J Korean Med Sci.  2022 Feb;37(7):e53. 10.3346/jkms.2022.37.e53.

Sodium-Glucose Cotransporter-2 Inhibitor-Related Diabetic Ketoacidosis: Accuracy Verification of Operational Definition

• Kang DY
• Kim H
• Ko S
• Kim H
• Shinn J
• Kang MG
• Byeon Sj
• Choi JH
• Shin SY
• Kim HS

• KMID: 2526516
• DOI: http://doi.org/10.3346/jkms.2022.37.e53

Abstract

Background
The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodiumglucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed.
Methods
All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital; subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP.
Results
A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3–10.0%, P > 0.999).
Conclusion
Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.

Keyword

Cohort Studies; Diabetes Complications; Diabetic Ketoacidosis; Diabetes Mellitus; Type 2; Sodium-Glucose Transporter 2 Inhibitors

Figure

• 

  Fig. 1 SGLT2i-related DKA detection algorithm. The solid lines represent a “Yes” in the algorithm. The broken lines represent a “No” in the algorithm.ABGA = arterial blood gas analysis, ICD-10 = International Statistical Classification of Diseases and Related Health Problems, SGLT2i = sodium–glucose cotransporter-2 inhibitor, DKA = diabetic ketoacidosis.

• 

  Fig. 2 Study design. Comparison of CDM and manual chart review of SGLT2i-related DKA.CDW = clinical data warehouse, EMR = electronic medical records, SGLT2i = sodium-glucose cotransporter-2 inhibitor.

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