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Exp Clin Endocrinol Diabetes 2023; 131(10): 539-547
DOI: 10.1055/a-2118-2011
Article

Prospective External Validation of an Algorithm Predicting Hourly Basal Insulin Infusion Rates from Characteristics of Patients with Type 1 Diabetes Treated with Insulin Pumps

Jana S. Schmelzer
1   Diabetes, Endocrinology and Metabolism Section, Medical Department I, Katholisches Klinikum Bochum, St. Josef-Hospital, Klinikum der Ruhr-Universität Bochum, Bochum, Germany
2   Department of Internal Medicine, Augusta-Krankenanstalt gGmbH, Bochum, Germany
,
Melanie Kahle-Stephan
1   Diabetes, Endocrinology and Metabolism Section, Medical Department I, Katholisches Klinikum Bochum, St. Josef-Hospital, Klinikum der Ruhr-Universität Bochum, Bochum, Germany
3   Diabetes Center Bad Lauterberg, Bad Lauterberg im Harz, Germany
,
Juris J. Meier
1   Diabetes, Endocrinology and Metabolism Section, Medical Department I, Katholisches Klinikum Bochum, St. Josef-Hospital, Klinikum der Ruhr-Universität Bochum, Bochum, Germany
2   Department of Internal Medicine, Augusta-Krankenanstalt gGmbH, Bochum, Germany
,
Michael A. Nauck
1   Diabetes, Endocrinology and Metabolism Section, Medical Department I, Katholisches Klinikum Bochum, St. Josef-Hospital, Klinikum der Ruhr-Universität Bochum, Bochum, Germany
3   Diabetes Center Bad Lauterberg, Bad Lauterberg im Harz, Germany
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Abstract

Background We previously published an algorithm predicting 24 h basal insulin infusion profiles in insulin pump-treated subjects with type 1 diabetes profiles from six subject characteristics. This algorithm was to be externally validated in an independent environment and patient population.

Methods Thirty-two patients with pump-treated type diabetes were switched to their individually algorithm-derived basal insulin infusion profile, and the appropriateness of fasting glycemic control was scrutinized by means of a supervised 24 h fast. Primary endpoint was appropriate fasting glycemic control according to pre-defined criteria in at least 80% of the cohort.

Results In 24 out of 32 patients switching to the algorithm-derived basal insulin infusion rate and undergoing a 24-h fasting period, appropriate glycemic control was achieved (=75%, lower than the pre-defined threshold of 80%), two patients discontinued the fast due to hyperglycemia, and six finished the fasting period, however, with inappropriate fasting glycemic control (entirely due to hyperglycemic episodes). There were no obvious differences in baseline characteristics between those with appropriate vs. inappropriate fasting glycemic control on the basal insulin infusion rate provided by the algorithm.

Conclusion In conclusion, when testing fasting glycemic control with an algorithm-derived individual basal insulin infusion profile during a 24 h fasting period in a cohort unrelated in terms of the hospital environment and catchment area, the success rate was lower than a pre-defined threshold for concluding utility of this algorithm. Therefore, applying this algorithm in order to initiate or optimize basal insulin infusion profiles in type 1 diabetes cannot be generally recommended.

Key words

type 1 diabetes - insulin pump (CSII: Continuous subcutaneous insulin infusion) - basal rate profiles - dawn phenomenon - dusk phenomenon

Additional material



Publication History

Received: 04 March 2023
Received: 13 June 2023

Accepted: 20 June 2023

Article published online:
20 July 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Pickup JC. Insulin-pump therapy for type 1 diabetes mellitus. New Engl J Med 2012; 366: 1616-1624
    CrossrefPubMedSearch in Google Scholar
  • 2 Pickup J, Mattock M, Kerry S. Glycaemic control with continuous subcutaneous insulin infusion compared with intensive insulin injections in patients with type 1 diabetes: Meta-analysis of randomised controlled trials. BMJ 2002; 324: 705-709
    CrossrefPubMedSearch in Google Scholar
  • 3 Nauck MA, Lindmeyer AM, Mathieu C. et al. Twenty-four hour fasting (basal rate) tests to achieve custom-tailored, hour-by-hour basal insulin infusion rates in patients with type 1 diabetes using insulin pumps (CSII). J Diabetes Sci Technol 2019; 1932296819882752
    CrossrefPubMedSearch in Google Scholar
  • 4 Lindmeyer AM, Meier JJ, Nauck MA. Patients with type 1 diabetes treated with insulin pumps need widely heterogeneous basal rate profiles ranging from negligible to pronounced diurnal variability. J Diabetes Sci Technol 2020; 1932296820949939
    CrossrefPubMedSearch in Google Scholar
  • 5 Nauck MA, Kahle-Stephan M, Lindmeyer AM. et al. Prediction of individual basal rate profiles from patient characteristics in type 1 diabetes on insulin pump therapy. J Diabetes Sci Technol 2021; 15: 1273-1281
    CrossrefPubMedSearch in Google Scholar
  • 6 Battelino T, Alexander CM, Amiel SA. et al. Continuous glucose monitoring and metrics for clinical trials: An international consensus statement. Lancet Diabetes Endocrinol 2023; 11: 42-57
    CrossrefPubMedSearch in Google Scholar
  • 7 Bulum T, Kolaric B, Duvnjak L. et al. Nonalcoholic fatty liver disease markers are associated with insulin resistance in type 1 diabetes. Dig Dis Sci 2011; 56: 3655-3663
    CrossrefPubMedSearch in Google Scholar
  • 8 Snaith JR, Danta M, Greenfield JR. Metabolic dysfunction-associated fatty liver disease and insulin resistance in type 1 diabetes. Lancet Gastroenterol Hepatol 2021; 6: 985-986
    CrossrefPubMedSearch in Google Scholar
  • 9 Heo JE, Shim JS, Lee H. et al. Association between the thigh muscle and insulin resistance according to body mass index in middle-aged Korean adults. Diabetes Metab J 2020; 44: 446-457
    CrossrefPubMedSearch in Google Scholar
  • 10 Srikanthan P, Karlamangla AS. Relative muscle mass is inversely associated with insulin resistance and prediabetes. Findings from the third National Health and Nutrition Examination Survey. J Clin Endocrinol Metab 2011; 96: 2898-2903
    CrossrefPubMedSearch in Google Scholar
  • 11 Jelleyman C, Edwardson CL, Henson J. et al. Associations of physical activity intensities with markers of insulin sensitivity. Med Sci Sports Exerc 2017; 49: 2451-2458
    CrossrefPubMedSearch in Google Scholar
  • 12 Mayer-Davis EJ, D'Agostino R, Karter AJ. et al. Intensity and amount of physical activity in relation to insulin sensitivity: The Insulin Resistance Atherosclerosis Study. JAMA 1998; 279: 669-674
    CrossrefPubMedSearch in Google Scholar
  • 13 Beato-Vibora PI, Gil-Poch E, Galan-Bueno L. et al. The incremental benefits of the predictive low-glucose suspend function compared to the low-glucose suspend function as automation against hypoglycemia in sensor-augmented pump therapy. J Diabetes Sci Technol 2018; 12: 1241-1243
    CrossrefPubMedSearch in Google Scholar
  • 14 Bergenstal RM, Nimri R, Beck RW. et al. A comparison of two hybrid closed-loop systems in adolescents and young adults with type 1 diabetes (FLAIR): A multicentre, randomised, crossover trial. Lancet 2021; 397: 208-219
    CrossrefPubMedSearch in Google Scholar