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Juneja D, Deepak D, Nasa P. What, why and how to monitor blood glucose in critically ill patients. World J Diabetes 2023; 14:528-538. [PMID: 37273246 PMCID: PMC10236998 DOI: 10.4239/wjd.v14.i5.528] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/17/2023] [Accepted: 03/07/2023] [Indexed: 05/15/2023] Open
Abstract
Critically ill patients are prone to high glycemic variations irrespective of their diabetes status. This mandates frequent blood glucose (BG) monitoring and regulation of insulin therapy. Even though the most commonly employed capillary BG monitoring is convenient and rapid, it is inaccurate and prone to high bias, overestimating BG levels in critically ill patients. The targets for BG levels have also varied in the past few years ranging from tight glucose control to a more liberal approach. Each of these has its own fallacies, while tight control increases risk of hypoglycemia, liberal BG targets make the patients prone to hyperglycemia. Moreover, the recent evidence suggests that BG indices, such as glycemic variability and time in target range, may also affect patient outcomes. In this review, we highlight the nuances associated with BG monitoring, including the various indices required to be monitored, BG targets and recent advances in BG monitoring in critically ill patients.
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Affiliation(s)
- Deven Juneja
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110017, India
| | - Desh Deepak
- Department of Critical Care, King's College Hospital, Dubai 340901, United Arab Emirates
| | - Prashant Nasa
- Department of Critical Care, NMC Speciality Hospital, Dubai 7832, United Arab Emirates
- Department of Critical Care, College of Medicine and Health Sciences, Al Ain 15551, United Arab Emirates
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Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artif Intell Gastroenterol 2022; 3:66-79. [DOI: 10.35712/aig.v3.i2.66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
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van den Boorn M, Lagerburg V, van Steen SCJ, Wedzinga R, Bosman RJ, van der Voort PHJ. The development of a glucose prediction model in critically ill patients. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106105. [PMID: 33979752 DOI: 10.1016/j.cmpb.2021.106105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
PURPOSE The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values. METHODS Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG). RESULTS The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions. CONCLUSION In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system.
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Affiliation(s)
- M van den Boorn
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands.
| | - V Lagerburg
- OLVG, Medical Physics, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - S C J van Steen
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Department of Endocrinology, Meibergdreef 9, Amsterdam, Netherlands
| | - R Wedzinga
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands; OLVG, Medical Physics, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - R J Bosman
- OLVG, Department of Intensive Care, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - P H J van der Voort
- University of Groningen, University Medical Center Groningen, Department of Intensive Care, Hanzeplein 2, 9713GZ Groningen, The Netherlands
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Lal A, Haque N, Lee J, Katta SR, Maranda L, George S, Trivedi N. Optimal Blood Glucose Monitoring Interval for Insulin Infusion in Critically Ill Non-Cardiothoracic Patients: A Pilot Study. ACTA BIO-MEDICA : ATENEI PARMENSIS 2021; 92:e2021036. [PMID: 33682835 PMCID: PMC7975947 DOI: 10.23750/abm.v92i1.9083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 01/05/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE The American Diabetes Association and the Society of Critical Care Medicine recommend monitoring blood glucose (BG) every 1-2 hours in patients receiving insulin infusion to guide titration of insulin infusion to maintain serum glucose in the target range; however, this is based on weak evidence. We evaluated the compliance of hourly BG monitoring and relation of less frequent BG monitoring to glycemic status. MATERIALS AND METHODS Retrospective chart review performed on 56 consecutive adult patients who received intravenous insulin infusion for persistent hyperglycemia in the ICU at Saint Vincent Hospital, a tertiary care community hospital an urban setting in Northeast region of USA. The frequency of fingerstick blood glucose (FSBG) readings was reviewed for compliance with hourly FSBG monitoring per protocol and the impact of FSBG testing at different time intervals on the glycemic status. Depending on time interval of FSBG monitoring, the data was divided into three groups: Group A (<90 min), Group B (91-179 min) and Group C (≥180 min). RESULTS The mean age was 69 years (48% were males), 77% patients had preexisting type 2 diabetes mellitus (T2DM). The mean MPM II score was 41. Of the 1411 readings for BG monitoring on insulin infusion, 467 (33%) were in group A, 806 (57%) in group B and 138 (10%) in group C; hourly BG monitoring compliance was 12.6%. The overall glycemic status was similar among all groups. There were 14 (0.99%) hypoglycemic episodes observed. The rate of hypoglycemic episodes was similar in all three groups (p=0.55). CONCLUSION In patients requiring insulin infusion for sustained hyperglycemia in ICU, the risk of hypoglycemic episodes was not significantly different with less frequent BG monitoring. The compliance to hourly blood glucose monitoring and ICU was variable, and hypoglycemic episodes were similar across the groups despite the variation in monitoring.
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Affiliation(s)
| | - Nurul Haque
- Department of Medicine Merit Health River Region Hospital 2100 US-61, Vicksburg, MS 39183.
| | - Jennifer Lee
- Clinical Pharmacy Coordinator, Critical Care Department of Pharmacy 123 Summer Street Saint Vincent Hospital, Worcester, Massachusetts. USA 01608.
| | - Sai Ramya Katta
- Clinical Pharmacy Coordinator, Critical Care Department of Pharmacy 123 Summer Street Saint Vincent Hospital, Worcester, Massachusetts. USA 01608.
| | - Louise Maranda
- Department of Biostatistics University of Massachusetts Medical School.
| | - Susan George
- Clinical Associate Professor of Medicine University of Massachusetts Medical School Program Director, Internal Medicine Residency Chair, Department of Medicine Performance Improvement Committee 123 Summer Street Saint Vincent Hospital, Worcester, Massachusetts..
| | - Nitin Trivedi
- Director, Division of Endocrinology Associate Program Director, Internal Medicine Residency Department of Medicine, Saint Vincent Hospital Associate Professor of Medicine University of Massachusetts Medical School 123 Summer Street Saint Vincent Hospital, Worcester, Massachusetts.
