MO201: Effects of Aspirin in Primary Prevention of Cardiovascular (CV) Disease in People with Chronic Kidney Disease (CKD): Results of the TIPS3 Trial
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Subjects with CKD carry a high (CV) risk. Whether this risk is blunted by aspirin is controversial. We examined CV outcomes of CKD participants of the TIPS3 trial. METHOD The International Polycap Study3 (TIPS3) randomized people (N = 5713) with and without CKD, but without previous CV disease, to aspirin, aspirin plus polypill, polypill or respective placebo in a factorial design. In 983 participants that were randomized to aspirin or placebo, eGFR was below 60 mL/min/1.73 m2 at baseline (CKD). The primary outcome for this comparison was non-fatal myocardial infarction (MI), non-fatal stroke or CV death. The mean follow-up was 4.6 years. RESULTS In all participants, there were 250 primary MACE outcomes, 116 on aspirin and 134 on placebo (HR 0.86; 95% CI 0.67–1.10). In those with CKD, there were 65 primary MACE outcomes, 26 in the 502 participants on aspirin, 39 in the 481 participants on placebo, HR 0.57 (95% CI 0.34–0.94). Directionally similar results were found for aspirin versus placebo for the secondary outcome all-cause death with 312 events in all participants (HR 0.87; 0.70–1.07) and 82 events in CKD patients (HR 0.64; 0.41–0.99). There was no significant interaction of eGFR <60 mL/min with the treatment effects of aspirin versus placebo. Major and minor bleedings were rare and not different between groups. When aspirin was combined with a polypill (containing atenolol, ramipril, hydrochlorothiazide and simvastatin) and compared to double placebo, in all participants the HR for the MACE outcome was 0.69, 0.50–0.97 and for all-cause death 0.80, 0.59–1.08; in CKD patients the HR was 0.37, 0.18–0.75 for MACE and HR 0.49, 0.29–0.97 for death. CONCLUSION Results suggest that the CV risk in people with CKD may be substantially alleviated with aspirin alone and in combination with a polypill.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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) | 0.004 | 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".