Associations Between Acute Kidney Injury and Cardiovascular and Renal Outcomes After Coronary Angiography
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is associated with early mortality after percutaneous coronary revascularization procedures, but its prognostic relevance to long-term clinical outcomes remains controversial. METHODS AND RESULTS: We conducted a retrospective study of 14782 adults who received coronary angiography in the province of Alberta, Canada, between 2004 and 2006. AKI was identified on the basis of changes in serum creatinine concentration within 7 days of the procedure according to AKI Network criteria. The associations between AKI and long-term outcomes, including mortality, end-stage renal disease, and cardiovascular and renal hospitalizations, were studied with the use of Cox regression of multiple failure times. The adjusted risk of death increased with increasing severity of AKI; compared with no AKI, the adjusted hazard ratio for death was 2.00 (95% confidence interval, 1.69 to 2.36) with stage 1 AKI and 3.72 (95% confidence interval, 2.92 to 4.76) with stage 2 or 3 AKI. The adjusted risk of end-stage renal disease requiring renal replacement therapy also increased according to the severity of AKI (hazard ratio, 4.15 [95% confidence interval, 2.32 to 7.42] and 11.74 [95% confidence interval, 6.38 to 21.59], respectively), as did the risks of subsequent hospitalizations for heart failure and acute renal failure. CONCLUSIONS: These findings inform the controversy surrounding AKI after angiography, demonstrating that it is a significant risk factor for long-term mortality, end-stage renal disease, and hospitalization for cardiovascular and renal events after coronary angiography.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".