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Braithwaite SS, Clark LP, Idrees T, Qureshi F, Soetan OT. Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion. Curr Diab Rep 2018; 18:26. [PMID: 29582176 DOI: 10.1007/s11892-018-0994-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE OF REVIEW This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier. RECENT FINDINGS Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.
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Affiliation(s)
- Susan Shapiro Braithwaite
- , 1135 Ridge Road, Wilmette, IL, 60091, USA.
- Endocrinology Consults and Care, S.C, 3048 West Peterson Ave, Chicago, IL, 60659, USA.
| | - Lisa P Clark
- Presence Saint Francis Hospital, 355 Ridge Ave, Evanston, IL, 60202, USA
| | - Thaer Idrees
- Presence Saint Joseph Hospital, 2900 N. Lakeshore Dr, Chicago, IL, 60657, USA
| | - Faisal Qureshi
- Presence Saint Joseph Hospital, 2800 N Sheridan Road Suite 309, Chicago, IL, 60657, USA
| | - Oluwakemi T Soetan
- Presence Saint Joseph Hospital, 2900 N. Lakeshore Dr, Chicago, IL, 60657, USA
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Fedosov V, Dziadzko M, Dearani JA, Brown DR, Pickering BW, Herasevich V. Decision Support Tool to Improve Glucose Control Compliance After Cardiac Surgery. AACN Adv Crit Care 2017; 27:274-282. [PMID: 27959310 DOI: 10.4037/aacnacc2016634] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Hyperglycemia control is associated with improved outcomes in patients undergoing cardiac surgery. The Surgical Care Improvement Project metric (SCIP-inf-4) was introduced as a performance measure in surgical patients and included hyperglycemia control. Compliance with the SCIP-inf-4 metric remains suboptimal. A novel real-time decision support tool (DST) with guaranteed feedback that is based on the existing electronic medical record system was developed at a tertiary academic center. Implementation of the DST increased the compliance rate with the SCIP-inf-4 from 87.3% to 96.5%. Changes in tested clinical outcomes were not observed with improved metric compliance. This new framework can serve as a backbone for development of quality control processes for other metrics. Further and, ideally, multicenter studies are required to test if implementation of electronic DSTs will translate into improved resource utilization and outcomes for patients.
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Affiliation(s)
- Vitali Fedosov
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
| | - Mikhail Dziadzko
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
| | - Joseph A Dearani
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
| | - Daniel R Brown
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
| | - Brian W Pickering
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
| | - Vitaly Herasevich
- Vitali Fedosov is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Mikhail Dziadzko is Research Fellow, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Joseph A. Dearani is Professor of Surgery, Department of Surgery, Mayo Clinic, Rochester, Minnesota. Daniel R. Brown is Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Brian W. Pickering is Assistant Professor of Anesthesiology, Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota. Vitaly Herasevich is Associate Professor of Anesthesiology and Medicine, Department of Anesthesiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905
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Blaha J, Barteczko-Grajek B, Berezowicz P, Charvat J, Chvojka J, Grau T, Holmgren J, Jaschinski U, Kopecky P, Manak J, Moehl M, Paddle J, Pasculli M, Petersson J, Petros S, Radrizzani D, Singh V, Starkopf J. Space GlucoseControl system for blood glucose control in intensive care patients--a European multicentre observational study. BMC Anesthesiol 2016; 16:8. [PMID: 26801983 PMCID: PMC4722682 DOI: 10.1186/s12871-016-0175-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2015] [Accepted: 01/20/2016] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Glycaemia control (GC) remains an important therapeutic goal in critically ill patients. The enhanced Model Predictive Control (eMPC) algorithm, which models the behaviour of blood glucose (BG) and insulin sensitivity in individual ICU patients with variable blood samples, is an effective, clinically proven computer based protocol successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols. eMPC has been integrated into the B.Braun Space GlucoseControl system (SGC), which allows direct data communication between pumps and microprocessor. The present study was undertaken to assess the clinical performance and safety of the SGC for glycaemia control in critically ill patients under routine conditions in different ICU settings and with various nutritional protocols. METHODS The study endpoints were the percentage of time the BG was within the target range 4.4 - 8.3 mmol.l(-1), the frequency of hypoglycaemic episodes, adherence to the advice of the SGC and BG measurement intervals. BG was monitored, and insulin was given as a continuous infusion according to the advice of the SGC. Nutritional management (enteral, parenteral or both) was carried out at the discretion of each centre. RESULTS 17 centres from 9 European countries included a total of 508 patients, the median study time was 2.9 (1.9-6.1) days. The median (IQR) time-in-target was 83.0 (68.7-93.1) % of time with the mean proposed measurement interval 2.0 ± 0.5 hours. 99.6% of the SGC advices on insulin infusion rate were accepted by the user. Only 4 episodes (0.01% of all BG measurements) of severe hypoglycaemia <2.2 mmol.l(-1) in 4 patients occurred (0.8%; 95% CI 0.02-1.6%). CONCLUSION Under routine conditions and under different nutritional protocols the Space GlucoseControl system with integrated eMPC algorithm has exhibited its suitability for glycaemia control in critically ill patients. TRIAL REGISTRATION ClinicalTrials.gov NCT01523665.
