Prasugrel and Ticagrelor in Patients with Drug-Eluting Stents and Kidney Failure
Bibliographic record
Abstract
Background and objectives Prasugrel and ticagrelor have superior efficacy compared with clopidogrel in moderate CKD but have not been studied in kidney failure. The study objective is to determine the effectiveness and safety of prasugrel and ticagrelor in kidney failure. Design, setting, participants, & measurements This retrospective cohort study used United States Renal Data System data from 2012 to 2015. We identified all patients on dialysis who received a drug-eluting stent and were alive at 90 days after stent implantation. Inverse probability–weighted Cox proportional hazard models were used. Weights were estimated with propensity scores for multiple treatments. Results This cohort included 6648 patients on clopidogrel, 621 on prasugrel, and 449 on ticagrelor. A total of 3279 primary composite (cardiovascular death, myocardial infarction, or stroke) and 2120 clinically relevant bleeding events were observed. The incidence of the primary composite outcome of cardiovascular death, myocardial infarction, or stroke at 12 months was similar across the three treatment groups. The absolute event rate in the unweighted cohort was 144 events per 100 patient-years for clopidogrel, 126 for prasugrel, and 161 for ticagrelor. For prasugrel versus clopidogrel, the weighted hazard ratio was 0.96 (95% confidence interval, 0.82 to 1.11; P =0.58). For ticagrelor versus clopidogrel, the hazard ratio was 1.00 (95% confidence interval, 0.83 to 1.20; P =0.98). A numerically higher incidence of clinically relevant bleeding was seen with prasugrel or ticagrelor compared with clopidogrel (weighted hazard ratio, 1.15; 95% confidence interval, 0.95 to 1.38 and weighted hazard ratio, 1.13; 95% confidence interval, 0.91 to 1.40, respectively). Conclusions Prasugrel or ticagrelor does not seem to be associated with significant benefits compared with clopidogrel in patients with kidney failure treated with drug-eluting stents. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_04_02_CJN12120720.mp3
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".