1
|
Teotia K, Jia Y, Link Woite N, Celi LA, Matos J, Struja T. Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations. J Biomed Inform 2024; 153:104643. [PMID: 38621640 PMCID: PMC11103268 DOI: 10.1016/j.jbi.2024.104643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 03/29/2024] [Accepted: 04/12/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). METHODS Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. RESULTS We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. CONCLUSION We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.
Collapse
Affiliation(s)
- Khushboo Teotia
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yueran Jia
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Naira Link Woite
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - João Matos
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Faculty of Engineering, University of Porto (FEUP), Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), Porto, Portugal.
| | - Tristan Struja
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical University Clinic, Kantonsspital Aarau, Aarau, Switzerland.
| |
Collapse
|
2
|
van Herpt TTW, van Rosmalen F, Hulsewé HPMG, van der Horst-Schrivers ANA, Driessen M, Jetten R, Zelis N, de Galan BE, van Kuijk SMJ, van der Horst ICC, van Bussel BCT. Hyperglycemia and glucose variability are associated with worse survival in mechanically ventilated COVID-19 patients: the prospective Maastricht Intensive Care Covid Cohort. Diabetol Metab Syndr 2023; 15:253. [PMID: 38057908 DOI: 10.1186/s13098-023-01228-1] [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] [Received: 08/05/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients. METHODS We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c. RESULTS Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20-1.06; ß 11.2 mg/dl; 95% CI 3.6-19.1; P = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37-1.33; ß 15.3; 95%CI 6.7-23.9; P = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24-1.23 mmol/l) ; (ß 13.3; 95%CI 4.3-22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25-0.98) mmol/l) (ß 6.7 (95% ci 4.5-17.6) mg/dl)). CONCLUSIONS Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.
Collapse
Affiliation(s)
- Thijs T W van Herpt
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands.
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.
| | - Frank van Rosmalen
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
| | - Hendrica P M G Hulsewé
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
| | - Anouk N A van der Horst-Schrivers
- Department of Emergency Medicine, Maastricht University Medical Centre +, Maastricht, The Netherlands
- Department of Endocrinology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Mariëlle Driessen
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
| | - Robin Jetten
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
| | - Noortje Zelis
- Department of Internal Medicine, Maastricht University Medical Centre +, Maastricht, The Netherlands
| | - Bastiaan E de Galan
- Department of Endocrinology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Bas C T van Bussel
- Department of Intensive Care Medicine, Maastricht University Medical Centre +, Debyelaan 25, 6229 HX, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
3
|
Teotia K, Jia Y, Woite NL, Celi LA, Matos J, Struja T. Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.12.23296568. [PMID: 37873163 PMCID: PMC10593024 DOI: 10.1101/2023.10.12.23296568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Objective Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). Methods Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. Results We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. Conclusion We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
|
6
|
Gunst J, Casaer MP, Preiser JC, Reignier J, Van den Berghe G. Toward nutrition improving outcome of critically ill patients: How to interpret recent feeding RCTs? Crit Care 2023; 27:43. [PMID: 36707883 PMCID: PMC9883882 DOI: 10.1186/s13054-023-04317-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
Although numerous observational studies associated underfeeding with poor outcome, recent randomized controlled trials (RCTs) have shown that early full nutritional support does not benefit critically ill patients and may induce dose-dependent harm. Some researchers have suggested that the absence of benefit in RCTs may be attributed to overrepresentation of patients deemed at low nutritional risk, or to a too low amino acid versus non-protein energy dose in the nutritional formula. However, these hypotheses have not been confirmed by strong evidence. RCTs have not revealed any subgroup benefiting from early full nutritional support, nor benefit from increased amino acid doses or from indirect calorimetry-based energy dosing targeted at 100% of energy expenditure. Mechanistic studies attributed the absence of benefit of early feeding to anabolic resistance and futile catabolism of extra provided amino acids, and to feeding-induced suppression of recovery-enhancing pathways such as autophagy and ketogenesis, which opened perspectives for fasting-mimicking diets and ketone supplementation. Yet, the presence or absence of an anabolic response to feeding cannot be predicted or monitored and likely differs over time and among patients. In the absence of such monitor, the value of indirect calorimetry seems obscure, especially in the acute phase of illness. Until now, large feeding RCTs have focused on interventions that were initiated in the first week of critical illness. There are no large RCTs that investigated the impact of different feeding strategies initiated after the acute phase and continued after discharge from the intensive care unit in patients recovering from critical illness.