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Affiliation(s)
- Jan Blaha
- Department of Anaesthesiology and Intensive Medicine, 1st Faculty of Medicine, Charles University and General University Hospital Prague, U Nemocnice 2, 128 08, Prague 2, Czech Republic.
| | - Barbara Barteczko-Grajek
- Department of Anaesthesiology and Intensive Therapy, Wroclaw Medical University, Wroclaw, Poland.
| | - Pawel Berezowicz
- Department of Anaesthesiology and Intensive Care Medicine, Vejle Hospital, Vejle, Denmark.
| | - Jiri Charvat
- Internal Medicine Clinic, University Hospital in Motol, Prague, Czech Republic.
| | - Jiri Chvojka
- Medical Department I, Faculty of Medicine in Pilsen, Charles University in Prague and University Hospital in Pilsen, Pilsen, Czech Republic.
| | - Teodoro Grau
- Department of Anaesthesiology and Intensive Care Medicine, Capio Hospital Sur, Madrid, Spain.
| | - Jonathan Holmgren
- Department of Anaesthesiology and Intensive Care Medicine, County Hospital Ryhov, Jönköping, Sweden.
| | - Ulrich Jaschinski
- Department of Anaesthesiology and Surgical Intensive Care Medicine, Klinikum Augsburg, Augsburg, Germany.
| | - Petr Kopecky
- Department of Anaesthesiology and Intensive Medicine, 1st Faculty of Medicine, Charles University and General University Hospital Prague, U Nemocnice 2, 128 08, Prague 2, Czech Republic.
| | - Jan Manak
- Department of Internal Medicine III - Metabolism and Gerontology, University Hospital Hradec Kralove, Hradec Kralove, Czech Republic.
| | - Mette Moehl
- Department of Cardiothoracic Anaesthesia and Intensive Care Unit, University Hospital, University of Copenhagen, Copenhagen, Denmark.
| | - Jonathan Paddle
- Intensive Care Department, Royal Cornwall Hospital, Truro, UK.
| | - Marcello Pasculli
- Department of Surgical and Intensive Medicine, Siena University Hospital, Siena, Italy.
| | - Johan Petersson
- Department of Anesthesiology and Intensive Care, Karolinska University Hospital Solna, Stockholm, Sweden.
| | - Sirak Petros
- Medical ICU, University Hospital Leipzig, Leipzig, Germany.
| | - Danilo Radrizzani
- Department of Anesthesiology and Intensive Care, Legnano Hospital, Legnano, Italy.
| | - Vinodkumar Singh
- Critical Care Services, Department of Anaesthetics, West Suffollk Hospital NHS Trust, Bury St Edmunds, UK.
| | - Joel Starkopf
- Department of Anaesthesiology and Intensive Care, Tartu University Hospital, Tartu, Estonia.
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Nair BG, Grunzweig K, Peterson GN, Horibe M, Neradilek MB, Newman SF, Van Norman G, Schwid HA, Hao W, Hirsch IB, Patchen Dellinger E. Intraoperative blood glucose management: impact of a real-time decision support system on adherence to institutional protocol. J Clin Monit Comput 2015; 30:301-12. [DOI: 10.1007/s10877-015-9718-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 06/05/2015] [Indexed: 11/28/2022]
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Boutin JM, Gauthier L. Insulin infusion therapy in critically ill patients. Can J Diabetes 2015; 38:144-50. [PMID: 24690510 DOI: 10.1016/j.jcjd.2014.01.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Revised: 01/28/2014] [Accepted: 01/29/2014] [Indexed: 12/20/2022]
Abstract
While dysglycemia (hyperglycemia, hypoglycemia and glucose variability) is clearly associated with increased mortality in critically ill patients, target range of blood glucose control remains controversial. Standardized insulin infusion protocols constitute the basis of treatment of these patients. The choice of protocol and its implementation is a great challenge. In this article, we review the published data to help define the essential elements that compose a good protocol and apply the right conditions to make it safe and effective.
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Affiliation(s)
- Jean-Marie Boutin
- Département de Médecine, Service d'endocrinologie, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada.
| | - Lyne Gauthier
- Département de Pharmacie, Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
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Colpaert K, Oeyen S, Sijnave B, Peleman R, Benoit D, Decruyenaere J. Influence of smart real-time electronic alerting on glucose control in critically ill patients. J Crit Care 2014; 30:216.e1-6. [PMID: 25194590 DOI: 10.1016/j.jcrc.2014.07.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Revised: 07/24/2014] [Accepted: 07/27/2014] [Indexed: 01/18/2023]
Abstract
PURPOSE Hyperglycemia and hypoglycemia are frequently encountered in critically ill patients and associated with adverse outcomes. We configured a smart glycemia alert (S-GLY alert) with our Intensive Care Information System to decrease the number of hyperglycemic values and increase the proportion of time within the glucose interval of 80 to 150 mg/dL. MATERIALS AND METHODS Prospective intervention study in surgical intensive care unit in a tertiary care hospital. An 11-week prealert phase was followed by a 15-week intervention phase where the S-GLY alert was alerting the nurses through the Clinical Notification System of the Intensive Care Information System. RESULTS Overall, 2335 S-GLY alerts were recorded. There were less hyperglycemic values and less persistent hyperglycemic episodes in the alert phase (19.5% vs 26.5% [P < .001] and 9.9% vs 15.4% [P < .001], respectively). More time was spent within target glucose interval (82.3% vs 75.0%, P = .009). A lower proportion of patients experienced a new-onset hypoglycemic event (<70 mg/dL) in the alert phase (9.2% vs 15.2%, P = .016). The Sequential Organ Failure Assessment score was significantly reduced (5.2 vs 4.2, P < .001). CONCLUSIONS The implementation of a real-time smart electronic glycemia alert resulted in significantly less episodes of persistent hyperglycemia and a higher proportion of time with normoglycemia, while decreasing the number of hypoglycemic events.
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Affiliation(s)
- Kirsten Colpaert
- Department of Intensive Care, Ghent University Hospital, Ghent, Belgium.
| | - Sandra Oeyen
- Department of Intensive Care, Ghent University Hospital, Ghent, Belgium.
| | - Bart Sijnave
- Department of Information Technology, Ghent University Hospital, Ghent, Belgium.
| | - Renaat Peleman
- Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium.
| | - Dominique Benoit
- Department of Intensive Care, Ghent University Hospital, Ghent, Belgium.
| | - Johan Decruyenaere
- Department of Intensive Care, Ghent University Hospital, Ghent, Belgium.
