4 Renal function-based contrast dosing to define ‘prognostic’ contrast limits in patients undergoing coronary angioplasty
Bibliographic record
Abstract
Background Renal function-based contrast dosing minimises renal injury following percutaneous coronary intervention (PCI). The ratio (R) of contrast volume:glomerular filtration rate (GFR) has been studied but its prognostic relevance is unknown. Aim To establish the relationship between R and mortality; and define a ‘prognostic’ threshold (RT) for contrast in PCI for stable disease, non ST-elevation ACS (NSTEACS) and ST-elevation ACS (STEACS). Method We evaluated 44 082 non-dialysis patients between 2008–2014. GFR was calculated using CG, CKD-EPI and MDRD equations. R was determined for each patient and its relationship with mortality was modelled mathematically and analysed using Cox regression and adjusted ROC curve analyses. Results Multivariable analyses identified R as an independent predictor of 3 year mortality (HR=1.03, 95% CI: 1.02 to 1.04, p<0.001). There was an exponential relationship between R and mortality; for every unit increase in R, 3 year mortality increased by 13%–14% regardless of PCI indication. Adjusted analyses indicated RT was consistently higher in stable disease (RT=7.7–8.3) compared to NSTEACS (RT=5.3–5.7) and STEACS (RT=5.3–5.7). Conclusion This study advocates a RT=7.7–8.3 for stable disease and RT=5.3–5.7 for NSTEACS/STEACS. This is greater than previously reported but implies greater contrast volumes may ultimately be tolerated in the contemporary PCI era.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".