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Identifying diabetic ketoacidosis encounters at diagnosis among children with type 1 diabetes in claims data

R Brett McQueen1, Nai-Chia Chen1, Eric J Gutierrez1

  • 1University of Colorado Anschutz Medical Campus, Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO 80045, USA.

Journal of the Endocrine Society
|July 15, 2026
PubMed

Insights

Claims data identify many, but not all, diabetic ketoacidosis (DKA) cases in new type 1 diabetes patients. DKA significantly increases inpatient encounters but reduces emergency room visits.

Area of Science:

  • Pediatric Endocrinology
  • Health Informatics
  • Diabetes Research

Background:

  • Diabetic ketoacidosis (DKA) is a serious complication of type 1 diabetes.
  • Accurate identification of DKA cases is crucial for understanding disease burden and resource utilization.
  • Claims data are increasingly used for health research but their performance in identifying specific conditions needs evaluation.

Purpose of the Study:

  • To quantify the accuracy of claims data in identifying DKA cases among newly diagnosed type 1 diabetes patients.
  • To estimate healthcare resource utilization in type 1 diabetes patients with and without DKA.

Main Methods:

  • Matched individuals from a diabetes registry to an all-payer claims database.
  • Evaluated DKA identification performance using type 1 diabetes and DKA codes in emergency department (ER) and inpatient (IP) settings.
  • Estimated associations between laboratory-confirmed DKA and healthcare resource utilization (ER and IP encounters).

Main Results:

  • A significant proportion (56%) of newly diagnosed type 1 diabetes patients experienced DKA.
  • Strict claims coding definitions for DKA had lower sensitivity (63%) but higher specificity (76%) compared to looser definitions (sensitivity 89%, specificity 60%).
  • Laboratory-confirmed DKA was associated with over 5 times the rate of inpatient encounters and fewer ER-only encounters.

Conclusions:

  • Claims data may not capture all DKA encounters at the time of type 1 diabetes diagnosis.
  • Further research is needed to refine coding and setting analyses for comprehensive DKA burden estimation.
Abstract

Keywords:
burdendiabetic ketoacidosishealthcare resource utilizationreal-world claims datatype 1 diabetes

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