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Wang Y, Xie H, Jiang X, Liu B. Intelligent closed-loop insulin delivery systems for ICU patients. IEEE J Biomed Health Inform 2014; 18:290-9. [PMID: 24403427 DOI: 10.1109/jbhi.2013.2269699] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Good glycemic control through insulin administration among intensive care unit (ICU) patients can reduce mortality significantly; however, it remains a big challenge because of scarcity of individualized models for ICU patients. To deal with this challenge, a new combination of particle swarm optimization (PSO) and model predictive control (MPC) has been proposed to identify the model online as well as to optimally design the input, i.e., the insulin delivery rate automatically. According to the population distribution, ten typical linear dynamic models were selected such that any patient's model could be approximated by a linear combination of these ten typical models. PSO was used to update the weight coefficients while MPC was used to design the insulin delivery rate based on the combination model identified by using PSO. The proposed strategy was compared with the Yale protocol on 30 virtual subjects. According to the control-variability grid analysis, the percentage values in A + B zone were, respectively, 100% under the proposed strategy and while 51% under the Yale protocol, which demonstrates the superior performance of the proposed strategy. As a good candidate for the full closed-loop insulin delivery method, this new combination can control the glucose level by bringing it to a safe range promptly thereby reducing the risk of death.
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Amrein K, Kachel N, Fries H, Hovorka R, Pieber TR, Plank J, Wenger U, Lienhardt B, Maggiorini M. Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units. BMC Endocr Disord 2014; 14:62. [PMID: 25074071 PMCID: PMC4118658 DOI: 10.1186/1472-6823-14-62] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 07/15/2014] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND The Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients. METHODS Two open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial. RESULTS Forty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings). CONCLUSIONS SGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.
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Affiliation(s)
- Karin Amrein
- Medical University of Graz, Austria, Department of Internal Medicine, Division of Endocrinology and Metabolism, Auenbruggerplatz 15, 8036 Graz, Austria
| | | | | | - Roman Hovorka
- Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Thomas R Pieber
- Medical University of Graz, Austria, Department of Internal Medicine, Division of Endocrinology and Metabolism, Auenbruggerplatz 15, 8036 Graz, Austria
- Joanneum Research Forschungsgesellschaft mbH, Graz, Austria
| | - Johannes Plank
- Medical University of Graz, Austria, Department of Internal Medicine, Division of Endocrinology and Metabolism, Auenbruggerplatz 15, 8036 Graz, Austria
| | - Urs Wenger
- Medical University of Zurich, Department of Internal Medicine, Medical Intensive Care Unit, Zurich, Switzerland
| | - Barbara Lienhardt
- Medical University of Zurich, Department of Internal Medicine, Medical Intensive Care Unit, Zurich, Switzerland
| | - Marco Maggiorini
- Medical University of Zurich, Department of Internal Medicine, Medical Intensive Care Unit, Zurich, Switzerland
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14
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Wernerman J, Desaive T, Finfer S, Foubert L, Furnary A, Holzinger U, Hovorka R, Joseph J, Kosiborod M, Krinsley J, Mesotten D, Nasraway S, Rooyackers O, Schultz MJ, Van Herpe T, Vigersky RA, Preiser JC. Continuous glucose control in the ICU: report of a 2013 round table meeting. Crit Care 2014; 18:226. [PMID: 25041718 PMCID: PMC4078395 DOI: 10.1186/cc13921] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Achieving adequate glucose control in critically ill patients is a complex but important part of optimal patient management. Until relatively recently, intermittent measurements of blood glucose have been the only means of monitoring blood glucose levels. With growing interest in the possible beneficial effects of continuous over intermittent monitoring and the development of several continuous glucose monitoring (CGM) systems, a round table conference was convened to discuss and, where possible, reach consensus on the various aspects related to glucose monitoring and management using these systems. In this report, we discuss the advantages and limitations of the different types of devices available, the potential advantages of continuous over intermittent testing, the relative importance of trend and point accuracy, the standards necessary for reporting results in clinical trials and for recognition by official bodies, and the changes that may be needed in current glucose management protocols as a result of a move towards increased use of CGM. We close with a list of the research priorities in this field, which will be necessary if CGM is to become a routine part of daily practice in the management of critically ill patients.
