320 DOI: 10.1186/s13054-017-1799-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 07/20/2017] [Indexed: 01/04/2023]
Abstract
Background Blood glucose control in the intensive care unit (ICU) has the potential to save lives. However, maintaining blood glucose concentrations within a chosen target range is difficult in clinical practice and holds risk of potentially harmful hypoglycemia. Clinically validated computer algorithms to guide insulin dosing by nurses have been advocated for better and safer blood glucose control. Methods We conducted an international, multicenter, randomized controlled trial involving 1550 adult, medical and surgical critically ill patients, requiring blood glucose control. Patients were randomly assigned to algorithm-guided blood glucose control (LOGIC-C, n = 777) or blood glucose control by trained nurses (Nurse-C, n = 773) during ICU stay, according to the local target range (80–110 mg/dL or 90–145 mg/dL). The primary outcome measure was the quality of blood glucose control, assessed by the glycemic penalty index (GPI), a measure that penalizes hypoglycemic and hyperglycemic deviations from the chosen target range. Incidence of severe hypoglycemia (<40 mg/dL) was the main safety outcome measure. New infections in ICU, duration of hospital stay, landmark 90-day mortality and quality of life were clinical safety outcome measures. Results The median GPI was lower in the LOGIC-C (10.8 IQR 6.2–16.1) than in the Nurse-C group (17.1 IQR 10.6–26.2) (P < 0.001). Mean blood glucose was 111 mg/dL (SD 15) in LOCIC-C versus 119 mg/dL (SD 21) in Nurse-C, whereas the median time-in-target range was 67.0% (IQR 52.1–80.1) in LOGIC-C versus 47.1% (IQR 28.1–65.0) in the Nurse-C group (both P < 0.001). The fraction of patients with severe hypoglycemia did not differ between LOGIC-C (0.9%) and Nurse-C (1.2%) (P = 0.6). The clinical safety outcomes did not differ between groups. The sampling interval was 2.3 h (SD 0.5) in the LOGIC-C group versus 3.0 h (SD 0.8) in the Nurse-C group (P < 0.001). Conclusions In a randomized controlled trial of a mixed critically ill patient population, the use of the LOGIC-Insulin blood glucose control algorithm, compared with blood glucose control by expert nurses, improved the quality of blood glucose control without increasing hypoglycemia. Trial registration ClinicalTrials.gov, NCT02056353. Registered on 4 February 2014. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1799-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jasperina Dubois
- Department of Anesthesia & Intensive Care, Jessa Hospital, Salvatorstraat 20, B-3500, Hasselt, Belgium
| | - Tom Van Herpe
- Department of Electrical Engineering (ESAT), Research Division SCD, iMINDS Future Health Dept, KU Leuven, Kasteelpark Arenberg 10, B-3001, Leuven (Heverlee), Belgium
| | - Roosmarijn T van Hooijdonk
- Department of Intensive Care Medicine, Academic Medical Center, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
| | - Ruben Wouters
- Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Domien Coart
- Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Pieter Wouters
- Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Aimé Van Assche
- Department of Anesthesia & Intensive Care, Jessa Hospital, Salvatorstraat 20, B-3500, Hasselt, Belgium
| | - Guy Veraghtert
- Department of Electrical Engineering (ESAT), Research Division SCD, iMINDS Future Health Dept, KU Leuven, Kasteelpark Arenberg 10, B-3001, Leuven (Heverlee), Belgium
| | - Bart De Moor
- Department of Electrical Engineering (ESAT), Research Division SCD, iMINDS Future Health Dept, KU Leuven, Kasteelpark Arenberg 10, B-3001, Leuven (Heverlee), Belgium
| | - Joost Wauters
- Clinical Department of General Internal Medicine, Medical Intensive Care Unit, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Alexander Wilmer
- Clinical Department of General Internal Medicine, Medical Intensive Care Unit, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Marcus J Schultz
- Department of Intensive Care Medicine, Academic Medical Center, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
| | - Greet Van den Berghe
- Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Dieter Mesotten
- Clinical Division and Laboratory of Intensive Care Medicine, Academic Department of Cellular and Molecular Medicine, KU Leuven, University Hospitals Leuven, Herestraat 49, B-3000, Leuven, Belgium.
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Uyttendaele V, Dickson JL, Shaw G, Desaive T, Chase JG. Virtual Trials of the NICE-SUGAR Protocol: The Impact on Performance of Protocol and Protocol Compliance. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.ifacol.2017.08.1159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Uyttendaele V, Dickson JL, Shaw GM, Desaive T, Chase JG. Untangling glycaemia and mortality in critical care. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017. [PMID: 28645302 PMCID: PMC5482947 DOI: 10.1186/s13054-017-1725-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Background Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Vincent Uyttendaele
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. .,GIGA - In Silico Medicine, University of Liège, Allée du 6 Août 19, bâtiment B5a, 4000, Liège, Belgium.
| | - Jennifer L Dickson
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA - In Silico Medicine, University of Liège, Allée du 6 Août 19, bâtiment B5a, 4000, Liège, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
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Dickson JL, Stewart KW, Pretty CG, Flechet M, Desaive T, Penning S, Lambermont BC, Benyo B, Shaw GM, Chase JG. Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care. IEEE Trans Biomed Eng 2017; 65:1543-1553. [PMID: 28358672 DOI: 10.1109/tbme.2017.2686432] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). OBJECTIVE This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. METHODS Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross simulation) were compared. RESULTS Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 h due to different cohort starting BG and underlying SI. CONCLUSIONS AND SIGNIFICANCE Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.
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Hashemian SM, Martindale RG, Jamaati H, Amirsavadkouhi A, Mahmudi Azer S, Shadnoush M, Ardehali SH, Najafi A, Ahmadi A, Seyyedi SR, Mahmoodpoor A, Moradi O, Abbasi S, Hosseini S, Shahrami R, Abdi S, Sepehri Z, Omranirad B, Mohajerani SA, Rohani P, Sayyari A, Imani H, Velayati AA. An Iranian Consensus Document for Nutrition in Critically Ill Patients, Recommendations and Initial Steps toward Regional Guidelines. TANAFFOS 2017; 16:89-98. [PMID: 29308073 PMCID: PMC5749333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Seyed Mohammadreza Hashemian
- Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Hamidreza Jamaati
- Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Amirsavadkouhi
- Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Mahdi Shadnoush
- Department of Clinical Nutrition, Faculty of Nutrition and Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Hossein Ardehali
- Department of Critical Care, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Atabak Najafi
- Department of Anesthesiology and Critical Care Medicine, Faculty of Medicine, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Arezoo Ahmadi
- Department of Anesthesiology and Critical Care Medicine, Faculty of Medicine, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyyed Reza Seyyedi
- Lung Transplantation Research Center, Department of Cardiology, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ata Mahmoodpoor
- Department of Anesthesiology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Omid Moradi
- Department of Anesthesiology and Critical Care, Iran University of Medical Sciences, Rassol-e-Akram Complex Hospital, Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Saeed Abbasi
- Anesthesiology and Critical Care Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Saeed Hosseini
- School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Saeed Abdi
- Department of Gastroenterology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Sepehri
- Department of Internal Medicine, Zabol University of Medical Sciences, Zabol, Iran
| | - Babak Omranirad
- Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Amir Mohajerani
- Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pejman Rohani
- Department of Pediatric Gastroenterology, Hepathology and Nutrition, Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Aliakbar Sayyari
- Department of Pediatric Gastroenterology, Hepathology and Nutrition, Mofid Children Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein Imani
- School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Velayati
- Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
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