Contrast-induced acute kidney injury after primary percutaneous coronary intervention: results from the HORIZONS-AMI substudy
Bibliographic record
Abstract
AIM: We sought to examine the short- and long-term outcomes of patients who developed contrast-induced acute kidney injury (CI-AKI; defined as an increase in serum creatinine of ≥0.5 mg/dL or a 25% relative rise within 48 h after contrast exposure) from the large-scale HORIZONS-AMI trial. METHODS AND RESULTS: Multivariable analyses were used to identify predictors of CI-AKI, as well predictors of the primary and secondary endpoints. The incidence of CI-AKI in this cohort of ST-segment elevation myocardial infarction (STEMI) patients was 16.1% (479/2968). Predictors of CI-AKI were contrast volume, white blood cell count, left anterior descending infarct-related artery, age, anaemia, creatinine clearance <60 mL/min, and history of congestive heart failure. Patients with CI-AKI had higher rates of net adverse clinical events [NACE; a combination of major bleeding or composite major adverse cardiac events (MACE; consisting of death, reinfarction, target vessel revascularization for ischaemia, or stroke)] at 30 days (22.0 vs. 9.3%; P < 0.0001) and 3 years (40.3 vs. 24.6%; P < 0.0001). They also had higher rates of mortality at 30 days (8.0 vs. 0.9%; P < 0.0001) and 3 years (16.2 vs. 4.5%; P < 0.0001). Multivariable analysis confirmed CI-AKI as an independent predictor of NACE [hazard ratio ([HR), 1.53; 95% confidence interval (CI), 1.23-1.90; P = 0.0001], MACE (HR, 1.56; 95% CI, 1.23-1.98; P = 0.0002), non-coronary artery bypass grafting major bleeding (HR, 2.07; 95% CI, 1.57-2.73; P < 0.0001), and mortality (HR, 1.80; 95% CI, 1.19-2.73; P = 0.005) at 3-year follow-up. CONCLUSION: Contrast-induced acute kidney injury is associated with poor short- and long-term outcomes after primary percutaneous coronary intervention in STEMI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".