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Affiliation(s)
- Jan Wernerman
- Department of Anesthesiology and Intensive Care Medicine, K32, Karolinska University Hospital, Stockholm, Huddinge 14186, Sweden
| | - Thomas Desaive
- GIGA - Cardiovascular Sciences, University of Liege, Institute of Physics, B5, Allee du 6 aout, 17, Liege 4000, Belgium
| | - Simon Finfer
- The George Institute for Global Health and Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia
| | - Luc Foubert
- Department of Anesthesia and Intensive Care Medicine, OLV Clinic, Aalst 9300, Belgium
| | - Anthony Furnary
- Starr-Wood Cardiac Group, 9155 SW Barnes Road, Portland, OR 97225-6629, USA
| | - Ulrike Holzinger
- Department of Medicine III - Division of Gastroenterology and Hepatology, Medical University of Vienna, Waehringer Guertel 18-20, Vienna 1090, Austria
| | - Roman Hovorka
- University of Cambridge Metabolic Research Laboratories, Level 4, Wellcome trust MRC Institute of Metabolic Science, Box 289, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Jeffrey Joseph
- Jefferson Artificial Pancreas Center and Anesthesiology Program for Translational Research, Department of Anesthesiology, Jefferson Medical College of Thomas Jefferson University, 1020 Walnut Street, Philadelphia, PA 19107, USA
| | - Mikhail Kosiborod
- Saint-Luke’s Mid America Heart Institute, University of Missouri - Kansas City, 4401 Wornall Road, Kansas City, MO 64111, USA
| | - James Krinsley
- Division of Critical Care, Stamford Hospital and Columbia University College of Physicians and Surgeons, 30 Shelburne Road, Stamford, CT 06904, USA
| | - Dieter Mesotten
- Department of Intensive Care Medicine, University Hospitals Leuven, Herestraat 49, Leuven B-3000, Belgium
| | - Stanley Nasraway
- Surgical Intensive Care Units, Tufts Medical Center, 800 Washington Street, NEMC 4360, Boston, MA 02111, USA
| | - Olav Rooyackers
- Anesthesiology and Intensive Care Clinic, Karolinska Institute and University Hospital, Huddinge 14186, Sweden
| | - Marcus J Schultz
- Department of Intensive Care Medicine, Academic Medical Center at the University of Amsterdam, Meibergdreef 9, Amsterdam 1105 AZ, The Netherlands
| | - Tom Van Herpe
- Department of Intensive Care Medicine, University Hospitals Leuven, Herestraat 49, Leuven B-3000, Belgium
- Department of Electrical Engineering (STADIUS) - iMinds Future Health Department, Katholieke Universiteit Leuven, Leuven, Heverlee B-3001, Belgium
| | - Robert A Vigersky
- Diabetes Institute, Walter Reed National Military Medical Center, Bethesda, MD 20895, USA
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme Hospital, Université libre de Bruxelles, 808 route de Lennik, Brussels 1070, Belgium
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15
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Abstract
Since the development of intensive insulin therapy for the critically ill adult, tight glycemic control (TGC) has become increasingly complicated to apply and achieve. Software-guided (SG) algorithms for insulin dosing represent a new method to achieve euglycemia in critical illness. We provide an overview of the state of SG TGC with an eye to the future. The current milieu is disorganized, with little research that incorporates newer variables of dysglycemia, such as glycemic variability. To develop and implement better algorithms, scientists, programmers, and clinicians need to standardize measurements and variables.
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16
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Olinghouse C. Development of a computerized intravenous insulin application (AutoCal) at Kaiser Permanente Northwest, integrated into Kaiser Permanente HealthConnect: impact on safety and nursing workload. Perm J 2013; 16:67-70. [PMID: 23012605 DOI: 10.7812/tpp/12.959] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
CONTEXT The electronic medical record, HealthConnect, at the Kaiser Sunnyside Medical Center in the Northwest used scanned paper protocols for intravenous insulin administration. A chart review of 15 patients on intravenous insulin therapy using state-of-the-art paper-based column protocols revealed 40% deviation from the protocol. A time study of experienced nurses computing the insulin dose revealed an average of 2 minutes per calculation per hour to complete. OBJECTIVE To improve patient safety and to reduce nursing workload burden with a computerized intravenous insulin calculator application connected to HealthConnect. SOLUTION Using Kaiser iLab developers through innovation funding, a computerized protocol was developed and integrated into HealthConnect, with a computerized tracking system used to store and to analyze intravenous insulin data. OUTCOME A review of 35 patient charts using computerized insulin infusion tool indicated 100% accuracy in computations with a reduction of nursing workload from 2 minutes to 30 seconds per calculation. CONCLUSION Development and operationalizing an integrated intravenous insulin calculator into HealthConnect was successfully completed at the Kaiser Sunnyside Medical Center, with 97% nursing satisfaction scores and a promise to generate data on intravenous insulin therapy to refine the protocol.
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17
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Ottavian M, Barolo M, Zisser H, Dassau E, Seborg DE. Adaptive blood glucose control for intensive care applications. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 109:144-156. [PMID: 22424730 DOI: 10.1016/j.cmpb.2012.01.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2011] [Revised: 11/24/2011] [Accepted: 01/29/2012] [Indexed: 05/31/2023]
Abstract
Control of blood glucose concentration for patients in intensive care units (ICUs) has been demonstrated to be beneficial in reducing mortality and the incidence of serious complications, for both diabetic and non-diabetic patients. However, the high degree of variability and uncertainty characterizing the physiological conditions of critically ill subjects makes automated glucose control quite difficult; consequently, traditional, nurse-implemented protocols are widely employed. These protocols are based on infrequent glucose measurements, look-up tables to determine the appropriate insulin infusion rates, and bedside insulin administration. In this paper, a novel automatic adaptive control strategy based on frequent glucose measurements and a self-tuning control technique is validated based on a simulation study for 200 virtual patients. The adaptive control strategy is shown to be highly effective in controlling blood glucose concentration despite the large degree of variability in the blood glucose response exhibited by the 200 simulated patients.
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Affiliation(s)
- Matteo Ottavian
- Dipartimento di Principi e Impianti di Ingegneria Chimica, Università di Padova, via Marzolo 9, 35131 Padova PD, Italy
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18
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Amrein K, Ellmerer M, Hovorka R, Kachel N, Fries H, von Lewinski D, Smolle K, Pieber TR, Plank J. Efficacy and safety of glucose control with Space GlucoseControl in the medical intensive care unit--an open clinical investigation. Diabetes Technol Ther 2012; 14:690-5. [PMID: 22694176 DOI: 10.1089/dia.2012.0021] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
BACKGROUND We aimed to investigate the performance of the Space GlucoseControl system (SGC) (B. Braun, Melsungen, Germany) in medical critically ill patients. The SGC is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm. SUBJECTS AND METHODS The trial was designed as a single-center, open clinical investigation in a nine-bed medical intensive care unit in a tertiary center in Graz, Austria. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L). RESULTS Twenty mechanically ventilated patients (age, 63±16 years; body mass index, 31.0±10.7 kg/m(2); Acute Physiology and Chronic Health Evaluation II score, 25.4±6.3; 14 males; six with diabetes) were included for a period of 7.0±3.6 days. Time within target range was 83.4±8.9% (mean±SD), and mean arterial blood glucose was 6.8±0.4 mmol/L. No severe hypoglycemic episodes (<2.2 mmol/L) occurred, and the percentage of time within 2.2 and 3.3 mmol/L was low (0.03±0.07%). The sampling interval was 2.0±0.4 h. The mean insulin dose was 93.5±80.1 IU/day, and the adherence to the given insulin dose advice was high (98.3%). A total of 11 unintended therapy interruptions (0.08 events/treatment day) caused by software problems occurred in four patients. CONCLUSIONS SGC is a safe and efficient method to control blood glucose in critically ill patients in the medical intensive care unit.