Collapse
Affiliation(s)
- Jan Gunst
- grid.5596.f0000 0001 0668 7884Clinical Division and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Michael P. Casaer
- grid.5596.f0000 0001 0668 7884Clinical Division and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Jean-Charles Preiser
- grid.4989.c0000 0001 2348 0746Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Jean Reignier
- grid.4817.a0000 0001 2189 0784Service de Médecine Intensive Réanimation, Centre Hospitalier Universitaire de Nantes, Université de Nantes, Nantes, France
| | - Greet Van den Berghe
- grid.5596.f0000 0001 0668 7884Clinical Division and Laboratory of Intensive Care Medicine, Department of Cellular and Molecular Medicine, KU Leuven, Herestraat 49, 3000 Leuven, Belgium
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Lou R, Jiang L, Wang M, Zhu B, Jiang Q, Wang P. Association Between Glycemic Gap and Mortality in Critically Ill Patients with Diabetes. J Intensive Care Med 2022; 38:42-50. [PMID: 35611506 DOI: 10.1177/08850666221101856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVES Dysglycemia is associated with poor outcomes in critically ill patients,which is uncertain in patients with diabetes regarding to the situation of glucose control before hospitalization. This study was aimed to investigate the effect of the difference between the level of blood glucose during ICU stay and before admission to ICU upon the outcomes of critically ill patients with diabetes. METHOD Patients with diabetes expected to stay for more than 24hs were enrolled, HbA1c was converted to A1C-derived average glucose (ADAG) by the equation: ADAG = [ (HbA1c * 28.7) - 46.7 ] * 18-1, blood glucose were measured four times a day during the first 7 days after admission, the mean glucose level(MGL) and SOFA (within 3, 5, and 7days) were calculated for each person, GAPadm and GAPmean was calculated as admission blood glucose and MGL minus ADAG, the incidence of moderate hypoglycemia(MH), severe hypoglycemia (SH), total dosage of glucocorticoids and average daily dosage of insulin, duration of renal replacement therapy(RRT), ventilator-free hours, and non-ICU days were also collected. Patients were divided into survival group and nonsurvival group according to survival or not at 28-day, the relationship between GAP and mortality were analyzed. RESULTS 431 patients were divided into survival group and nonsurvival group. The two groups had a comparable level of HbA1c, the nonsurvivors had greater APACHE II, SOFA, GAPadm, GAPmean-3, GAPmean-5, GAPmean-7 and higher MH and SH incidences. Less duration of ventilator-free, non-ICU stay and longer duration of RRT were recorded in the nonsurvival group. GAPmean-5 had the greatest predictive power with an AUC of 0.807(95%CI: 0.762-0.851), the cut-off value was 3.6 mmol/L (sensitivity 77.7% and specificity 76.6%). The AUC was increased to 0.852(95%CI: 0.814-0.889) incorporated with SOFA5 (NRI = 11.34%). CONCLUSION Glycemic GAP between the MGL within 5 days and ADAG was independently associated with 28-day mortality of critically ill patients with diabetes. The predictive power was optimized with addition of SOFA5.
