Literature DB >> 22071011

Effects of two different levels of computerized decision support on blood glucose regulation in critically ill patients.

Saeid Eslami1, Nicolette F de Keizer, Dave A Dongelmans, Evert de Jonge, Marcus J Schultz, Ameen Abu-Hanna.   

Abstract

INTRODUCTION: Although the use of computerized decision support systems (CDSS) in glucose control in the ICU has been reported, little is known about the effect of the systems' operating modes on the quality of glucose control. The objective of this study was to evaluate the effect of providing patient-specific and patient non-specific computerized advice on timing of blood glucose level (BGL) measurements. Our hypothesis was that both levels of support would be effective for improving the quality of glucose regulation and safety, with patient specific advice being the most effective strategy. PATIENTS AND METHODS: A prospective study was performed in a 30-bed mixed medical-surgical intensive care unit (ICU) of a university hospital. In phase 1 the CDSS provided non-specific advice and thereafter, in phase 2, the system provided specific advice on timing of BGL measurements. The primary outcome measure was delay in BGL measurements before and after the two levels of support. Secondary endpoints were sampling frequency, mean BGL, BGL within pre-defined targets, time to capture target, incidences of severe hypoglycemia and hyperglycemia. These indicators were analyzed over the course of time using Statistical Control Charts. The analysis was restricted to patients with at least two blood glucose measurements.
RESULTS: Data of 3934 patient admissions were evaluated, which corresponded to 119,116 BGL measurements. The BGL sampling interval, delays in BG sampling, and percentage of hypoglycemia all decreased after introducing either of the two levels of decision support. The effect was however larger for the patient specific CDSS. Mean BGL, time to capture target, hyperglycemia index, percentage of hyperglycemia events and "in range" measurements remained unchanged and stable after introducing both patient non-specific and patient specific decision support.
CONCLUSION: Adherence to protocol sampling rules increased by using decision support with a larger effect at the patient specific level. This led to a decrease in the percentage of hypoglycemia events and improved safety. The use of the CDSS at both levels, however, did not improve the quality of glucose control as measured by our indicators. More research is needed to investigate whether other socio-technical factors are in play.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Year:  2011        PMID: 22071011     DOI: 10.1016/j.ijmedinf.2011.10.004

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  8 in total

1.  A Pediatric Intensive Care Unit Bedside Computer Clinical Decision Support Protocol for Hyperglycemia Is Feasible, Safe and Offers Advantages.

Authors:  Eliotte L Hirshberg; Michael J Lanspa; Emily L Wilson; Katherine A Sward; Al Jephson; Gitte Y Larsen; Alan H Morris
Journal:  Diabetes Technol Ther       Date:  2017-03-01       Impact factor: 6.118

Review 2.  Computerized decision support in adult and pediatric critical care.

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

3.  Point accuracy and reliability of an interstitial continuous glucose-monitoring device in critically ill patients: a prospective study.

Authors:  Roosmarijn T M van Hooijdonk; Jan Hendrik Leopold; Tineke Winters; Jan M Binnekade; Nicole P Juffermans; Janneke Horn; Johan C Fischer; Edmée C van Dongen-Lases; Marcus J Schultz
Journal:  Crit Care       Date:  2015-02-05       Impact factor: 9.097

4.  Accuracy and limitations of continuous glucose monitoring using spectroscopy in critically ill patients.

Authors:  Roosmarijn Tm van Hooijdonk; Tineke Winters; Johan C Fischer; Edmée C van Dongen-Lases; James S Krinsley; Jean-Charles Preiser; Marcus J Schultz
Journal:  Ann Intensive Care       Date:  2014-03-06       Impact factor: 6.925

5.  Estimating the Time to Benefit for Preventive Drugs with the Statistical Process Control Method: An Example with Alendronate.

Authors:  Esther M M van de Glind; Hanna C Willems; Saeid Eslami; Ameen Abu-Hanna; Willem F Lems; Lotty Hooft; Sophia E de Rooij; Dennis M Black; Barbara C van Munster
Journal:  Drugs Aging       Date:  2016-05       Impact factor: 3.923

6.  Associations between bolus infusion of hydrocortisone, glycemic variability and insulin infusion rate variability in critically Ill patients under moderate glycemic control.

Authors:  Roosmarijn T M van Hooijdonk; Jan M Binnekade; Lieuwe D J Bos; Janneke Horn; Nicole P Juffermans; Ameen Abu-Hanna; Marcus J Schultz
Journal:  Ann Intensive Care       Date:  2015-11-02       Impact factor: 6.925

Review 7.  An overview of clinical decision support systems: benefits, risks, and strategies for success.

Authors:  Reed T Sutton; David Pincock; Daniel C Baumgart; Daniel C Sadowski; Richard N Fedorak; Karen I Kroeker
Journal:  NPJ Digit Med       Date:  2020-02-06

8.  Performance of an Electronic Decision Support System as a Therapeutic Intervention During a Multicenter PICU Clinical Trial: Heart and Lung Failure-Pediatric Insulin Titration Trial (HALF-PINT).

Authors:  Eliotte L Hirshberg; Jamin L Alexander; Lisa A Asaro; Kerry Coughlin-Wells; Garry M Steil; Debbie Spear; Cheryl Stone; Vinay M Nadkarni; Michael S D Agus
Journal:  Chest       Date:  2021-04-29       Impact factor: 9.410

  8 in total

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