Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the “how” and “for whom” in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control.
Methods:
Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome (n = 3, 15%) also matches SPRINT results for this patient group.
Results:
Data cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article.
Conclusion:
Model-based methods can provide tighter, more adaptable “one method fits all” solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data.
CapesSEHuntDMalmbergKGersteinHC. Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: A systematic overview. Lancet.2000; 355 (9206): 773–8.
2.
Van den BergheGWoutersPWeekersFVerwaestCBruyninckxFSchetzMVlasselaersDFerdinandePLauwersPBouillonR. Intensive insulin therapy in the critically ill patients. N Engl J Med.2001; 345 (19): 1359–67.
3.
MizockBA. Alterations in fuel metabolism in critical illness: Hyperglycaemia. Best Pract Res Clin Endocrinol Metab.2001; 15 (4): 533–51.
4.
McCowenKCMalhotraABistrianBR. Stress-induced hyperglycemia. Crit Care Clin.2001; 17 (1): 107–24.
5.
PatinoJFde PimientoSEVergaraASavinoPRodriguezMEscallonJ. Hypocaloric support in the critically ill. World J Surg.1999; 23 (6): 553–9.
6.
WeissmanC. Nutrition in the intensive care unit. Crit Care.1999; 3 (5): R67–75.
7.
WoolfsonAM. Control of blood glucose during nutritional support in ill patients. Intensive Care Med.1980; 7 (1): 11–14.
8.
AhrensCLBarlettaJFKanjiSTyburskiJGWilsonRFJanisseJJDevlinJW. Effect of low-calorie parenteral nutrition on the incidence and severity of hyperglycemia in surgical patients: A randomized, controlled trial. Crit Care Med.2005; 33 (11): 2507–12.
9.
KimHSonEKimJChoiKKimCShinWSuhO. Association of hyperglycemia and markers of hepatic dysfunction with dextrose infusion rates in Korean patients receiving total parenteral nutrition. Am J Health Syst Pharm.2003; 60 (17): 1760–6.
10.
KrishnanJAParcePBMartinezADietteGBBrowerRG. Caloric intake in medical ICU patients: Consistency of care with guidelines and relationship to clinical outcomes. Chest.2003; 124 (1): 297–305.
11.
BistrianBR. Hyperglycemia and infection: Which is the chicken and which is the egg?. JPEN J Parenter Enteral Nutr.2001; 25 (4): 180–1.
12.
MarikPERaghavanM. Stress-hyperglycemia, insulin and immunomodulation in sepsis. Intensive Care Med.2004; 30 (5): 748–56.
13.
DiringerMN. Improved outcome with aggressive treatment of hyperglycemia: Hype or hope?. Neurology.2005; 64 (8): 1330–1.
14.
KrinsleyJS. Decreased mortality of critically ill patients with the use of an intensive glycemic management protocol. Crit Care Med.2003; 31: 31.
15.
KrinsleyJS. Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc.2004; 79 (8): 992–1000.
16.
Van den BergheGWoutersPJBouillonRWeekersFVerwaestCSchetzMVlasselaersDFerdinandePLauwersP. Outcome benefit of intensive insulin therapy in the critically ill: Insulin dose versus glycemic control. Crit Care Med.2003; 31 (2): 359–66.
17.
Van den BergheGWilmerAHermansGMeerssemanWWoutersPJMilantsIVan WijngaerdenEBobbaersHBouillonR. Intensive insulin therapy in the medical ICU. N Engl J Med.2006; 354 (5): 449–61.
18.
KrinsleyJSJonesRL. Cost analysis of intensive glycemic control in critically ill adult patients. Chest.2006; 129 (3): 644–50.
19.
Van den BergheGWoutersPJKestelootKHillemanDE. Analysis of healthcare resource utilization with intensive insulin therapy in critically ill patients. Crit Care Med.2006; 34 (3): 612–616.
20.
GoldbergPASiegelMDSherwinRSHalickmanJILeeMBaileyVALeeSLDziuraJDInzucchiSE. Implementation of a safe and effective insulin infusion protocol in a medical intensive care unit. Diabetes Care.2004; 27 (2): 461–7.
21.
LaverSPrestonSTurnerDMcKinstryCPadkinA. Implementing intensive insulin therapy: Development and audit of the Bath insulin protocol. Anaesth Intensive Care.2004; 32 (3): 311–6.