Collapse
Affiliation(s)
- Ran Lou
- Department of Crtical Care Medicine, 71044Xuanwu Hospital Capital Medical University, 45Changchun Street, Xicheng District, Beijing 100053, China
| | - Li Jiang
- Department of Crtical Care Medicine, 71044Xuanwu Hospital Capital Medical University, 45Changchun Street, Xicheng District, Beijing 100053, China
| | - Meiping Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmenwai, Fengtai District, Beijing 100069, China
| | - Bo Zhu
- Department of Critical Care Medicine, 71043Fu Xing Hospital, Capital Medical University, 20A Fuxingmenwai Street, Xicheng District, Beijing 100038, China
| | - Qi Jiang
- Department of Critical Care Medicine, 71043Fu Xing Hospital, Capital Medical University, 20A Fuxingmenwai Street, Xicheng District, Beijing 100038, China
| | - Peng Wang
- Department of Critical Care Medicine, 71043Fu Xing Hospital, Capital Medical University, 20A Fuxingmenwai Street, Xicheng District, Beijing 100038, China
| |
Collapse
|
9
|
Thouy F, Bohé J, Souweine B, Abidi H, Quenot JP, Thiollière F, Dellamonica J, Preiser JC, Timsit JF, Brunot V, Klich A, Sedillot N, Tchenio X, Roudaut JB, Mottard N, Hyvernat H, Wallet F, Danin PE, Badie J, Jospe R, Morel J, Mofredj A, Fatah A, Drai J, Mialon A, Ait Hssain A, Lautrette A, Fontaine E, Vacheron CH, Maucort-Boulch D, Klouche K, Dupuis C. Impact of prolonged requirement for insulin on 90-day mortality in critically ill patients without previous diabetic treatments: a post hoc analysis of the CONTROLING randomized control trial. Crit Care 2022; 26:138. [PMID: 35578303 PMCID: PMC9109308 DOI: 10.1186/s13054-022-04004-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stress hyperglycemia can persist during an intensive care unit (ICU) stay and result in prolonged requirement for insulin (PRI). The impact of PRI on ICU patient outcomes is not known. We evaluated the relationship between PRI and Day 90 mortality in ICU patients without previous diabetic treatments. METHODS This is a post hoc analysis of the CONTROLING trial, involving 12 French ICUs. Patients in the personalized glucose control arm with an ICU length of stay ≥ 5 days and who had never previously received diabetic treatments (oral drugs or insulin) were included. Personalized blood glucose targets were estimated on their preadmission usual glycemia as estimated by their glycated A1c hemoglobin (HbA1C). PRI was defined by insulin requirement. The relationship between PRI on Day 5 and 90-day mortality was assessed by Cox survival models with inverse probability of treatment weighting (IPTW). Glycemic control was defined as at least one blood glucose value below the blood glucose target value on Day 5. RESULTS A total of 476 patients were included, of whom 62.4% were male, with a median age of 66 (54-76) years. Median values for SAPS II and HbA1C were 50 (37.5-64) and 5.7 (5.4-6.1)%, respectively. PRI was observed in 364/476 (72.5%) patients on Day 5. 90-day mortality was 23.1% in the whole cohort, 25.3% in the PRI group and 16.1% in the non-PRI group (p < 0.01). IPTW analysis showed that PRI on Day 5 was not associated with Day 90 mortality (IPTWHR = 1.22; CI 95% 0.84-1.75; p = 0.29), whereas PRI without glycemic control was associated with an increased risk of death at Day 90 (IPTWHR = 3.34; CI 95% 1.26-8.83; p < 0.01). CONCLUSION In ICU patients without previous diabetic treatments, only PRI without glycemic control on Day 5 was associated with an increased risk of death. Additional studies are required to determine the factors contributing to these results.
