Association of kidney and cardiovascular outcomes in patients with type 2 diabetes mellitus: insights from the EMPA-REG OUTCOME trial
Bibliographic record
Abstract
Background In EMPA-REG OUTCOME, empagliflozin (10 or 25mg once daily) reduced the risk of hospitalisation for heart failure (HHF) and kidney events in patients with type 2 diabetes and established cardiovascular (CV) disease. We evaluated the bi-directional relationship between kidney and HF outcomes. Methods Bi-directional associations of kidney events and subsequent CV events were explored using Cox regression with time-varying covariates. Results Of 2,061 placebo patients, 18.8% experienced a kidney event (progression to macroalbuminuria with UACR > 300mg/g, doubling of serum creatinine with eGFR ≤ 45 ml/min/1.73 m2, initiation of renal-replacement therapy or renal death). Factors significantly associated with risk of experiencing a kidney event included: low baseline eGFR, albuminuria ≥ 30mg/g, high uric acid and LDL-C, prior HF but no coronary artery disease. In placebo patients, occurrence of a non-fatal kidney event increased subsequent HHF risk (hazard ratio [95% confidence intervals]) (2.40 [1.42,4.05]) but not 3P-MACE (1.30 [0.89,1.91]). Vice-versa, HHF (2.03 [1.22,3.39]) but not myocardial infarction (MI)/stroke (0.94 [0.56,1.56]) increased subsequent kidney event risk. Conclusions These findings demonstrate strong bi-directional inter-relationship between HHF and kidney events. Strategies to optimise the use of therapies such as empagliflozin, reducing both kidney and HF outcomes, are warranted, as their benefits may be compounded. Publication History Article published online: 26 May 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".