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Intensive Care Medicine

, 35:123 | Cite as

Intensive insulin therapy: enhanced Model Predictive Control algorithm versus standard care

  • Jeremy J. CordingleyEmail author
  • Dirk Vlasselaers
  • Natalie C. Dormand
  • Pieter J. Wouters
  • Stephen D. Squire
  • Ludovic J. Chassin
  • Malgorzata E. Wilinska
  • Clifford J. Morgan
  • Roman Hovorka
  • Greet Van den Berghe
Original

Abstract

Objective

To investigate the effectiveness of an enhanced software Model Predictive Control (eMPC) algorithm for intravenous insulin infusion, targeted at tight glucose control in critically ill patients, over 72 h, in two intensive care units with different management protocols.

Design

Comparison with standard care in a two center open randomized clinical trial.

Setting

Two adult intensive care units in University Hospitals.

Patients and participants

Thirty-four critically ill patients with hyperglycaemia (glucose >120 mg/dL) or already receiving insulin infusion.

Interventions

Patients were randomized, within each ICU, to intravenous insulin infusion advised by eMPC algorithm or the ICU’s standard insulin infusion administration regimen.

Measurements and results

Arterial glucose concentration was measured at study entry and when advised by eMPC or measured as part of standard care. Time-weighted average glucose concentrations in patients receiving eMPC advised insulin infusions were similar [104 mg/dL (5.8 mmol/L)] in both ICUs. eMPC advised glucose measurement interval was significantly different between ICUs (1.1 vs. 1.8 h, P < 0.01). The standard care insulin algorithms resulted in significantly different time-weighted average glucose concentrations between ICUs [128 vs. 99 mg/dL (7.1 vs. 5.5 mmol/L), P < 0.01].

Conclusions

In this feasibility study the eMPC algorithm provided similar, effective and safe tight glucose control over 72 h in critically ill patients in two different ICUs. Further development is required to reduce glucose sampling interval while maintaining a low risk of hypoglycaemia.

Keywords

Hyperglycaemia Glucose Critical care Insulin Model Predictive Control Algorithm 

Notes

Acknowledgments

The authors wish to thank the patients, nurses, doctors, clinical engineers and allied health professionals in both ICUs for their support in undertaking this study. The CLINICIP project is supported by an EC 6th Framework Programme grant.

