Association of early initiation of dialysis with all‐cause and cardiovascular mortality: A propensity score weighted analysis of the United States Renal Data System
Bibliographic record
Abstract
BACKGROUND: Early initiation of maintenance hemodialysis has been associated with excess mortality in some studies, but the effects on cardiovascular (CV) mortality has not been studied. Moreover, whether the increased mortality is due to co-morbidities or early initiation of dialysis is unclear. We used a propensity score weighted analysis of the United States Renal Data System (USRDS) to examine how the estimated glomerular filtration rate (eGFR) at initiation of dialysis affects total and CV mortality. METHODS: Association between tertiles of eGFR at initiation of hemodialysis and all-cause and CV mortality were assessed in 676,196 adult patients who initiated hemodialysis between 2006 and 2014, using inverse probability of treatment weighting (IPTW) weighted multivariable regression models. RESULTS: The intermediate (eGFR 8.7 to <13.0 mL/min) and early start groups (eGFR ≥13.0 mL/min) had a 42% and 93% increased all-cause mortality, respectively compared to late (eGFR < 8.7), start group (unadjusted hazard ratio (HR) = 1.42; 95% CI, 1.41-1.43 and HR = 1.93; 95%CI, 1.91-1.94, respectively). This association was attenuated but remained significant in propensity weighted multivariable analysis (adjusted HR = 1.13; 95%CI, 1.12-1.14 for intermediate and HR = 1.37; 95%CI, 1.36-1.39, for early start, respectively). The CV mortality was similarly increased (adjusted HR = 1.08; 95%CI, 1.07-1.10 and HR = 1.23; 95%CI, 1.21-1.24, for intermediate and early start, respectively). In patients with cystic kidney disease, all-cause mortality was increased with early start, but there were no differences in CV mortality between groups. CONCLUSIONS: Early initiation of dialysis is associated with increased all-cause and CV mortality. Our observations support delaying hemodialysis according to the eGFR values.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".