Use of diuretics and outcomes in patients with type 2 diabetes: findings from the <scp>EMPA‐REG OUTCOME</scp> trial
Bibliographic record
Abstract
AIMS: Loop diuretics (LD) relieve symptoms and signs of congestion due to heart failure (HF), but many patients prescribed LD do not have such a diagnosis. We studied the relationship between HF diagnosis, use of LD, and outcomes in patients with type 2 diabetes mellitus (T2DM) enrolled in the EMPA-REG OUTCOME trial. METHODS AND RESULTS: The relationship between HF diagnosis, use of LD, and outcomes was evaluated in four patient subgroups with T2DM: (i) investigator-reported HF on LD, (ii) investigator-reported HF not on LD, (iii) no HF on LD, and (iv) no HF and not on LD, and we assessed their risk of cardiovascular events. Of 7020 participants, 706 (10%) had a diagnosis of HF at baseline, of whom 334 were prescribed LD. However, 755 (11%) patients who did not have a diagnosis of HF were prescribed LD. Compared to those with neither HF nor prescribed LD (reference group; placebo), those with both HF and receiving LD had the highest rates for all-cause [hazard ratio (HR) (95% confidence interval) 3.19 (2.03-5.01)] and cardiovascular mortality [3.83 [(2.28-6.44)], and HF hospitalizations [9.51 (5.61-16.14)]. Patients without HF but prescribed LD had higher rates for all three outcomes [1.62 (1.10-2.39); 1.97 (1.26-3.08); 3.20 (1.90-5.39)], which were similar to patients with HF who were not receiving LD [1.42 (0.78-2.57); 1.56 (0.78-3.11); 3.00 (1.40-6.40)]. Empagliflozin had similar benefits regardless of subgroup (P for interaction >0.1 for all outcomes). CONCLUSION: Patients with T2DM prescribed LD are at greater risk of cardiovascular events even if they are not reported to have HF; this might reflect under-diagnosis. Empagliflozin was similarly effective in all subgroups investigated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".