22.
ThomasANMarchantAEOgdenMCCollinS. Implementation of a tight glycaemic control protocol using a web-based insulin dose calculator. Anaesthesia.2005; 60 (11): 1093–1100.
23.
MeijeringSCorstjensAMTullekenJEMeertensJHZijlstraJGLigtenbergJJ. Towards a feasible algorithm for tight glycaemic control in critically ill patients: A systematic review of the literature. Crit Care.2006; 10 (1): R19.
24.
GoldbergPASakharovaOVBarrettPWFalkoLNRousselMGBakLBlake-HolmesDMariebNJInzucchiSE. Improving glycemic control in the cardiothoracic intensive care unit: Clinical experience in two hospital settings. J Cardiothorac Vasc Anesth.2004; 18 (6): 690–697.
25.
ShulmanRFinneySJO'SullivanCGlynnePAGreeneR. Tight glycaemic control: A prospective observational study of a computerised decision-supported intensive insulin therapy protocol. Criti Care.2007; 11 (4): R75.
26.
LonerganTLeCompteAWillacyMChaseJGShawGMWongXWLotzTLinJHannCE. A simple insulin-nutrition protocol for tight glycemic control in critical illness: Development and protocol comparison. Diabetes Technol Ther.2006; 8 (2): 191–206.
27.
WongXWChaseJGShawGMHannCELinJLotzT. Comparison of adaptive and sliding-scale glycaemic control in critical care and the impact of nutritional inputs. Proc of the 12th International Conf on Biomedical Engineering (ICBME 2005); 2005 Dec. 7–10; Singapore; 2005.
28.
CheeFFernandoTvan HeerdenPV. Closed-loop control of blood glucose levels in critically ill patients. Anaesth Intensive Care.2002; 30 (3): 295–307.
29.
ChaseJGLonerganTLeCompteAWillacyMShawGMWongXWLinJLotzTHannCE. Tight glucose control in critically ill patients using a specialized insulin-nutrition table. Proc of the 12th International Conf on Biomedical Engineering (ICBME 2005); 2005 Dec. 7–10; Singapore; 2005.
30.
QuealeWSSeidlerAJBrancatiFL. Glycemic control and sliding scale insulin use in medical inpatients with diabetes mellitus. Arch Intern Med.1997; 157 (5): 545–52.
SawinCT. Action without benefit. The sliding scale of insulin use. Arch Intern Med.1997; 157 (5): 489.
34.
GaleSCGraciasVH. Glycemic control needs a standard reference point. Crit Care Med.2006; 34 (6): 1856–7.
35.
MackenzieIIngleSZaidiSBuczaskiS. Tight glycaemic control: A survey of intensive care practice in large English hospitals. Intensive Care Med.2005; 31 (8): 1136.
36.
SchultzMJSpronkPEMoeniralamHS. Tight glycaemic control: A survey of intensive care practice in the Netherlands. Intensive Care Med.2006; 32 (4): 618–9.
37.
ChaseJGHannCEShawGMWongXWLinJLotzTLe CompteAJLonerganT. An overview of glycemic control in critical care: Relating performance and clinical results. J Diabetes Sci Technol.2007; 1 (1): 82–91.
38.
EgiMBellomoRStachowskiEFrenchCJHartG. Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology.2006; 105 (2): 244–52.
39.
ChaseJGShawGMLinJDoranCVHannCLotzTWakeGCBroughtonB. Targeted glycemic reduction in critical care using closed-loop control. Diabetes Technol Ther.2005; 7 (2): 274–82.
40.
CheeFFernandoTLSavkinAVvan HeedenV. Expert PID control system for blood glucose control in critically ill patients. IEEE Trans Inf Technol Biomed.2003; 7 (4): 419–25.
41.
BlahaJHovorkaRMatiasMKotulakTKremenJSloukovaASvacinaSHaluzikM. Intensive insulin therapy in critically ill patients: Comparison of standard and MPC protocols. Intensive Care Med.2005; 31 (S1): S203.
42.
PlankJBlahaJCordingleyJWilinskaMEChassinLJMorganCSquireSHaluzikMKremenJSvacinaSTollerWPlasnikAEllmererMHovorkaRPieberTR. Multicentric, randomized, controlled trial to evaluate blood glucose control by the model predictive control algorithm versus routine glucose management protocols in intensive care unit patients. Diabetes Care.2006; 29 (2): 271–6.
