Literature DB >> 18520641

A replicable method for blood glucose control in critically Ill patients.

Alan H Morris1, James Orme, Jonathon D Truwit, Jay Steingrub, Colin Grissom, Kang H Lee, Guoliang L Li, B Taylor Thompson, Roy Brower, Mark Tidswell, Gordon R Bernard, Dean Sorenson, Katherine Sward, Hui Zheng, David Schoenfeld, Homer Warner.   

Abstract

CONTEXT: To ensure interpretability and replicability of clinical experiments, methods must be adequately explicit and should elicit the same decision from different clinicians who comply with the study protocol.
OBJECTIVE: The objective of this study was to determine whether clinician compliance with protocol recommendations exceeds 90%.
DESIGN: We developed an adequately explicit computerized protocol (eProtocol-insulin) for managing critically ill adult patient blood glucose. We monitored clinician compliance with eProtocol-insulin recommendations in four intensive care units in four hospitals and compared blood glucose distributions with those of a simple clinical guideline at one hospital and a paper-based protocol at another. All protocols and the guideline used intravenous insulin and 80 to 110 mg/dL (4.4-6.1 mmol/L) blood glucose targets.
SETTING: The setting for this study was four academic hospital intensive care units. PATIENTS: This study included critically ill adults requiring intravenous insulin. INTERVENTION: Intervention used in this study was a bedside computerized protocol for managing blood glucose. MAIN OUTCOME MEASURE: The main outcome measure was clinician compliance with eProtocol-insulin recommendations.
RESULTS: The number of patients was 31 to 458 and the number of blood glucose measurements was 2,226 to 19,925 among the four intensive care units. Clinician compliance with eProtocol-insulin recommendations was 91% to 98%. Blood glucose distributions were similar in the four hospitals (generalized linear model p = .18). Compared with the simple guideline, eProtocol-insulin glucose measurements within target increased from 21% to 39%, and mean blood glucose decreased from 142 to 115 mg/dL (generalized linear model p < .001). Compared with the paper-based protocol, eProtocol-insulin glucose measurements within target increased from 28% to 42%, and mean blood glucose decreased from 134 to 116 mg/dL (generalized linear model p = .001).
CONCLUSIONS: The 91% to 98% clinician compliance indicates eProtocol-insulin is an exportable instrument that can establish a replicable experimental method for clinical trials of blood glucose management in critically ill adults. Control of blood glucose was better with eProtocol-insulin than with a simple clinical guideline or a paper-based protocol.

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Year:  2008        PMID: 18520641     DOI: 10.1097/CCM.0b013e3181743a5a

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  42 in total

1.  Interface design and human factors considerations for model-based tight glycemic control in critical care.

Authors:  Logan Ward; James Steel; Aaron Le Compte; Alicia Evans; Chia-Siong Tan; Sophie Penning; Geoffrey M Shaw; Thomas Desaive; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

2.  The evolution of eProtocols that enable reproducible clinical research and care methods.

Authors:  Denitza P Blagev; Eliotte L Hirshberg; Katherine Sward; B Taylor Thompson; Roy Brower; Jonathon Truwit; Duncan Hite; Jay Steingrub; James F Orme; Terry P Clemmer; Lindell K Weaver; Frank Thomas; Colin K Grissom; Dean Sorenson; Dean F Sittig; C Jane Wallace; Thomas D East; Homer R Warner; Alan H Morris
Journal:  J Clin Monit Comput       Date:  2012-04-11       Impact factor: 2.502

3.  Data entry errors and design for model-based tight glycemic control in critical care.

Authors:  Logan Ward; James Steel; Aaron Le Compte; Alicia Evans; Chia-Siong Tan; Sophie Penning; Geoffrey M Shaw; Thomas Desaive; J Geoffrey Chase
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

Review 4.  The role of computer-based clinical decision support systems to deliver protective mechanical ventilation.

Authors:  Robinder G Khemani; Justin C Hotz; Katherine A Sward; Christopher J L Newth
Journal:  Curr Opin Crit Care       Date:  2020-02       Impact factor: 3.687

5.  Barriers and facilitators to the use of computer-based intensive insulin therapy.

Authors:  Thomas R Campion; Lemuel R Waitman; Nancy M Lorenzi; Addison K May; Cynthia S Gadd
Journal:  Int J Med Inform       Date:  2011-10-21       Impact factor: 4.046

6.  An electronic protocol for translation of research results to clinical practice: a preliminary report.

Authors:  Alan H Morris; James Orme; Beatriz H Rocha; John Holmen; Terry Clemmer; Nancy Nelson; Jode Allen; Al Jephson; Dean Sorenson; Kathy Sward; Homer Warner
Journal:  J Diabetes Sci Technol       Date:  2008-09

7.  Intermediary variables and algorithm parameters for an electronic algorithm for intravenous insulin infusion.

Authors:  Susan S Braithwaite; Hemant Godara; Julie Song; Bruce A Cairns; Samuel W Jones; Guillermo E Umpierrez
Journal:  J Diabetes Sci Technol       Date:  2009-07-01

Review 8.  Glycemic control in the burn intensive care unit: focus on the role of anemia in glucose measurement.

Authors:  Elizabeth A Mann; Alejandra G Mora; Heather F Pidcoke; Steven E Wolf; Charles E Wade
Journal:  J Diabetes Sci Technol       Date:  2009-11-01

9.  Glucose Regulation in Acute Stroke Patients (GRASP) trial: a randomized pilot trial.

Authors:  Karen C Johnston; Christiana E Hall; Brett M Kissela; Thomas P Bleck; Mark R Conaway
Journal:  Stroke       Date:  2009-10-15       Impact factor: 7.914

Review 10.  Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control.

Authors:  Miriam Hoekstra; Mathijs Vogelzang; Evgeny Verbitskiy; Maarten W N Nijsten
Journal:  Crit Care       Date:  2009-10-16       Impact factor: 9.097

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