Collapse
Affiliation(s)
- François Thouy
- Service de Médecine Intensive Réanimation, CHU Hôpital Gabriel-Montpied, 58 rue Montalembert, 63000, Clermont Ferrand, France
| | - Julien Bohé
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Bertrand Souweine
- Service de Médecine Intensive Réanimation, CHU Hôpital Gabriel-Montpied, 58 rue Montalembert, 63000, Clermont Ferrand, France
| | - Hassane Abidi
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Jean-Pierre Quenot
- Service de Médecine Intensive Réanimation, CHU Dijon Bourgogne, Dijon, France
| | - Fabrice Thiollière
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Jean Dellamonica
- Service de Médecine Intensive Réanimation, CHU Hôpital de L'Archet, Nice, France.,UR2CA Unité de Recherche Clinique Côte d'Azur, Université Côte d'Azur, Nice, France
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Jean-François Timsit
- Service de Réanimation Médicale et des Maladies Infectieuses, Université Paris Diderot/Hôpital Bichat, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Vincent Brunot
- Service de Médecine Intensive Réanimation, Centre Hospitalier Universitaire, Montpellier, France
| | - Amna Klich
- Service de Biostatistique - Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France.,UMR5558, Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, CNRS, Villeurbanne, France
| | | | - Xavier Tchenio
- Service de Réanimation, Hôpital Fleyriat, Bourg en Bresse, France
| | | | - Nicolas Mottard
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Hervé Hyvernat
- Service de Médecine Intensive Réanimation, CHU Hôpital de L'Archet, Nice, France
| | - Florent Wallet
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Pierre-Eric Danin
- Service de Réanimation Médico-Chirurgicale, CHU Hôpital de L'Archet, Nice, France
| | - Julio Badie
- Service de Réanimation Médico-Chirurgicale, CHU Hôpital de L'Archet, Nice, France
| | - Richard Jospe
- Département d'Anesthésie et Réanimation, CHU, Saint Etienne, France
| | - Jérôme Morel
- Département d'Anesthésie et Réanimation, CHU, Saint Etienne, France
| | - Ali Mofredj
- Service de Réanimation, Hôpital du pays Salonais, Salon de Provence, France
| | - Abdelhamid Fatah
- Service de Réanimation, Hôpital Pierre Oudot, Bourgoin Jallieu, France
| | - Jocelyne Drai
- Laboratoire de Biochimie, Groupement Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France
| | - Anne Mialon
- Laboratoire de Biochimie, Groupement Hospitalier Lyon Sud, Hospices Civils de Lyon, Lyon, France
| | - Ali Ait Hssain
- Service de Médecine Intensive Réanimation, CHU Hôpital Gabriel-Montpied, 58 rue Montalembert, 63000, Clermont Ferrand, France
| | - Alexandre Lautrette
- Département d'Anesthésie et Réanimation, Centre Jean Perrin, Clermont Ferrand, France
| | - Eric Fontaine
- INSERM U1055 - LBFA, University Grenoble Alpes, Grenoble, France
| | - Charles-Hervé Vacheron
- Service d'Anesthésie-Réanimation-Médecine Intensive, Groupement hospitalier sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Delphine Maucort-Boulch
- Service de Biostatistique - Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Kada Klouche
- Service de Médecine Intensive Réanimation, Centre Hospitalier Universitaire, Montpellier, France
| | - Claire Dupuis
- Service de Médecine Intensive Réanimation, CHU Hôpital Gabriel-Montpied, 58 rue Montalembert, 63000, Clermont Ferrand, France.
| |
Collapse
|
10
|
Ma H, Yu G, Wang Z, Zhou P, Lv W. Association between dysglycemia and mortality by diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study. Acta Diabetol 2022; 59:461-470. [PMID: 34761326 PMCID: PMC8917030 DOI: 10.1007/s00592-021-01818-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 02/08/2023]
Abstract
AIMS Dysglycemia, including the three domains hyperglycemia, hypoglycemia, and increased glycemic variability (GV), is associated with high mortality among critically ill patients. However, this association differs by diabetes status, and reports in this regard are limited. This study aimed to evaluate the associations between the three dysglycemia domains and mortality in critically ill patients by diabetes status and determined the contributing factors for dysglycemia. METHODS This retrospective study included 958 critically ill patients (admitted to the ICU) with or without DM. Dysglycemia was defined as abnormality of any of the three dimensions. We evaluated the effects of the three domains of glucose control on mortality using binary logistic regression and then adjusted for confounders. The associations between dysglycemia and other variables were investigated using cumulative logistic regression analysis. RESULT GV independently and similarly affected mortality in both groups after adjustment for confounders (DM: odds ratio [OR], 1.05; 95% confidence interval [CI]: 1.03-1.08; p <0.001; non-DM: OR, 1.07; 95% CI, 1.03-1.11; p = 0.002). Hypoglycemia was strongly associated with ICU mortality among patients without DM (3.12; 1.76-5.53; p <0.001) and less so among those with DM (1.18; 0.49-2.83; p = 0.72). Hyperglycemia was non-significantly associated with mortality in both groups. However, the effects of dysglycemia seemed cumulative. The factors contributing to dysglycemia included disease severity, insulin treatment, glucocorticoid use, serum albumin level, total parenteral nutrition, duration of diabetes, elevated procalcitonin level, and need for mechanical ventilation and renal replacement therapy. CONCLUSION The association between the three dimensions of dysglycemia and mortality varied by diabetes status. Dysglycemia in critical patients is associated with excess mortality; however, glucose management in patients should be specific to the patient's need considering the diabetes status and broader dimensions. The identified factors for dysglycemia could be used for risk assessment in glucose management requirement in critically ill patients, which may improve clinical outcomes.
