A Comparative Analysis of Diabetic Ketoacidosis (DKA) in Sodium-Glucose Cotransporter-2 Inhibitor (SGLT2i) Users Versus Non-users: A Systematic Review
Bibliographic record
Abstract
Diabetic ketoacidosis (DKA) remains a life-threatening complication of diabetes mellitus, with emerging concerns about its association with sodium-glucose cotransporter-2 inhibitors (SGLT2i). While SGLT2i offer significant cardiovascular and renal benefits, their potential to increase DKA risk - particularly euglycemic diabetic ketoacidosis (eDKA) - warrants systematic evaluation. This review aims to compare the incidence, clinical features, and outcomes of DKA in SGLT2i users versus non-users. A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, CINAHL, IEEE Xplore, and Web of Science. Eligible studies included comparative analyses of DKA in SGLT2i users and non-users, with retrospective and prospective designs. Data were extracted on incidence, laboratory features, precipitating factors, and clinical outcomes. Risk of bias was assessed using the Newcastle-Ottawa Scale. Thirteen studies were included, encompassing diverse populations and regions. SGLT2i users exhibited a higher incidence of DKA, with a notable proportion presenting as eDKA. Key distinguishing features included lower blood glucose levels, higher sodium concentrations, and prolonged hospital stays. Surgery and infections were common triggers, while mortality rates remained comparable between groups. Methodological quality was generally high, though heterogeneity precluded meta-analysis. SGLT2i use is associated with an increased risk of DKA, particularly euglycemic variants, necessitating heightened clinical vigilance. Temporary discontinuation during high-risk periods and early diagnostic suspicion are recommended. Standardized criteria for eDKA and further prospective studies are needed to optimize patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".