Relation between Renal Dysfunction and Cardiovascular Outcomes after Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The presence of coexisting conditions has a substantial effect on the outcome of acute myocardial infarction. Renal failure is associated with one of the highest risks, but the influence of milder degrees of renal impairment is less well defined. METHODS: As part of the Valsartan in Acute Myocardial Infarction Trial (VALIANT), we identified 14,527 patients with acute myocardial infarction complicated by clinical or radiologic signs of heart failure, left ventricular dysfunction, or both, and a documented serum creatinine measurement. Patients were randomly assigned to receive captopril, valsartan, or both. The glomerular filtration rate (GFR) was estimated by means of the four-component Modification of Diet in Renal Disease equation, and the patients were grouped according to their estimated GFR. We used a 70-candidate variable model to adjust and compare overall mortality and composite cardiovascular events among four GFR groups. RESULTS: The distribution of estimated GFR was wide and normally shaped, with a mean (+/-SD) value of 70+/-21 ml per minute per 1.73 m2 of body-surface area. The prevalence of coexisting risk factors, prior cardiovascular disease, and a Killip class of more than I was greatest among patients with a reduced estimated GFR (less than 45.0 ml per minute per 1.73 m2), and the use of aspirin, beta-blockers, statins, or coronary-revascularization procedures was lowest in this group. The risk of death or the composite end point of death from cardiovascular causes, reinfarction, congestive heart failure, stroke, or resuscitation after cardiac arrest increased with declining estimated GFRs. Although the rate of renal events increased with declining estimated GFRs, the adverse outcomes were predominantly cardiovascular. Below 81.0 ml per minute per 1.73 m2, each reduction of the estimated GFR by 10 units was associated with a hazard ratio for death and nonfatal cardiovascular outcomes of 1.10 (95 percent confidence interval, 1.08 to 1.12), which was independent of the treatment assignment. CONCLUSIONS: Even mild renal disease, as assessed by the estimated GFR, should be considered a major risk factor for cardiovascular complications after a myocardial infarction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".