Literature DB >> 22581038

Decision support for optimized blood glucose control and nutrition in a neurotrauma intensive care unit: preliminary results of clinical advice and prediction accuracy of the Glucosafe system.

Ulrike Pielmeier1, Mark Lillelund Rousing, Steen Andreassen, Birgitte Steenfeldt Nielsen, Pernille Haure.   

Abstract

Assessment of glycemic control with model-based decision support ("Glucosafe") in neurotrauma intensive care patients in an ongoing randomized controlled trial with a blood glucose (BG) target of 5-8 mmol/L. Assessment of BG prediction accuracy of the model and assessment of the effect that two potential model extensions would have on prediction accuracy in this trial. In the intervention group insulin infusion rates and nutrition are varied based on Glucosafe's decision support. In the control group, the caloric target is 25-30 kcal/kg per day and insulin is regulated according to department rules. BG concentrations, insulin infusion rates, and feed rates are compared from the data of 12 consecutive patients. BG measurements are predicted retrospectively and the mean relative prediction error is calculated using (1) the current model from the trial, (2) the current model modified by using a BG-dependent variable endogenous insulin appearance rate, (3) the current model modified by a patient-specific carbohydrate absorption factor. BG control was improved by Glucosafe. 76 % of BG measurements in Glucosafe patients were in the 5-8 mmol/L band (Controls: 51 %). BG means (log-normal) ± SD were 7.0 ± 1.19 mmol/L in Glucosafe patients compared to 8.0 ± 1.24 mmol/L in controls (P = 0.05). Mean caloric intake was 93.5 ± 15 % of resting energy expenditure in Glucosafe patients (Controls: 129 ± 29 %). The BG-dependent variable insulin appearance rate had no measurable effect on prediction accuracy. The patient-specific carbohydrate absorption factor improved prediction accuracy significantly (P = 0.001). Glucosafe advice reduces hyperglycemia in neurotrauma intensive care patients. Further parameterization can improve model prediction accuracy.

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Year:  2012        PMID: 22581038     DOI: 10.1007/s10877-012-9364-y

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  25 in total

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2.  Two-hour seven-sample oral glucose tolerance test and meal protocol: minimal model assessment of beta-cell responsivity and insulin sensitivity in nondiabetic individuals.

Authors:  Chiara Dalla Man; Marco Campioni; Kenneth S Polonsky; Rita Basu; Robert A Rizza; Gianna Toffolo; Claudio Cobelli
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3.  Glycemic control and critical illness: is the kidney involved?

Authors:  Ravindra L Mehta
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4.  The Glucosafe system for tight glycemic control in critical care: a pilot evaluation study.

Authors:  Ulrike Pielmeier; Steen Andreassen; Brian Juliussen; J Geoffrey Chase; Birgitte Steenfeldt Nielsen; Pernille Haure
Journal:  J Crit Care       Date:  2010-03       Impact factor: 3.425

Review 5.  Intensive insulin therapy in critical care: a review of 12 protocols.

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8.  Poor glycemic control is associated with increased mortality in critically ill trauma patients.

Authors:  Stephen C Gale; Corinna Sicoutris; Patrick M Reilly; C William Schwab; Vicente H Gracias
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9.  Glucose absorption and gastric emptying in critical illness.

Authors:  Marianne J Chapman; Robert J L Fraser; Geoffrey Matthews; Antonietta Russo; Max Bellon; Laura K Besanko; Karen L Jones; Ross Butler; Barry Chatterton; Michael Horowitz
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Journal:  J Am Med Inform Assoc       Date:  2015-07-30       Impact factor: 4.497

5.  Usability study of a new tool for nutritional and glycemic management in adult intensive care: Glucosafe 2.

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Authors:  Cydni N Williams; Susan L Bratton; Eliotte L Hirshberg
Journal:  World J Crit Care Med       Date:  2013-11-04

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Journal:  J Am Med Inform Assoc       Date:  2021-06-12       Impact factor: 4.497

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Journal:  Biomed Eng Online       Date:  2018-02-20       Impact factor: 2.819

  10 in total

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