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Affiliation(s)
- Karin Amrein
- Division of Endocrinology and Metabolism, Medical University of Graz, Auenbruggerplatz 15, Graz, Austria.
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19
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Nirantharakumar K, Chen YF, Marshall T, Webber J, Coleman JJ. Clinical decision support systems in the care of inpatients with diabetes in non-critical care setting: systematic review. Diabet Med 2012; 29:698-708. [PMID: 22150466 DOI: 10.1111/j.1464-5491.2011.03540.x] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Computerized clinical decision support systems have been claimed to reduce prescription errors and improve patient care. They may play an important role in the care of hospitalized patients with diabetes. AIM To collate evidence for the use of clinical decision support systems in improving the care of hospitalized patients with diabetes in a non-critical care setting and to assess their effectiveness. METHODS We searched four databases from 1980 to 2010 without language restrictions. All types of studies other than case reports were included. Data extraction and quality assessment were carried out based on the Centre for Review and Dissemination guidance. A narrative synthesis was conducted. RESULTS Fourteen studies met the inclusion criteria, including two cluster randomized controlled trials, eight before-and-after studies and four other descriptive studies. Generally, the quality of the studies was not very high. Nine out of 10 studies reported reduction in mean blood glucose or similar measures (patient-day-weighted mean blood glucose) during inpatient stay. The reduction using computerized physician order entry system in patient-day-weighted mean blood glucose ranged from 0.6 to 0.8 mmol/l (10.8-15.6 mg/dl). Other beneficial effects during inpatient stay included reduced use of sliding scale insulin and greater use of basal-bolus insulin regimen. Only one study found a significant increase in hypoglycaemic events. CONCLUSIONS Clinical decision support systems have been used, often as part of a complex programme, to improve the care of hospitalized patients with diabetes. There is some evidence that they may have a beneficial effect, but this needs further confirmation.
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20
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Bauman KA, Hyzy RC. ICU 2020: five interventions to revolutionize quality of care in the ICU. J Intensive Care Med 2012; 29:13-21. [PMID: 22328598 DOI: 10.1177/0885066611434399] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Intensive care units (ICUs) are an essential and unique component of modern medicine. The number of critically ill individuals, complexity of illness, and cost of care continue to increase with time. In order to meet future demands, maintain quality, and minimize medical errors, intensivists will need to look beyond traditional medical practice, seeking lessons on quality assurance from industry and aviation. Intensivists will be challenged to keep pace with rapidly advancing information technology and its diverse roles in ICU care delivery. Modern ICU quality improvement initiatives include ensuring evidence-based best practice, participation in multicenter ICU collaborations, employing state-of-the-art information technology, providing point-of-care diagnostic testing, and efficient organization of ICU care delivery. This article demonstrates that each of these initiatives has the potential to revolutionize the quality of future ICU care in the United States.
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Affiliation(s)
- Kristy A Bauman
- Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan Medical Center, Ann Arbor, MI, USA
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21
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Abstract
Diabetes affects approximately one quarter of all hospitalized patients. Poor inpatient glycemic control has been associated with increased risk for multiple adverse events including surgical site infections, prolonged hospital length of stay, and mortality. Inpatient glycemic control protocols based on physiologic basal-bolus insulin regimens have been shown to improve glycemia and clinical outcomes and are recommended by the American Diabetes Association, the American Association of Clinical Endocrinologists, and the Society of Hospital Medicine for inpatient glycemic management of noncritically ill patients. The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act will catalyze widespread computerized medication order entry implementation over the next few years. Here, we focus on the noncritical care setting and review the background on inpatient glycemic management as it pertains to computerized order entry, the translation and efficacy of computerizing glycemic control protocols, and the barriers to computerizing glycemic protocols.
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Affiliation(s)
- Nancy J Wei
- Massachusetts General Hospital, Diabetes Center, 55 Fruit Street, Boston, MA 02114, USA.
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22
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Abstract
Intensive monitoring of blood glucose concentrations in critically ill patients has become a standard of care in intensive care units over the past 10 years, following the publication of a single-center randomized trial targeting euglycemia in postoperative patients. This article summarizes the literature describing the relationship between hyperglycemia and mortality in the critically ill, the main findings of the major interventional trials of intensive insulin therapy, the association between hypoglycemia and increased glycemic variability with adverse outcomes, and the impact of a preexisting diagnosis of diabetes. A framework for understanding dysglycemia in the critically ill, an approach that recognizes disturbances in the "3 domains" of glycemic control--hyperglycemia, hypoglycemia, and increased glycemic variability--is presented. Finally, practical considerations relating to the implementation of glycemic management protocols are discussed.
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Affiliation(s)
- James S Krinsley
- Division of Critical Care, Stamford Hospital, Stamford, CT 06902, USA.
