Effect of Urate Lowering Therapy on Renal Disease Progression in Hyperuricemic Patients with Chronic Kidney Disease
Bibliographic record
Abstract
OBJECTIVE: To determine whether urate lowering therapy (ULT) could delay renal disease progression in hyperuricemic patients with chronic kidney disease (CKD). METHODS: We performed a retrospective review of hyperuricemic patients with stage 3 CKD followed from September 2005 to July 2014 in Dongguk University Ilsan Hospital, Goyang, Korea. A total of 158 eligible patients were identified and 65 of them were treated with ULT in addition to the usual CKD management. We divided the patients according to the use of ULT and compared the estimated glomerular filtration rate (eGFR) change from baseline value and the proportion of renal disease progression (decline of eGFR > 30% of the baseline value, initiation of dialysis or eGFR < 15 ml/min/1.73m(2)) at the time of last followup. Risk factors for renal disease progression were identified by logistic regression analysis. RESULTS: After a median followup of 118.5 weeks (minimum 25, maximum 465), the ULT group showed better outcomes compared to the non-ULT group in terms of eGFR change from baseline (-1.19 ± 12.07 vs -7.37 ± 11.17 ml/min/1.73 m(2), p = 0.001) and the proportion of renal disease progression (12.3% vs 27.9%, p = 0.01). Goal-directed ULT showed better clinical outcomes compared to maintaining the initial ULT dose. Actual (area under the SUA-time curve adjusted by total observation time period) serum uric acid was significantly associated with the risk of renal disease progression (p for trend = 0.04) and actual serum uric acid level < 7 mg/dl reduced the risk of renal disease progression by 69.4%. CONCLUSION: ULT significantly delayed renal disease progression in hyperuricemic patients with CKD. Goal-directed ULT seems to be better than continuing the initial ULT prescription.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".