Age, glomerular filtration rate, ejection fraction, and the AGEF score predict contrast‐induced nephropathy in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: In patients undergoing primary percutaneous coronary interventions (PCI) for ST-segment elevation myocardial infarction (STEMI), the occurrence of Contrast-Induced Nephropathy (CIN) has a pronounced impact both on morbidity and mortality. We investigated the variables associated with CIN development in 481 consecutive patients with STEMI undergoing primary PCI and evaluated the predictive value of a 3-variable clinical risk score (the AGEF score) based on age, left ventricular ejection fraction (EF), and estimated glomerular filtration rate (eGFR). METHODS: CIN was defined as an absolute increase in serum creatinine ≥0.5 mg/dL or an increase ≥25% from baseline within 72 hr. AGEF score was calculated by adding 1 point to the Age/EF(%) ratio if the eGFR was <60 mL/min per 1.73 m(2) . RESULTS: Overall, the incidence of CIN was 5.2%. In-hospital mortality was higher in patients with CIN than in those without (16% Vs 1.3%, P = 0.001). At multivariate analysis age (OR 1.06, P = 0.042), eGFR (OR 0.95, P = 0.001), EF (OR 0.94, P = 0.007) and post-procedural TIMI flow grade (OR 0.43, P = 0.045) were independent predictors of CIN. AGEF score was an accurate (OR 5.19, P < 0.001, AUC 0.88) and calibrated (Hosmer-Lemeshow χ(2) = 10.25, P = 0.25) predictor of CIN. CONCLUSIONS: Advanced age, depressed EF, and reduced eGFR are independent predictors of CIN development after primary PCI for STEMI. The preprocedural individual patient risk can be clinically assessed with the calculation of the AGEF score, which is based on such readily available parameters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".