Abstract 13433: Predictors of Contrast Induced Nephropathy in Patients With Acute Coronary Syndromes and Normal Baseline Glomerular Filtration Rate
Bibliographic record
Abstract
Background: The incidence and predictors of contrast-induced nephropathy (CIN) in patients with normal glomerular filtration rate (GFR) are not well ascertained. We aim to determine the incidence and predictors for CIN after coronary catheterization (CATH) for acute coronary syndromes (ACS). Methods: We combined the datasets of two studies. The AMI-QUEBEC was an observational cohort of patients with ST-segment elevation myocardial infarctions in 2003. The AMI-OPTIMA was a study of patients hospitalized with ACS in 2009 and 2012. For this analysis, we retained only patients with GFR > 60 ml/min who underwent CATH. We defined “hyperfiltrators” as patients with GFR above the 95th percentile age and sex-adjusted value. CIN was defined as an increase in serum creatinine >0.5 mg/dL (44.2 μmols/L) or > 50% from baseline serum creatinine. Results: There were 3,188 patients with GFR > 60 ml/min : 39 hyperfiltrators and 3,149 without hyperfiltration. The mean age was similar between the two groups of patients (62 years); 21% and 27% females in hyperfiltrators and non-hyperfiltrators (p<0.0001). The prevalences of diabetes mellitus and hypertension were 36% and 64%, respectively in hyperfiltrators compared to 20% and 46%, respectively in non-hyperfiltrators. The mean baseline GFR and creatinine were 112 ml/min and 50 μmols/L, respectively in hyperfiltrators; 84.2 ml/min and 80 μmols/L in non-hyperfiltrators. There were 225 CIN following CATH; 7.1% of the whole cohort with 35.9% in the hyperfiltrators and 6.7% in non-hyperfiltrators. Hyperfiltration was independently associated with a 13-fold increase in the risk of CIN (Table 1). Each year of increase in age was associated with a 5% increase in the risk of CIN. Shock was also associated with an 11-fold increase in the risk of CIN. Conclusion: Hyperfiltrators may be at high risk of CIN following CATH in ACS. The risk of CIN associated with hyperfiltration should be evaluated in other populations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".