914-P: Association of Sodium–Glucose Cotransporter 2 Inhibitors with Risk of Diabetic Ketoacidosis among Hospitalized Patients—A Multicentre Cohort Study
Bibliographic record
Abstract
Importance: Sodium glucose co-transporter-2 (SGLT-2) inhibitors are being used among hospitalized patients, but there is no real-world empiric data on the risk of diabetic ketoacidosis (DKA) in-hospital. Objective: To assess the risk of DKA with SGLT-2 inhibitor use during hospital stay. Design, Setting, Participants: We conducted a multi-centre cohort study of hospitalized patients in 19 hospitals in Canada between January 1, 2015 and December 31, 2021. We included patients over age 18 years with type 2 diabetes mellitus who received an SGLT-2 inhibitor or a dipeptidyl peptidase-4 (DPP-4) inhibitor in hospital. Exposure: SGLT-2 inhibitor Comparator: DPP4-inhibitor Main Outcomes and Measures: The primary outcome was the risk of DKA defined using American Diabetes Association (ADA) DKA criteria (pH <7.30, bicarbonate less than 18 mmol/l and positive ketones). Patients were followed from the date of admission to hospital to date of discharge or occurrence of DKA. Results: We identified 11,098 patients who received an SGLT-2 inhibitor and 67,429 who received a DPP-4 inhibitor. The average age was 67.7 years (SD: 18.3 years), and 47.7% were women. In the matched population, the risk of DKA was 0.06% (N=6 events) over 5.2 hospital days with DPP-4 inhibitors and 0.19% (N=20 events) over 5.4 hospital days with SGLT-2 inhibitors. The relative risk was 3.33 (95% CI 1.33 - 8.30) for SGLT-2 inhibitors compared to DPP-4 inhibitors. Conclusion and Relevance: Among hospitalized patients, the risk of DKA with use of SGLT-2 inhibitors was three-fold higher compared to DPP-4 inhibitors, but the absolute risk of DKA was low. Disclosure S. Sarma: None. B. Hodzic-Santor: None. M. Colacci: None. A.A. Verma: None. F. Razak: None. A. Raissi: None. M.C.H. Lassen: None. M. Fralick: Consultant; singal1, proofdx.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".