Abstract MP28: Filtration Markers, Cardiovascular Disease and All-cause Mortality in Kidney Transplant Recipients: The FAVORIT Trial
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is common in kidney transplant recipients (KTRs). The level of kidney function (glomerular filtration rate [GFR]) estimated from serum creatinine has been shown to be independently associated with CVD outcomes in a wide range of populations, including KTRs. In other populations, CVD outcomes are more strongly associated with the alternative filtration markers serum cystatin C and beta-2-microglobulin (B2M) than creatinine, but this has not been evaluated in KTRs. Methods: We performed a case-cohort study of participants of the Folic Acid for Vascular Outcome Reduction in Transplantation (FAVORIT) Trial, a randomized, double blind clinical trial of homocysteine lowering with B vitamins in KTRs. Study participants were enrolled from 2002-07 from centers in the US, Canada & Brazil; we studied a 15% random subcohort of eligible baseline participants (N=508; mean age 52 years, 38% women) and all additional eligible adjudicated CVD (CVD death, myocardial infarction, resuscitated sudden death, stroke, N=211) and all-cause mortality (N=314) events that occurred through June 2009. Filtration markers measured at baseline were expressed as estimated GFR using established CKD-EPI equations (eGFRcr, eGFRcys & eGFRB2M). Associations of eGFRcr, eGFRcys & eGFRB2M with CVD and mortality were evaluated with weighted Cox proportional hazard regression to account for the case-cohort design. Results: In multivariable adjusted models eGFRcr was not significantly associated with CVD or all-cause mortality. In contrast, both lower eGFRcys and eGFRB2M were associated with increased risk of CVD and all-cause mortality (Figure). Findings were similar when eGFR was treated as a continuous variable. Conclusion: Similar to non-transplant cohorts, cystatin C and B2M have stronger risk associations for CVD and all-cause mortality compared to creatinine in KTRs. These findings likely reflect differences in their non-GFR determinants; future work is needed to evaluate non-GFR determinants in KTRs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".