Collapse
Affiliation(s)
- Haoming Ma
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Tianhe District, Guangzhou City, Guangdong Province, China
| | - Guo Yu
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Tianhe District, Guangzhou City, Guangdong Province, China
| | - Ziwen Wang
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Tianhe District, Guangzhou City, Guangdong Province, China
| | - Peiru Zhou
- Health Management Centre, The First Affiliated Hospital of Jinan University, No. 613, West Huangpu Avenue, Tianhe District, Guangzhou City, Guangdong Province, China.
| | - Weitao Lv
- Division of Critical Care, The First Affiliated Hospital of Jinan University, No. 613, West Huangpu Avenue, Tianhe District, Guangzhou City, Guangdong Province, China.
| |
Collapse
|
11
|
Lou R, Jiang L, Zhu B. Effect of glycemic gap upon mortality in critically ill patients with diabetes. J Diabetes Investig 2021; 12:2212-2220. [PMID: 34075715 PMCID: PMC8668057 DOI: 10.1111/jdi.13606] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 11/29/2022] Open
Abstract
AIMS/INTRODUCTION Hyperglycemia, hypoglycemia, and blood glucose fluctuation are associated with the outcome in critically ill patients, but the target of blood glucose control is debatable especially in patients with diabetes regarding the status of blood glucose control before admission to ICU. This study aimed to investigate the association between the glycemic gap which is calculated as the mean blood glucose level during the first 7 days after admission to ICU minus the A1C-derived average glucose and the outcome of critically ill patients with diabetes. METHOD This study was undertaken in two intensive care units (ICUs) with a total of 30 beds. Patients with diabetes who were expected to stay for more than 24 h were enrolled, the HbA1c was tested within 3 days after admission and converted to the A1C-derived average glucose (ADAG) by the equation: ADAG = [(HbA1c * 28.7) - 46.7 ] * 18-1 , arterial blood glucose measurements were four per day routinely during the first 7 days after admission, the APACHE II score within the first 24 h, the mean blood glucose level (MGL), standard deviation (SD), and coefficient of variation (CV) during the first 7 days were calculated for each person, the GAPadm and GAPmean were calculated as the admission blood glucose and MGL minus the ADAG, respectively, the incidence of moderate hypoglycemia (MH) and severe hypoglycemia (SH), the total dosage of glucocorticoids and average daily dosage of insulin within 7 days, the duration of renal replacement therapy (RRT), ventilator-free hours, and non-ICU stay days within 28 days were also collected. The enrolled patients were divided into a survival group and a nonsurvival group according to survival or not at 28 days and 1 year after admission, and the relationship between parameters derived from blood glucose and mortality in the enrolled critically ill patients was explored. RESULTS Five hundred and two patients were enrolled and divided into a survival group (n = 310) and a nonsurvival group (n = 192). It was shown that the two groups had a comparable level of HbA1c, the nonsurvivors had a greater APACHE II, MGL, SD, CV, GAPadm , GAPmean , and a higher incidence of hypoglycemia. A lesser duration of ventilator-free, non-ICU stay, and a longer duration of RRT were recorded in the nonsurvival group, who received a lower carbohydrate intake, a higher daily dosage of insulin and glucocorticoid. GAPmean had the greatest predictive power with an AUC of 0.820 (95%CI: 0.781-0.850), the cut-off value was 3.60 mmol/L (sensitivity 78.2% and specificity 77.3%). Patients with a low GAPmean tended to survive longer than the high GAPmean group 1 year after admission. CONCLUSIONS Glycemic GAP between the mean level of blood glucose within the first 7 days after admission to ICU and the A1C-derived average glucose was independently associated with a 28 day mortality of critically ill patients with diabetes, the predictive power extended to 1 year. The incidence of hypoglycemia was associated with mortality either.
