Use of sodium–glucose cotransporter-2 inhibitors and risk of acute kidney injury in older adults with diabetes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Regulatory agencies warn about the risk of acute kidney injury (AKI) after the initiation of sodium-glucose cotransporter-2 (SGLT2) inhibitors. Our objective was to quantify the 90-day risk of AKI in older adults after initiation of SGLT2 inhibitors in routine clinical practice. METHODS: We conducted a population-based retrospective cohort study in Ontario, Canada, involving adults with diabetes who were aged 66 years or older and who were newly dispensed either an SGLT2 inhibitor or a dipeptidyl peptidase-4 (DPP4) inhibitor in an outpatient setting between 2015 and 2017. We used inverse probability of treatment weighting based on a propensity score to balance the 2 groups on measured baseline characteristics. The primary outcome was 90-day risk of a hospital encounter (i.e., visit to the emergency department or admission to hospital) with AKI, which we defined by a 50% or greater increase in the concentration of serum creatinine from the baseline value or an absolute increase of at least 27 μmol/L after an SGLT2 or DDP4 inhibitor was dispensed. We obtained weighted risk ratios using modified Poisson regression and weighted risk differences using binomial regression. RESULTS: We included 39 094 patients with a median age of 70 (interquartile range 68-74) years in the study. Relative to new use of a DPP4 inhibitor, initiation of a SGLT2 inhibitor was associated with a lower 90-day risk of a hospital encounter with AKI: 216 events in 19 611 patients (1.10%) versus 388 events in 19 483 patients (1.99%); weighted risk ratio 0.79 (95% confidence interval 0.64-0.98). INTERPRETATION: In routine care of older adults, new use of SGLT2 inhibitors compared with use of DPP4 inhibitors was associated with a lower risk of AKI. Together with previous evidence, our findings suggest that regulatory warnings about AKI risk with SGLT2 inhibitors are unwarranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".