Induction immunosuppression agents as risk factors for incident cardiovascular events and mortality after kidney transplantation
Bibliographic record
Abstract
Low T cell counts and acute rejection are associated with increased cardiovascular events (CVEs); T cell-depleting agents decrease both. Thus, we aimed to characterize the risk of CVEs by using an induction agent used in kidney transplant recipients. We conducted a secondary data analysis of patients who received a kidney transplant and used Medicare as their primary insurance from 1999 to 2010. Outcomes of interest were incident CVE, all-cause mortality, CVE-related mortality, and a composite outcome of mortality and CVE. Of 47 258 recipients, 29.3% received IL-2 receptor antagonist (IL-2RA), 33.3% received anti-thymocyte globulin (ATG), 7.3% received alemtuzumab, and 30.0% received no induction. Compared with IL-2RA, there was no difference in the risk of CVE in the ATG (adjusted hazard ratio [aHR] 0.98, 95% confidence interval [CI] 0.92-1.05) and alemtuzumab group (aHR 1.01, 95% CI 0.89-1.16), but slightly higher in the no induction group (aHR 1.06, 95% CI 1.00-1.14). Acute rejection did not modify this association in the latter group but did increase CVE by 46% in the alemtuzumab group. There was no difference in the hazard of all-cause or CVE-related mortality. Only in the ATG group, a 7% lower hazard of the composite outcome of mortality and CVE was noted. Induction agents are not associated with incident CVE, although prospective trials are needed to determine a personalized approach to prevention.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".