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Automation of insulin bolus dose calculation in type 1 diabetes: a feasibility study

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International Journal of Diabetes in Developing Countries Aims and scope Submit manuscript

Abstract

Background

Diabetes care in type 1 diabetes has remained a challenge over time. Ability to count carbohydrates in every meal and varying insulin dose according to individual insulin/carbohydrate ratio allows people with type 1 DM to have wider food choices with better glycemic control. Difficulties in carbohydrate counting may largely be solved by use of technology.

Methods

This work was done at the endocrine unit of a superspeciality centre involved in care of people with type 1 diabetes. The process of development of software and its preliminary application and results from its use in clinical care in a small group of interested patients is presented in this manuscript. Carbohydrate counting tool for Indian foods was developed, and subsequently, bolus dose calculation was automated by using the reinforcement algorithm in an android app platform named “T1-Life”. Data on app usability and acceptability is documented in this pilot study report.

Results

Five patients completed 3 months of this app usage. Among five people with combined usage over 115 patient weeks, a total of 2661 insulin dose predictions were made. This translates to 3.31 patient-initiated bolus dose predictions per day. Of the total bolus dose predictions made, 82% were accepted by the participants. With usage of app, time in range (70–180 mg/dl) increased by an average of 16.67% in children who used CGMS in their first week as well as last week of observation.

Conclusion

T1-life, integrated carbohydrate counting and reinforcement-based insulin bolus dose prediction system, has good patient usability and acceptability.

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References

  1. Kumar P, Krishna P, Reddy SC, Gurappa M, Aravind SR, Munichoodappa C. Incidence of type 1 diabetes mellitus and associated complications among children and young adults: results from Karnataka Diabetes Registry 1995-2008. J Indian Med Assoc. 2008;106(11):708–11.

    PubMed  Google Scholar 

  2. Kalra S, Kalra B, Sharma A. Prevalence of type 1 diabetes mellitus in Karnal district, Haryana state, India. Diabetol Metab Syndr. 2010;2:14.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kumar KMP. Incidence trends for childhood type 1 diabetes in India. Indian J Endocrinol Metab. 2015;19(Suppl 1):S34–5.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kahkoska AR, Shay CM, Crandell J, et al. Association of race and ethnicity with glycemic control and hemoglobin A levels in youth with type 1 diabetes. JAMA Netw Open. 2018;1(5). https://doi.org/10.1001/jamanetworkopen.2018.1851.

  5. Niba LL, Aulinger B, Mbacham WF, Parhofer KG. Predictors of glucose control in children and adolescents with type 1 diabetes: results of a cross-sectional study in Cameroon. BMC Res Notes. 2017;10(1):207.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Schoenaker DAJM, Simon D, Chaturvedi N, Fuller JH, Soedamah-Muthu SS. EURODIAB Prospective Complications Study Group. Glycemic control and all-cause mortality risk in type 1 diabetes patients: the EURODIAB prospective complications study. J Clin Endocrinol Metab. 2014;99(3):800–7.

    Article  CAS  PubMed  Google Scholar 

  7. Unnikrishnan AG, Bhatia E, Bhatia V, et al. Type 1 diabetes versus type 2 diabetes with onset in persons younger than 20 years of age. Ann N Y Acad Sci. 2008;1150:239–44.

    Article  CAS  PubMed  Google Scholar 

  8. Agiostratidou G, Anhalt H, Ball D, et al. Standardizing clinically meaningful outcome measures beyond HbA for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange. Diabetes Care. 2017;40(12):1622–30.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Schmidt S, Schelde B, Nørgaard K. Effects of advanced carbohydrate counting in patients with type 1 diabetes: a systematic review. Diabet Med. 2014;31(8):886–96.

    Article  CAS  PubMed  Google Scholar 

  10. Fu S, Li L, Deng S, Zan L, Liu Z. Effectiveness of advanced carbohydrate counting in type 1 diabetes mellitus: a systematic review and meta-analysis. Sci Rep. 2016;6:37067.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bell KJ, Barclay AW, Petocz P, Colagiuri S, Brand-Miller JC. Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2014;2(2):133–40.

