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Peer-reviewedCC BY-NC 4.0Journal of Diabetes Science and Technology

Evaluation of Diabetic Ketoacidosis Outcomes Following the Adoption of a Computer-Based Variable-Rate Insulin Infusion Algorithm

  • Margaret Wei1,2
  • Omar Kais Ibrahim3
  • Jacqueline Marie Deirmenjian1,4
  • Tochukwu Madu2,4,5
  • Gian Paolo Simone6
  • Steve Kim7
  • Elizabeth Vi Nguyen7
  • Cameron Escovedo8,9
  • Hai Tran7
  • Shawna Masih1
  • Nichole McCann Ballweg3
  • Elizabeth Zakis10
2 more authors
  • Jennifer Ting-Chan11
  • Roma Gianchandani1
11 affiliations
  1. 1Division of Endocrinology, Diabetes, and Metabolism, Department of Internal Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
  2. 2Division of Endocrinology, Diabetes, and Metabolism, Los Angeles General Medical Center, CA, USA
  3. 3Department of Enterprise Information Services, Cedars Sinai Health System, Los Angeles, CA, USA
  4. 4Department of Internal Medicine, Huntington Hospital, Pasadena, CA, USA
  5. 5Division of Endocrinology, Diabetes, and Metabolism, Department of Internal Medicine, Keck School of Medicine, Los Angeles, CA, USA
  6. 6Data and Analytics Delivery, Cedars-Sinai Health Systems, Los Angeles, CA, USA
  7. 7Department of Clinical Pharmacy, Cedars-Sinai Medical Center, Los Angeles, CA, USA
  8. 8Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
  9. 9Department of Pediatrics, Cedars-Sinai Guerin Children’s, Los Angeles, CA, USA
  10. 10Department of Nursing, Huntington Hospital, Pasadena, CA, USA
  11. 11Department of Pharmacy Services, Huntington Hospital, Pasadena, CA, USA
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Abstract

Background:: Diabetic ketoacidosis (DKA) management by variable-rate insulin infusion is increasing with the widespread adoption of electronic glucose management systems (eGMS). Herein, we evaluate the implementation of an updated process for DKA management incorporating the eGMS algorithm.

Methods:: An eGMS was implemented within the Cedars-Sinai Health System at our tertiary academic center (AC) and community hospital (CH) in January 2024. We compared outcomes after eGMS implementation (post-GMS) to the previous standard of care (pre-GMS). Adult patients admitted to the intensive care unit with a primary diagnosis of DKA and treated with an insulin infusion between January 2023 and December 2024 were included. We separately evaluated outcomes of hypoglycemia (blood glucose, BG <70 mg/dL) and severe hypoglycemia (BG <40 mg/dL) and treatment efficacy.

Results:: In total, 324 patients were included in the pre-GMS and 211 patients in the post-GMS cohorts. There was a decrease in the prevalence of hypoglycemia (pre-GMS: 11.0%, post-GMS: 4.3%, P = .005) and severe hypoglycemia (pre-GMS: 1.90%, post-GMS: 0.00%, P = .047). At AC, the hospital length of stay (LOS) decreased from 8.6 to 6.0 days (P = .012). At CH, insulin drip duration increased significantly (pre-GMS: 26.4 h, post-GMS: 36.7 h, P = .004), but there was no impact on LOS.

Conclusion:: Updating the DKA process and implementing an eGMS improved hypoglycemia rates throughout the institution. Hospital LOS was reduced at the AC, reflecting standardization and streamlining of the DKA processes.

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Cite

BibTeXRIS
BibTeX
@article{wei2026evaluation,
  title = {{Evaluation of Diabetic Ketoacidosis Outcomes Following the Adoption of a Computer-Based Variable-Rate Insulin Infusion Algorithm}},
  author = {Margaret Wei and Omar Kais Ibrahim and Jacqueline Marie Deirmenjian and Tochukwu Madu and Gian Paolo Simone and Steve Kim and Elizabeth Vi Nguyen and Cameron Escovedo and Hai Tran and Shawna Masih and Nichole McCann Ballweg and Elizabeth Zakis and Jennifer Ting-Chan and Roma Gianchandani},
  journal = {Journal of Diabetes Science and Technology},
  year = {2026},
  month = nov,
  doi = {10.1177/19322968261501915},
  url = {https://doi.org/10.1177/19322968261501915},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Evaluation of Diabetic Ketoacidosis Outcomes Following the Adoption of a Computer-Based Variable-Rate Insulin Infusion Algorithm
AU  - Margaret Wei
AU  - Omar Kais Ibrahim
AU  - Jacqueline Marie Deirmenjian
AU  - Tochukwu Madu
AU  - Gian Paolo Simone
AU  - Steve Kim
AU  - Elizabeth Vi Nguyen
AU  - Cameron Escovedo
AU  - Hai Tran
AU  - Shawna Masih
AU  - Nichole McCann Ballweg
AU  - Elizabeth Zakis
AU  - Jennifer Ting-Chan
AU  - Roma Gianchandani
PY  - 2026
DA  - 2026/11/03
JO  - Journal of Diabetes Science and Technology
DO  - 10.1177/19322968261501915
UR  - https://doi.org/10.1177/19322968261501915
AB  - Background:: Diabetic ketoacidosis (DKA) management by variable-rate insulin infusion is increasing with the widespread adoption of electronic glucose management systems (eGMS). Herein, we evaluate the implementation of an updated process for DKA management incorporating the eGMS algorithm.  Methods:: An eGMS was implemented within the Cedars-Sinai Health System at our tertiary academic center (AC) and community hospital (CH) in January 2024. We compared outcomes after eGMS implementation (post-GMS) to the previous standard of care (pre-GMS). Adult patients admitted to the intensive care unit with a primary diagnosis of DKA and treated with an insulin infusion between January 2023 and December 2024 were included. We separately evaluated outcomes of hypoglycemia (blood glucose, BG <70 mg/dL) and severe hypoglycemia (BG <40 mg/dL) and treatment efficacy.  Results:: In total, 324 patients were included in the pre-GMS and 211 patients in the post-GMS cohorts. There was a decrease in the prevalence of hypoglycemia (pre-GMS: 11.0%, post-GMS: 4.3%, P = .005) and severe hypoglycemia (pre-GMS: 1.90%, post-GMS: 0.00%, P = .047). At AC, the hospital length of stay (LOS) decreased from 8.6 to 6.0 days (P = .012). At CH, insulin drip duration increased significantly (pre-GMS: 26.4 h, post-GMS: 36.7 h, P = .004), but there was no impact on LOS.  Conclusion:: Updating the DKA process and implementing an eGMS improved hypoglycemia rates throughout the institution. Hospital LOS was reduced at the AC, reflecting standardization and streamlining of the DKA processes.
SN  - 1932-2968
ER  -