Association between allopurinol and mortality among Japanese hemodialysis patients: results from the DOPPS
Bibliographic record
Abstract
PURPOSE: Allopurinol, for treating hyperuricemia, is associated with lower mortality among hyperuricemic patients without chronic kidney disease (CKD). Greater allopurinol utilization in hemodialysis (HD) in Japan versus other countries provides an opportunity for understanding allopurinol-related HD outcomes. METHODS: Data from 6,252 Japanese HD patients from phases 1-3 of the Dialysis Outcomes and Practice Patterns Study (1999-2008) at ~60 facilities per phase were analyzed. Mortality was compared for patients prescribed (25 %) versus not-prescribed allopurinol using Cox regression, overall, and in patient subgroups. RESULTS: Patients prescribed allopurinol were more likely to be younger, male, and non-diabetic, and had higher serum creatinine and lower (treated) serum uric acid levels (mean = 7.0 vs. 8.0 mg/dL, p < 0.001). The inverse association between allopurinol prescription and mortality in unadjusted analyses (HR 0.65, 95 % CI 0.52-0.81) was attenuated by covariate adjustment (HR 0.84, 0.66-1.06). In subgroup analyses, allopurinol was associated with lower mortality among patients with no history of cardiovascular disease (CVD) (HR 0.48, 0.28-0.83), but not among patients with CVD (HR 1.00, 0.76-1.32). A similar pattern was seen outside Japan and for cardiovascular (CV)-related mortality. CONCLUSIONS: Allopurinol prescription was not significantly associated with case-mix-adjusted mortality in Japanese HD patients overall, but was associated with lower all-cause and CV-related mortality in the subgroup of patients with no prior CVD history. These findings in HD patients may be related to findings in non-dialysis CKD patients showing lower CV event rates and mortality, and improved endothelial function with allopurinol prescription. These results are useful for designing future trials of allopurinol use in HD patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".