Kidney outcomes using a sustained ≥40% decline in <scp>eGFR</scp>: A meta‐analysis of <scp>SGLT2</scp> inhibitor trials
Bibliographic record
Abstract
Abstract Background A recent meta‐analysis of sodium–glucose cotransporter 2 (SGLT2) inhibitor outcome trials reported that SGLT2 inhibitors were associated with reduction in the risk of adverse composite kidney outcomes, with moderate heterogeneity across the trials; however, the endpoints were defined differently across the trials. Hypothesis The apparent heterogeneity of the meta‐analysis of kidney composite outcomes of SGLT2 inhibitor trials will be substantially reduced by using a consistent assessment of sustained ≥40% decline in eGFR/chronic kidney dialysis/transplantation/renal death across trials. Methods We performed a meta‐analysis of kidney composite outcomes from the four SGLT2 cardiovascular outcome trial programs conducted in general type 2 diabetes mellitus populations, which included, as a surrogate of progression to kidney failure, a sustained ≥40% decline in eGFR along with kidney replacement therapy and kidney death. The trials assessed were VERTIS CV (NCT01986881), CANVAS Program (NCT01032629 and NCT01989754), DECLARE‐TIMI 58 (NCT01730534), and EMPA‐REG OUTCOME (NCT01131676). Results Data from the trials comprised 42 516 individual participants; overall, 998 composite kidney events occurred. SGLT2 inhibition was associated with a significant reduction in the kidney composite endpoint (HR 0.58 [95% CI 0.51–0.65]) and with a highly consistent effect across the trials (Q statistic p = .64; I 2 = 0.0%). Conclusions Our meta‐analysis highlights the value of using similarly defined endpoints across trials and supports the finding of consistent protection against kidney disease progression with SGLT2 inhibitors as a class in patients with type 2 diabetes mellitus who either have established atherosclerotic cardiovascular disease or are at high cardiovascular risk with multiple cardiovascular risk factors.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".