Relationship between serum uric acid, all-cause mortality and cardiovascular mortality in peritoneal dialysis patients: systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
OBJECTIVES: To analyse the relationship between serum uric acid (SUA), all-cause and cardiovascular (CV) mortality in peritoneal dialysis (PD) patients to inform clinical practice and future research. DESIGN: A systematic review of observational studies. DATA SOURCES: PubMed, Embase, Web of Science, the Cochrane Library, China National Knowledge Infrastructure (CNKI), SinoMed, Chinese Science and Technology Journal Database (VIP) and Wan Fang databases were searched from their inception to January 2021 for cohort and case-control studies reporting SUA and mortality in patients with PD. METHODS: The Newcastle-Ottawa Quality Assessment Scale was used to appraise quality of cohort and case-control studies. Effect estimates were presented as HRs with 95% CIs in a meta-analysis using STATA V.16.0. Data not suitable for pooling were synthesised qualitatively. RESULTS: Fourteen cohort studies with 24 022 patients were included. No case-control studies were identified. For prospective cohort studies, pooled results for the highest SUA category were significantly greater than the lowest for all-cause (one study; 1278participants; HR 1.79; 95% CI 1.17 to 2.75) and CV mortality (one study; 1278 participants; HR 2.63; 1.62-4.27). An increase of 1 mg/dL in SUA level was associated with a 16% increased risk of all-cause mortality (one study; 1278 participants; HR 1.16; 1.03-1.32) and 34% increased CV mortality risk (one study; 1278 participants; HR 1.34; 1.16-1.55). For retrospective cohort studies, the highest SUA category did not demonstrate an elevated all-cause (five studies; 4570 participants; HR 1.09; 0.70-1.70) or CV mortality (three studies; 3748 participants; HR 1.00; 0.44-2.31) compared with the lowest SUA category. Additionally, there was no increase in all-cause (eight studies; 11 541 participants; HR 0.94; 0.88-1.02) or CV mortality (three studies; 7427 participants; HR 0.90; 0.76-1.06) for every 1 mg/dL increase in SUA level. CONCLUSIONS: Results of prospective and retrospective cohort studies were inconsistent. Consequently, prospective, multicentre, long-term follow-up studies are required to confirm the relationship between SUA and mortality in patients with PD.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".