Kidney Transplantation and Cardiovascular Events Among Patients With <scp>End‐Stage</scp> Renal Disease Due to Lupus Nephritis: A Nationwide Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the potential impact of kidney transplantation on cardiovascular (CV) events among patients with end-stage renal disease (ESRD) due to lupus nephritis (LN). METHODS: In a nationwide cohort study, we identified all patients with LN-ESRD enrolled in the US Renal Data System who were waitlisted for a kidney transplant and enrolled in Medicare between January, 2000 and December, 2016. The primary outcome was incident CV events, including myocardial infarction (MI) and ischemic cerebrovascular accident (CVA). We used time-dependent Cox regression to estimate the hazard ratios (HRs) of these outcomes associated with kidney transplant as a time-varying exposure, adjusting for sex, age, race, ethnicity, geographic region, year of ESRD onset, first ESRD treatment modality (e.g., hemodialysis or peritoneal dialysis), Charlson Comorbidity Index score, and history of prior organ transplants. RESULTS: Of 5,963 waitlisted patients with LN-ESRD, 3,209 (54%) had a kidney transplant during the study period. The majority were female (82%), and African American patients represented 48% of waitlisted patients and 43% of transplanted patients. Kidney transplantation was associated with a lower risk of incident CV events (adjusted HR 0.31 [95% confidence interval (95% CI) 0.18-0.53]) as well as lower risks of MI and CVA (adjusted HRs 0.13 [95% CI 0.08-0.34] and 0.30 [95% CI 0.16-0.54], respectively). CONCLUSION: Kidney transplantation was associated with a reduced risk of CV events, including MI and CVA, in patients with LN-ESRD. Our findings highlight the importance of identifying barriers to transplantation in this population, as improved access could reduce CV morbidity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".