Collapse
Affiliation(s)
- Ran Lou
- Department of Critical Care MedicineXuanwu HospitalCapital Medical UniversityBeijingChina
| | - Li Jiang
- Department of Critical Care MedicineXuanwu HospitalCapital Medical UniversityBeijingChina
| | - Bo Zhu
- Department of Critical Care MedicineFu Xing HospitalCapital Medical UniversityBeijingChina
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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.
Collapse
|
14
|
Emidio AC, Faria R, Bispo B, Vaz-Pinto V, Messias A, Meneses-Oliveira C. GlucoSTRESS - A project to optimize glycemic control in a level C (III) Portuguese intensive care unit. Rev Bras Ter Intensiva 2021; 33:138-145. [PMID: 33886863 PMCID: PMC8075347 DOI: 10.5935/0103-507x.20210015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 06/17/2020] [Indexed: 11/20/2022] Open
Abstract
Objective To double the percentage of time within the 100 - 180mg/dL blood glucose range in the first three months following a phased implementation of a formal education program, and then, of an insulin therapy protocol, without entailing an increased incidence of hypoglycemia. Methods The pre-intervention glycemic control was assessed retrospectively. Next, were carried out the implementation of a formal education program, distribution of manual algorithms for intravenous insulin therapy - optimized by the users, based on the modified Yale protocol - and informal training of the nursing staff. The use of electronic blood glucose control systems was supported, and the results were recorded prospectively. Results The first phase of the program (formal education) lead to improvement of the time within the euglycemic interval (28% to 37%). In the second phase, euglycemia was achieved 66% of the time, and the incidence of hypoglycemia was decreased. The percentage of patients on intravenous insulin infusion at 48 hours from admission increased from 6% to 35%. Conclusion The phased implementation of a formal education program, fostering the use of electronic insulin therapy protocols and dynamic manuals, received good adherence and has shown to be safe and effective for blood glucose control in critically ill patients, with a concomitant decrease in hypoglycemia.
Collapse
Affiliation(s)
- Ana Catarina Emidio
- Serviço de Medicina Interna, Centro Hospitalar de Setúbal - Setúbal, Portugal
| | - Rita Faria
- Serviço de Medicina Intensiva, Hospital Beatriz Ângelo - Loures, Lisboa, Portugal
| | - Bruno Bispo
- Serviço de Medicina Intensiva, Hospital Beatriz Ângelo - Loures, Lisboa, Portugal
| | - Vítor Vaz-Pinto
- Serviço de Medicina Intensiva, Hospital Beatriz Ângelo - Loures, Lisboa, Portugal
| | - António Messias
- Serviço de Medicina Intensiva, Hospital Beatriz Ângelo - Loures, Lisboa, Portugal
| | | |
Collapse
|
15
|
Fitzgerald O, Perez-Concha O, Gallego B, Saxena MK, Rudd L, Metke-Jimenez A, Jorm L. Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU. J Am Med Inform Assoc 2021; 28:1642-1650. [PMID: 33871017 PMCID: PMC8324237 DOI: 10.1093/jamia/ocab060] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/10/2021] [Accepted: 03/22/2021] [Indexed: 12/20/2022] Open
Abstract
Objective Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. Materials and Methods Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. Results Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%. Discussion ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. Conclusion We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.
Collapse
Affiliation(s)
- Oisin Fitzgerald
- Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW, Australia
| | - Oscar Perez-Concha
- Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW, Australia
| | - Blanca Gallego
- Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW, Australia
| | - Manoj K Saxena
- The George Institute for Global Health, UNSW Sydney, Sydney, NSW, Australia
| | - Lachlan Rudd
- Data and Analytics, eHealth NSW, Chatswood, NSW, Australia
| | | | - Louisa Jorm
- Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW, Australia
| |
Collapse
|
16
|
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.