References

  1. 1.
    Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P, Bouillon R (2001) Intensive insulin therapy in the critically ill patients. N Engl J Med 345:1359–1367PubMedCrossRefGoogle Scholar
  2. 2.
    Van den Berghe G, Wouters PJ, Bouillon R, Weekers F, Verwaest C, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P (2003) Outcome benefit of intensive insulin therapy in the critically ill: insulin dose versus glycemic control. Crit Care Med 31:359–366PubMedCrossRefGoogle Scholar
  3. 3.
    Krinsley JS (2004) Effect of an intensive glucose management protocol on the mortality of critically ill adult patients. Mayo Clin Proc 79:992–1000 (Erratum 80:1101)PubMedCrossRefGoogle Scholar
  4. 4.
    Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H, Bouillon R (2006) Intensive insulin therapy in the medical ICU. N Engl J Med 354:449–461PubMedCrossRefGoogle Scholar
  5. 5.
    Furnary AP, Gao G, Grunkemeier GL, Wu Y, Zerr KJ, Bookin SO, Floten HS, Starr A (2003) Continuous insulin infusion reduces mortality in patients with diabetes undergoing coronary artery bypass grafting. J Thorac Cardiovasc Surg 125:1007–1021PubMedCrossRefGoogle Scholar
  6. 6.
    Pittas AG, Siegel RD, Lau J (2006) Insulin therapy and in-hospital mortality in critically ill patients: systematic review and meta-analysis of randomized controlled trials. JPEN 30:164–172Google Scholar
  7. 7.
    Cheung NW, Wong VW, McLean M (2006) The hyperglycemia: intensive insulin infusion in infarction (HI-5) study: a randomized controlled trial of insulin infusion therapy for myocardial infarction. Diabetes Care 29:765–770PubMedCrossRefGoogle Scholar
  8. 8.
    CLINICIP. Available online at: http://www.clinicip.org. Accessed 12 March 2007
  9. 9.
    Plank J, Blaha J, Cordingley J, Wilinska ME, Chassin LJ, Morgan C, Squire S, Haluzik M, Svacina S, Toller W, Plasnik A, Ellmerer M, Hovorka R, Pieber TR (2006) 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 29:271–276PubMedCrossRefGoogle Scholar
  10. 10.
    Cordingley J, Vlaselaers D, Hovorka R, Dormand N, Wouters P, Chassin L, Squire S, Wilinska M, Morgan C, Van den Berghe G (2007) Tight glycaemic control in critically ill patients: two centre trial of enhanced model predictive algorithm versus standard care. Intensive Care Med 33(Suppl 2):190Google Scholar
  11. 11.
    Hovorka R, Chassin LJ, Wilinska ME, Canonico V, Akwi JA, Federici MO, Massi-Benedetti M, Hutzli I, Zaugg C, Kaufmann H, Both M, Vering T, Schaller HC, Schaupp L, Bodenlenx M, Pieber TR (2004) Closing the loop: the Adicol experience. Diabetes Technol Ther 6:307–318PubMedCrossRefGoogle Scholar
  12. 12.
    Hovorka R (2006) Continuous glucose monitoring and closed-loop systems. Diabet Med 23:1–12PubMedCrossRefGoogle Scholar
  13. 13.
    Vogelzang M, van der Horst IC, Nijsten MW (2004) Hyperglycaemic index as a tool to assess glucose control: a retrospective study. Crit Care 8:R122–R127PubMedCrossRefGoogle Scholar
  14. 14.
    Van den Berghe G (2004) How to compare adequacy of algorithms to control blood glucose in the intensive care unit? Crit Care 8:151–152PubMedCrossRefGoogle Scholar
  15. 15.
    Rood E, Bosman RJ, van der Spoel JI, Taylor P, Zandstra DF (2005) Use of a computerized guideline for glucose regulation in the intensive care unit improved both guideline adherence and glucose regulation. J Am Med Inform Assoc 12:172–180PubMedCrossRefGoogle Scholar
  16. 16.
    Thomas AN, Marchant AE, Ogden MC, Collin S (2005) Implementation of a tight glycaemic control protocol using a web-based insulin dose calculator. Anaesthesia 60:1093–1100PubMedCrossRefGoogle Scholar
  17. 17.
    Davidson PC, Steed RD, Bode BW (2005) Glucomander. Diabetes Care 28:2418–2423PubMedCrossRefGoogle Scholar
  18. 18.
    Vogelzang M, Zijlstra F, Nijsten W (2005) Design and implementation of GRIP: a computerized glucose control system at a surgical intensive care unit. BMC Med Inform Decis Mak 5:38PubMedCrossRefGoogle Scholar
  19. 19.
    Wong XW, Chase JG, Shaw JM, Hann CE, Lotz T, Singh-Levett I, Hollingsworth LJ, Wong OS, Andreassen S (2006) Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study. Med Eng Phys 28:665–681PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Jeremy J. Cordingley
    • 1
    Email author
  • Dirk Vlasselaers
    • 2
  • Natalie C. Dormand
    • 1
  • Pieter J. Wouters
    • 2
  • Stephen D. Squire
    • 1
  • Ludovic J. Chassin
    • 3
  • Malgorzata E. Wilinska
    • 3
  • Clifford J. Morgan
    • 1
  • Roman Hovorka
    • 3
  • Greet Van den Berghe
    • 2
  1. 1.Adult Intensive Care UnitRoyal Brompton HospitalLondonUK
  2. 2.Intensive Care UnitUniversity HospitalLeuvenBelgium
  3. 3.Department of PaediatricsUniversity of CambridgeCambridgeUK

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