43.
DoranCV. Modelling and control of hyperglycemia in critical care patients. Masters of Engineering (ME). Christchurch, New Zealand: University of Canterbury; 2004.
44.
WongXWSingh-LevettIHollingsworthLJShawGMHannCELotzTLinJWongOSChaseJG. A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients. Diabetes Technol Ther.2006; 8 (2): 174–90.
45.
ChaseJShawGMWongXWLotzTLinJHannCE. Modelbased glycaemic control in critical care: A review of the state of the possible. Biomed Signal Processing Control.2006; 1 (1): 3–21.
46.
HovorkaRWilinskaMEChassinLJ. In Silico simulation environment and glucose control in critically ill subjects: Strategic considerations. Third European Medical & Biological Engineering Conference; 2005 Nov 20–25. Prague, Czech Republic: IFMBE; 2005.
47.
WilinskaMEChassinLJHovorkaR. Automated glucose control in the ICU: Effect of nutritional protocol and measurement error. Conf Proc IEEE Eng Med Biol Soc.2006; 1: 67–70.
48.
ChaseJGShawGMLotzTLeCompteAWongJLinJLonerganTWillacyMHannCE. Model-based insulin and nutrition administration for tight glycaemic control in critical care. Curr Drug Deliv.2007; 4 (4): 283–96.
49.
WilinskaMEChassinLHovorkaR. In silico testing—impact on the progress of the closed loop insulin infusion for critically ill patients project. J Diabetes Sci Technol.2008; 2 (3): 417–23.
50.
HovorkaRChassinLJWilinskaME. Virtual type 1 diabetic treated by CSII: Model description. WC2003: 2003 Aug 24–29; Sydney, Australia; 2003.
51.
LuzioSDunseathGPeterRPauvadayVOwensDR. Comparison of the pharmacokinetics and pharmacodynamics of biphasic insulin aspart and insulin glargine in people with type 2 diabetes. Diabetologia.2006; 49 (6): 1163–8.
52.
ChassinLJWilinskaMEHovorkaR. Evaluation of glucose controllers in virtual environment: Methodology and sample application. Artif Intell Med.2004; 32 (3): 171–81.
WongJChaseJGHannCELotzTFLinJLe CompteAShawGM. Development of a clinical type 1 diabetes metabolic system model and in silico simulation tool. J Diabetes Sci Technol.2008; 2 (3): 425–35.
55.
WongJChaseJGHannCELotzTFLinJLe CompteAShawGM. In silico simulation of long-term type 1 diabetes glycemic control treatment outcomes. J Diabetes Sci Technol.2008; 2 (3): 436–49.
56.
LinJLeeDSChaseJGHannCELotzTWongXW. Stochastic modelling of insulin sensitivity variability in critical care. Biomed Signal Processing Control.2006; 1: 229–42.
57.
LinJLeeDChaseJGShawGMLe CompteALotzTWongJLonerganTHannCE. Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care. Comput Methods Programs Biomed.2008; 89 (2): 141–52.
58.
ShawGMChaseJGWongJLinJLotzTLe CompteAJLonerganTRWillacyMBHannCE. Rethinking glycaemic control in critical illness—from concept to clinical practice change. Crit Care Resusc.2006; 8 (2): 90–9.
59.
LonerganTCompteALWillacyMChaseJGShawGMHannCELotzTLinJWongXW. A pilot study of the SPRINT protocol for tight glycemic control in critically ill patients. Diabetes Technol Ther.2006; 8 (4): 449–62.
60.
ChaseJGShawGLe CompteALonerganTWillacyMWongXWLinJLotzTLeeDHannC. Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: A clinical practice change. Crit Care.2008; 12 (2): R49.
61.
LimpertEStahelWAAbbtM. Log-normal distributions across the sciences: Keys and clues. Bioscience.2001; 51 (5): 341–52.
62.
ChaseJGShawGMHannCELecompteALonerganTWillacyMWongXWLinJLotzT. Clinical validation of a modelbased glycaemic control design approach and comparison to other clinical protocols. Conf Proc IEEE Eng Med Biol Soc.2006; 1: 59–62.