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23
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The evaluation of the ability of closed-loop glycemic control device to maintain the blood glucose concentration in intensive care unit patients. Crit Care Med 2011; 39:575-8. [PMID: 21178768 DOI: 10.1097/ccm.0b013e318206b9ad] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Reduction in the variability of blood glucose concentration might be an important aspect of blood glucose management. A closed-loop glycemic control device (STG-22; NIKKISO, Tokyo, Japan) has been developed to maintain blood glucose levels within the target range through automatic infusion of insulin and glucose. We hypothesized that the STG-22 system could provide optimal blood glucose management without causing hypoglycemic events in patients admitted to intensive care units. In this study, we investigated the feasibility of glycemic control with the STG-22 system. Furthermore, we evaluated the variability in blood glucose concentration associated with the use of the STG-22 system. DESIGN Retrospective analysis. SETTING A five-bed medical/surgical intensive care unit in a university hospital. PATIENTS Two hundred eight patients admitted to the intensive care unit between August 2006 and July 2009. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We calculated the mean and sd of blood glucose concentrations in each patient during intensive insulin therapy (target range, 90-110 mg/dL) administered using STG-22. In addition, to evaluate the blood glucose control achieved using STG-22, the durations for which the blood glucose level was maintained at 70-110 mg/dL, 110-150 mg/dL, 150-180 mg/dL, and>180 mg/dL were calculated. The average operation time of STG-22 was 33.9±42.4 hrs. The blood glucose level was maintained at 70-110 mg/dL for 49.5% of the study period; the corresponding values for 110-150 mg/dL, 150-180 mg/dL, and>180 mg/dL were 31.4%, 7.0%, and 6.9%, respectively. No hypoglycemic events occurred. The sd of blood glucose levels was 19.9±10.9 mg/dL. After a level of 150 mg/dL was achieved, the sd of blood glucose was 12.6±3.1 mg/dL. CONCLUSIONS STG-22 can help maintain optimal blood glucose levels without causing hypoglycemia in patients admitted to the intensive care unit. In addition, the use of this device might help decrease the variability in blood glucose concentration. Further randomized clinical trials are required to elucidate whether the low glucose variability maintained using STG-22 can contribute to improving the outcomes of patients admitted to the intensive care unit.
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24
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Hoekstra M, Schoorl MA, van der Horst ICC, Vogelzang M, Wietasch JKG, Zijlstra F, Nijsten MWN. Computer-assisted glucose regulation during rapid step-wise increases of parenteral nutrition in critically ill patients: a proof of concept study. JPEN J Parenter Enteral Nutr 2011; 34:549-53. [PMID: 20852185 DOI: 10.1177/0148607110372390] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Early delivery of calories is important in critically ill patients, and the administration of parenteral nutrition (PN) is sometimes required to achieve this goal. However, PN can induce acute hyperglycemia, which is associated with adverse outcome. We hypothesized that initiation of PN using a rapid "step-up" approach, coupled with a computerized insulin-dosing protocol, would result in a desirable caloric intake within 24 hours without causing hyperglycemia. METHODS In our surgical intensive care unit (ICU), glucose is regulated by a nurse-centered computerized glucose regulation program. When adequate enteral feeding was not possible, PN was initiated according to a simple step-up rule at an infusion rate of 10 mL/h (approximately 10 kcal/h) and subsequently increased by steps of 10 mL/h every 4 hours, provided glucose was <10 mmol/L, until the target caloric intake (1 kcal/kg/h) was reached. All glucose levels and insulin doses were collected during the step-up period and for 24 hours after achieving target feeding. RESULTS In all 23 consecutive patients requiring PN, mean intake was 1 kcal/kg/h within 24 hours. Of the 280 glucose samples during the 48-hour study period, mean ± standard deviation glucose level was 7.4 ± 1.4 mmol/L. Only 4.5% of glucose measurements during the step-up period were transiently ≥10 mmol/L. After initiating PN, the insulin requirement rose from 1.1 ± 1.5 units/h to 2.9 ± 2.5 units/h (P < .001). CONCLUSIONS This proof of concept study shows that rapid initiation of PN using a step-up approach coupled with computerized glucose control resulted in adequate caloric intake within 24 hours while maintaining adequate glycemic control.
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Affiliation(s)
- Miriam Hoekstra
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
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25
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Abstract
PURPOSE OF REVIEW To review the domains in which computerized information systems have proven beneficial in facilitating the metabolic and nutritional management RECENT FINDINGS In glucose control, computerized insulin algorithms have proven safer and more efficient than manual systems, reducing workload, time to target glycemia and numbers of hypoglycemic and hyperglycemic events. By rendering the nutritional variables visible through specific customization, computers do improve daily monitoring of energy balance and adherence to guidelines, particularly for substrate delivery. Nurse-centered systems have shown to be the most successful to enable routine workflow based on protocol-based care. SUMMARY Computers are needed to analyze the increasing amount of data collected from critically ill patients from monitoring systems, laboratories and other sources. Studies have shown that computerized information systems do facilitate glucose control, helping reducing hypoglycemic events. They also improve nutritional monitoring (energy delivery and balance, protein and fat delivery), and quality of nutrition. They reduce nurse workload associated with the multiple balance calculations and ease visualization of events out of planned targets. Though integrated systems are expensive, they improve work efficiency.
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Affiliation(s)
- Mette M Berger
- Service de Médecine Intensive Adulte et Centre des Brûlés, University Hospital, Rue du Bugnon 46, Lausanne, Switzerland.
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26
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Maslove DM, Rizk N, Lowe HJ. Computerized physician order entry in the critical care environment: a review of current literature. J Intensive Care Med 2011; 26:165-71. [PMID: 21257633 DOI: 10.1177/0885066610387984] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The implementation of health information technology (HIT) is accelerating, driven in part by a growing interest in computerized physician order entry (CPOE) as a tool for improving the quality and safety of patient care. Computerized physician order entry could have a substantial impact on patients in intensive care, where the potential for medical error is high, and the clinical workflow is complex. In 2009, only 17% of hospitals had functional CPOE systems in place. In intensive care unit (ICU) settings, CPOE has been shown to reduce the occurrence of some medication errors, but evidence of a beneficial effect on clinical outcomes remains limited. In some cases, new error types have arisen with the use of CPOE. Intensive care unit workflow and staff relationships have been affected by CPOE, often in unanticipated ways. The design of CPOE software has a strong impact on user acceptance. Intensive care unit-specific order sets lessen the cognitive workload associated with the use of CPOE and improve user acceptance. The diffusion of new technological innovations in the ICU can have unintended consequences, including changes in workflow, staff roles, and patient outcomes. When implementing CPOE in critical care areas, both organizational and technical factors should be considered. Further research is needed to inform the design and management of CPOE systems in the ICU and to better assess their impact on clinical end points, cost-effectiveness, and user satisfaction.
