Acute kidney injury with sodium‐glucose co‐transporter‐2 inhibitors: A meta‐analysis of cardiovascular outcome trials
Bibliographic record
Abstract
Three, multicentre, large-scale, randomized, placebo-controlled trials of cardiovascular outcomes with sodium-glucose co-transporter-2 (SGLT2) inhibitors have each shown substantial reductions in rates of hospitalization for heart failure and progression of chronic kidney disease in people with type 2 diabetes. However, safety concerns remain for this ostensibly paradigm-shifting drug class. In particular, the US Food and Drug Administration has highlighted the risk of acute kidney injury (AKI), a condition associated with high morbidity and mortality. To investigate this further, we conducted a meta-analysis of the three trials to compare the frequency of AKI adverse event reports between participants treated with placebo and those who had received an SGLT2 inhibitor. Rather than an increase, we noted a consistent and robust reduction in the likelihood of AKI among those participants who had been randomized to receive an SGLT2 inhibitor (hazard ratio 0.66, 95% confidence interval 0.54-0.80). We further noted that the reports of AKI were similar in frequency to those of kidney disease progression. The caveats of the non-adjudicated reporting of AKI in the trials notwithstanding, these data suggest that SGLT2 inhibitors may protect vulnerable patients with type 2 diabetes from AKI and that prospective studies to evaluate this additional aspect of kidney protection are warranted.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".