    Article  PubMed  Google Scholar 

  12. Hommel E, Schmidt S, Vistisen D, et al. Effects of advanced carbohydrate counting guided by an automated bolus calculator in type 1 diabetes mellitus (StenoABC): a 12-month, randomized clinical trial. Diabet Med. 2017;34(5):708–15.

    Article  CAS  PubMed  Google Scholar 

  13. Website. Accessed December 26, 2019. Herrero, Pau, López, Beatriz, & Martin, Clare. PEPPER: patient empowerment through predictive personalised decision support. Zenodo. 2016. https://doi.org/10.5281/zenodo.427542.

  14. Patient empowerment through predictive personalised decision support (PEPPER)-validation study. - Tabular View - ClinicalTrials.gov. Accessed December 26, 2019. https://clinicaltrials.gov/ct2/show/record/NCT03849755

  15. Pesl P, Herrero P, Reddy M, et al. An advanced bolus calculator for type 1 diabetes: system architecture and usability results. IEEE J Biomed Health Inform. 2016;20(1):11–7.

    Article  PubMed  Google Scholar 

  16. Pesl P, Herrero P, Reddy M, et al. Case-based reasoning for insulin bolus advice. J Diabetes Sci Technol. 2017;11(1):37–42. https://doi.org/10.1177/1932296816629986.

    Article  PubMed  Google Scholar 

  17. Bozzetto L, Giorgini M, Alderisio A, et al. Glycemic load versus carbohydrate counting for insulin bolus calculation in patients with type 1 diabetes on insulin pump. Acta Diabetol. 2015;52(5):865–71.

    Article  CAS  PubMed  Google Scholar 

  18. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593–603.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Beck RW, Bergenstal RM, Cheng P, et al. The relationships between time in range, hyperglycemia metrics, and HbA1c. J Diabetes Sci Technol. 2019;13(4):614–26.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Vigersky RA, McMahon C. The relationship of hemoglobin A1C to time-in-range in patients with diabetes. Diabetes Technol Ther. 2019;21(2):81–5.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgment

Contributions of Amit Lahoti and Vineet Surana are acknowledged for their valuable inputs towards study design and analysis of data.

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Authors and Affiliations

  1. Department of Endocrinology, Kalpavriksh Clinic, Kalpavriksh Healthcare, 96, Sector 13, Dwarka, Delhi, 110078, India

    Rajiv Singla

  2. Department of Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India

    Jatin Bindra, Ankush Singla & Shivam Aggarwal

  3. Department of Nutrition, Kalpavriksh Healthcare, Dwarka, Delhi, India

    Geetu Gupta

  4. Department of Endocrinology, All India Institute of Medical Sciences, Delhi, India

    Yashdeep Gupta

Authors
  1. Rajiv Singla
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  2. Jatin Bindra
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  3. Ankush Singla
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  4. Geetu Gupta
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  5. Yashdeep Gupta
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  6. Shivam Aggarwal
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Contributions

R.S., G.G., J.B., and A.S. performed the research. R.S., A.S., and Y.G. designed the research study. R.S., J.B., S.A., and A.S. analysed the data. R.S., J.B., and Y.G. wrote the paper. All authors critically edited and endorsed the manuscript.

Corresponding author

Correspondence to Rajiv Singla.

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Ethics approval

This study was carried out as part of routine clinical care and is automation of clinical care of type 1 diabetes participants. Data was collated retrospectively.

Conflict of interest

The authors declare no competing interests.

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Singla, R., Bindra, J., Singla, A. et al. Automation of insulin bolus dose calculation in type 1 diabetes: a feasibility study. Int J Diabetes Dev Ctries 43, 66–71 (2023). https://doi.org/10.1007/s13410-022-01054-7

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  • DOI: https://doi.org/10.1007/s13410-022-01054-7

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