Collapse
Affiliation(s)
| | | | | | - Geoffrey M Shaw
- Otago University School of Medicine; and ICU, Christchurch Hospital; Christchurch, New Zealand.
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
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.
Collapse
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
| |
Collapse
|
20
|
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]
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
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.
Collapse
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.
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Huang CT, Lue JH, Cheng TH, Tsai YJ. Glycemic control with insulin attenuates sepsis-associated encephalopathy by inhibiting glial activation via the suppression of the nuclear factor kappa B and mitogen-activated protein kinase signaling pathways in septic rats. Brain Res 2020; 1738:146822. [PMID: 32272096 DOI: 10.1016/j.brainres.2020.146822] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/03/2020] [Accepted: 04/04/2020] [Indexed: 12/11/2022]
Abstract
Sepsis-associated encephalopathy (SAE) is frequently encountered in critically ill patients. Hyperglycemia is a common phenomenon among patients with sepsis, and glycemic control improves patient outcomes. Therefore, here, we aimed to explore whether glycemic control using insulin inhibits the pro-inflammatory cytokine response and glial activation in the cerebrum and is concomitantly associated with the relief of SAE. Using cecal ligation and puncture (CLP), sepsis was induced in male Sprague-Dawley rats. The CLP rats were administered intravenous glucose or subjected to subcutaneous insulin implant within the first hour after CLP. The survival rate, blood glucose (BG) values, and behavioral expression were assessed daily for 5 days after CLP. At day 5 after CLP, electroencephalography (EEG) recordings and blood-brain barrier (BBB) permeability testing were performed. Immunohistochemistry, immunoblotting, and enzyme-linked immunosorbent assays were used to evaluate glial activation and the pro-inflammatory cytokine response qualitatively and quantitatively, respectively. The glucose-treated CLP rats (BG > 390 mg/dL) exhibited a decline in survival rate; insensitivity to mechanical and thermal stimuli; slowed EEG activity; and an increase in BBB permeability, pro-inflammatory cytokine (TNF-α, IL-1β, and IL-6) levels, and glial activation (astrocytes and microglia) in the cerebral tissues compared with CLP rats (BG ~ 270 mg/dL). Double-immunofluorescence showed that activated astrocytes and microglia co-expressed phosphorylated nuclear factor kappa B and mitogen-activated protein kinases, respectively. Furthermore, glycemic control using insulin therapy maintained the BG at 120-160 mg/dL and inhibited the production of pro-inflammatory cytokines and glial activation in the cerebrum of septic rats. In addition, the survival rate, sensory threshold, EEG activity, and BBB permeability recovered to near-normal levels in septic rats after insulin therapy. Taken together, the results of this study elucidated the pathophysiological alterations in brains subjected to sepsis, especially regarding glycemic control. These findings improve our understanding of SAE and support the importance of glycemic control in sepsis.
Collapse
Affiliation(s)
- Chun-Ta Huang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, National Taiwan University, Taipei, Taiwan
| | - June-Horng Lue
- Department of Anatomy and Cell Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tong-Hong Cheng
- Department of Internal Medicine, Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan
| | - Yi-Ju Tsai
- Graduate Institute of Biomedical and Pharmaceutical Science, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.