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Affiliation(s)
- David M Maslove
- Division of Pulmonary and Critical Care Medicine, Stanford University School of Medicine, CA, USA.
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27
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Hoekstra M, Vogelzang M, van der Horst IC, Lansink AO, van der Maaten JM, Ismael F, Zijlstra F, Nijsten MW. Trial design: Computer guided normal-low versus normal-high potassium control in critically ill patients: Rationale of the GRIP-COMPASS study. BMC Anesthesiol 2010; 10:23. [PMID: 21194419 PMCID: PMC3022901 DOI: 10.1186/1471-2253-10-23] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2010] [Accepted: 12/31/2010] [Indexed: 01/04/2023] Open
Abstract
Background Potassium depletion is common in hospitalized patients and can cause serious complications such as cardiac arrhythmias. In the intensive care unit (ICU) the majority of patients require potassium suppletion. However, there are no data regarding the optimal control target in critically ill patients. After open-heart surgery, patients have a strongly increased risk of atrial fibrillation or atrial flutter (AFF). In a novel trial design, we examined if in these patients different potassium control-targets within the normal range may have different effects on the incidence of AFF. Methods/Design The "computer-driven Glucose and potassium Regulation program in Intensive care Patients with COMparison of PotASSium targets within normokalemic range (GRIP-COMPASS) trial" is a single-center prospective trial in which a total of 1200 patients are assigned to either a potassium control-target of 4.0 mmol/L or 4.5 mmol/L in consecutive alternating blocks of 50 patients each. Potassium levels are regulated by the computer-assisted potassium suppletion algorithm called GRIP-II (Glucose and potassium regulation for Intensive care Patients). Primary endpoint is the in-hospital incidence of AFF after cardiac surgery. Secondary endpoints are: in-hospital AFF in medical patients or patients after non-cardiac surgery, actually achieved potassium levels and their variation, electrolyte and glucose levels, potassium and insulin requirements, cumulative fluid balance, (ICU) length of stay, ICU mortality, hospital mortality and 90-day mortality. Discussion The GRIP-COMPASS trial is the first controlled clinical trial to date that compares potassium targets. Other novel methodological elements of the study are that it is performed in ICU patients where both targets are within the normal range and that a computer-assisted potassium suppletion algorithm is used. Trial registration NCT 01085071 at ClinicalTrials.gov
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Affiliation(s)
- Miriam Hoekstra
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB, Groningen, the Netherlands.
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28
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Hoekstra M, Vogelzang M, Verbitskiy E, Nijsten MW. Hourly measurements not required for safe and effective glycemic control in the critically ill patient. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2010; 14:404. [PMID: 20156329 PMCID: PMC2875483 DOI: 10.1186/cc8190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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29
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Hoekstra M, Vogelzang M, Drost JT, Janse M, Loef BG, van der Horst ICC, Zijlstra F, Nijsten MWN. Implementation and evaluation of a nurse-centered computerized potassium regulation protocol in the intensive care unit--a before and after analysis. BMC Med Inform Decis Mak 2010; 10:5. [PMID: 20100342 PMCID: PMC2826292 DOI: 10.1186/1472-6947-10-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2009] [Accepted: 01/25/2010] [Indexed: 12/23/2022] Open
Abstract
Background Potassium disorders can cause major complications and must be avoided in critically ill patients. Regulation of potassium in the intensive care unit (ICU) requires potassium administration with frequent blood potassium measurements and subsequent adjustments of the amount of potassium administrated. The use of a potassium replacement protocol can improve potassium regulation. For safety and efficiency, computerized protocols appear to be superior over paper protocols. The aim of this study was to evaluate if a computerized potassium regulation protocol in the ICU improved potassium regulation. Methods In our surgical ICU (12 beds) and cardiothoracic ICU (14 beds) at a tertiary academic center, we implemented a nurse-centered computerized potassium protocol integrated with the pre-existent glucose control program called GRIP (Glucose Regulation in Intensive Care patients). Before implementation of the computerized protocol, potassium replacement was physician-driven. Potassium was delivered continuously either by central venous catheter or by gastric, duodenal or jejunal tube. After every potassium measurement, nurses received a recommendation for the potassium administration rate and the time to the next measurement. In this before-after study we evaluated potassium regulation with GRIP. The attitude of the nursing staff towards potassium regulation with computer support was measured with questionnaires. Results The patient cohort consisted of 775 patients before and 1435 after the implementation of computerized potassium control. The number of patients with hypokalemia (<3.5 mmol/L) and hyperkalemia (>5.0 mmol/L) were recorded, as well as the time course of potassium levels after ICU admission. The incidence of hypokalemia and hyperkalemia was calculated. Median potassium-levels were similar in both study periods, but the level of potassium control improved: the incidence of hypokalemia decreased from 2.4% to 1.7% (P < 0.001) and hyperkalemia from 7.4% to 4.8% (P < 0.001). Nurses indicated that they considered computerized potassium control an improvement over previous practice. Conclusions Computerized potassium control, integrated with the nurse-centered GRIP program for glucose regulation, is effective and reduces the prevalence of hypo- and hyperkalemia in the ICU compared with physician-driven potassium regulation.
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Affiliation(s)
- Miriam Hoekstra
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
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