Download PDF
Appl Clin Inform 2014; 05(02): 548-556
DOI: 10.4338/ACI-2014-04-RA-0033
Research Article – ehealth2014 special topic
Schattauer GmbH

A toolbox to improve algorithms for insulin-dosing decision support

K. Donsa
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
P. Beck
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
J. Plank
2   Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria
,
L. Schaupp
2   Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria
,
J. K. Mader
2   Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria
,
T. Truskaller
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
B. Tschapeller
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
B. Höll
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
S. Spat
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
,
T. R. Pieber
1   HEALTH – Institute for Biomedicine and Health Sciences, JOANNEUM RESEARCH Forschungsgesellschaft mbH, Graz, Austria
2   Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Graz, Austria
› Author Affiliations
Further Information

Publication History

Received: 03 April 2014

Accepted: 30 April 2014

Publication Date:
21 December 2017 (online)

   Permissions and Reprints

Summary

Background: Standardized insulin order sets for subcutaneous basal-bolus insulin therapy are recommended by clinical guidelines for the inpatient management of diabetes. The algorithm based GlucoTab system electronically assists health care personnel by supporting clinical workflow and providing insulin-dose suggestions.

Objective: To develop a toolbox for improving clinical decision-support algorithms.

Methods: The toolbox has three main components. 1) Data preparation: Data from several heterogeneous sources is extracted, cleaned and stored in a uniform data format. 2) Simulation: The effects of algorithm modifications are estimated by simulating treatment workflows based on real data from clinical trials. 3) Analysis: Algorithm performance is measured, analyzed and simulated by using data from three clinical trials with a total of 166 patients.

Results: Use of the toolbox led to algorithm improvements as well as the detection of potential individualized subgroup-specific algorithms.

Conclusion: These results are a first step towards individualized algorithm modifications for specific patient subgroups.

Citation: Donsa K, Beck P, Plank J, Schaupp L, Mader JK, Truskaller T, Tschapeller B, Höll B, Spat S, Pieber TR. A toolbox to improve algorithms for insulin-dosing decision support. Appl Clin Inf 2014; 5: 548–556 http://dx.doi.org/10.4338/ACI-04-RA-0033

Keywords

clinical decision support systems - workflow - algorithms - computer simulation - diabetes mellitus type 2
 

  • References

  • 1 Umpierrez GE. Hyperglycemia: An Independent Marker of In-Hospital Mortality in Patients with Undiag-nosed Diabetes. J Clin Endocrinol Metab 2002; 87 (Suppl. 03) 978-982.
    CrossrefPubMedGoogle Scholar
  • 2 Rayman G. National Health Service.. National Diabetes Inpatient Audit 2012. United Kingdom; 2013.
    PubMedGoogle Scholar
  • 3 Umpierrez GE, Smiley D, Zisman A, Prieto LM, Palacio A, Ceron M. et al. Randomized study of basal-bolus insulin therapy in the inpatient management of patients with type 2 diabetes (RABBIT 2 trial). Diabetes Care 2007; 30 (Suppl. 09) 2181-2186.
    CrossrefPubMedGoogle Scholar
  • 4 Umpierrez GE, Hor T, Smiley D, Temponi A, Umpierrez D, Ceron M. et al. Comparison of inpatient insulin regimens with detemir plus aspart versus neutral protamine hagedorn plus regular in medical patients with type 2 diabetes. J Clin Endocrinol Metab 2009; 94 (Suppl. 02) 564-569.
    CrossrefPubMedGoogle Scholar
  • 5 Umpierrez GE, Smiley D, Jacobs S, Peng L, Temponi A, Mulligan P. et al. Randomized study of basal-bolus insulin therapy in the inpatient management of patients with type 2 diabetes undergoing general surgery (RABBIT 2 surgery). Diabetes Care 2011; 34 (Suppl. 02) Supplement.
    PubMedGoogle Scholar
  • 6 Umpierrez GE, Smiley D, Hermayer K, Khan A, Olson DE, Newton C. et al. Randomized study comparing a Basal-bolus with a basal plus correction insulin regimen for the hospital management of medical and surgical patients with type 2 diabetes: basal plus trial. Diabetes Care 2013; 36 (Suppl. 08) 2169-2174.
    CrossrefPubMedGoogle Scholar
  • 7 McDonnell ME, Umpierrez GE. Insulin therapy for the management of hyperglycemia in hospitalized patients. Endocrinol Metab Clin North Am 2012; 41 (Suppl. 01) 175-201.
    CrossrefPubMedGoogle Scholar
  • 8 Mader JK, Neubauer KM, Schaupp L, Augustin T, Beck P, Spat S. et al. Efficacy, usability and sequence of operations of a workflow-integrated algorithm for basal-bolus insulin therapy in hospitalized type 2 diabetes patients. Diabetes Obes Metab 2013; 16 (Suppl. 02) 137-146.
    PubMedGoogle Scholar
  • 9 ICH Expert Working Group.. Guideline for Good Clinical Practice E6 (R1). ICH of Technical Requirements for Registration of Pharmaceuticals for Human Use. 1996
    PubMedGoogle Scholar
  • 10 Zainuddin Z, Pauline O, Ardil C. A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients. Int J Comput Intell 2009; 5: 1-8.
    PubMedGoogle Scholar
  • 11 Quchani S, Tahami E. Comparison of MLP and Elman Neural Network for Blood Glucose Level Prediction in Type 1 Diabetics. 3rd Kuala Lumpur Int Conf Biodemical Eng 2007; 15: 54-58.
    PubMedGoogle Scholar
  • 12 Price CP. Point-of-care testing in diabetes mellitus. Clin Chem Lab Med 2003; 41 (Suppl. 09) 1213-1219.
    PubMedGoogle Scholar
  • 13 Zijlstra E, Heise T, Nosek L, Heinemann L, Heckermann S. Continuous glucose monitoring: quality of hypoglycaemia detection. Diabetes Obes Metab 2013; 15 (Suppl. 02) 130-135.
    PubMedGoogle Scholar
  • 14 R Development Core Team.. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011
    Google Scholar