| |
Collapse
|
25
|
Chao WC, Tseng CH, Wu CL, Shih SJ, Yi CY, Chan MC. Higher glycemic variability within the first day of ICU admission is associated with increased 30-day mortality in ICU patients with sepsis. Ann Intensive Care 2020; 10:17. [PMID: 32034567 PMCID: PMC7007493 DOI: 10.1186/s13613-020-0635-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Accepted: 01/30/2020] [Indexed: 12/29/2022] Open
Abstract
Background High glycemic variability (GV) is common in critically ill patients; however, the prevalence and mortality association with early GV in patients with sepsis remains unclear. Methods This retrospective cohort study was conducted in a medical intensive care unit (ICU) in central Taiwan. Patients in the ICU with sepsis between January 2014 and December 2015 were included for analysis. All of these patients received protocol-based management, including blood sugar monitoring every 2 h for the first 24 h of ICU admission. Mean amplitude of glycemic excursions (MAGE) and coefficient of variation (CoV) were used to assess GV. Results A total of 452 patients (mean age 71.4 ± 14.7 years; 76.7% men) were enrolled for analysis. They were divided into high GV (43.4%, 196/452) and low GV (56.6%, 256/512) groups using MAGE 65 mg/dL as the cut-off point. Patients with high GV tended to have higher HbA1c (6.7 ± 1.8% vs. 5.9 ± 0.9%, p < 0.01) and were more likely to have diabetes mellitus (DM) (50.0% vs. 23.4%, p < 0.01) compared with those in the low GV group. Kaplan–Meier analysis showed that a high GV was associated with increased 30-day mortality (log-rank test, p = 0.018). The association remained strong in the non-DM (log-rank test, p = 0.035), but not in the DM (log-rank test, p = 0.254) group. Multivariate Cox proportional hazard regression analysis identified that high APACHE II score (adjusted hazard ratio (aHR) 1.045, 95% confidence interval (CI) 1.013–1.078), high serum lactate level at 0 h (aHR 1.009, 95% CI 1.003–1.014), having chronic airway disease (aHR 0.478, 95% CI 0.302–0.756), high mean day 1 glucose (aHR 1.008, 95% CI 1.000–1.016), and high MAGE (aHR 1.607, 95% CI 1.008–2.563) were independently associated with increased 30-day mortality. The association with 30-day mortality remained consistent when using CoV to assess GV. Conclusions We found that approximately 40% of the septic patients had a high early GV, defined as MAGE > 65 mg/dL. Higher GV within 24 h of ICU admission was independently associated with increased 30-day mortality. These findings highlight the need to monitor GV in septic patients early during an ICU admission.
Collapse
Affiliation(s)
- Wen-Cheng Chao
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan.,Department of Critical Care Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan
| | - Chien-Hua Tseng
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chieh-Liang Wu
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan.,Center of Quality Management, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan.,Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Sou-Jen Shih
- Department of Nursing, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan
| | - Chi-Yuan Yi
- Department of Nursing, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan
| | - Ming-Cheng Chan
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan. .,Division of Critical Care and Respiratory Therapy, Department of Internal Medicine, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Taichung, 40705, Taiwan. .,Central Taiwan University of Science and Technology, Taichung, Taiwan. .,The College of Science, Tunghai University, Taichung, 40704, Taiwan.
| |
Collapse
|
26
|
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.
Collapse
|
27
|
Abstract
PURPOSE OF REVIEW To provide an update of glycemic management during metabolic stress related to surgery or critical illness. RECENT FINDINGS There is a clear association between severe hyperglycemia, hypoglycemia, and high glycemic variability and poor outcomes of postoperative or critically ill patients. However, the impressive beneficial effects of tight glycemic management (TGM) by intensive insulin therapy reported in one study were never reproduced. Hence, the recommendation of TGM is now replaced by more liberal blood glucose (BG) targets (< 180 mg/dL or 10 mM). Recent data support the concept of targeting individualized blood glucose (BG) values according to the presence of diabetes mellitus/chronic hyperglycemia, the presence of brain injury, and the time from injury. A more liberal glycemic management goal is currently advised during metabolic stress and could be switched to individualized glycemic management once validated by prospective trials.
Collapse
Affiliation(s)
- Wasineenart Mongkolpun
- Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Bruna Provenzano
- Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Route de Lennik, 808, 1070, Brussels, Belgium.
| |
Collapse
|
28
|
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.
Collapse
|
29
|
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.
Collapse
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
| | | |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
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.
Collapse
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.
| |
Collapse
|
32
|
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]
|
33
|
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.
Collapse
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.
| |
Collapse
|
34
|
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]
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
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.
Collapse
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
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
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.
Collapse
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
| |
Collapse
|
39
|
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
| |
Collapse
|