MO522: The Effect of Statin on Anemia in Patients With End-Stage Kidney Disease Receiving Hemodialysis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Although treatment with erythropoietin-stimulating agents is effective in treating anemia in patients with end-stage kidney disease undergoing hemodialysis, some uremia patients, especially those with inflammation, continue to suffer from anemia. HMG-CoA reductase inhibitors (statins), which are lipid-lowering agents, may have a pleiotropic effect in reducing inflammation. Hence, we investigated the role of statin on hemoglobin (Hb) in patients receiving hemodialysis. METHOD We searched the PubMed, Embase, Medline, and Cochrane databases by using predefined keywords and MeSH terms for randomized controlled trials or cohort studies. Studies were included if (1) participants with CKD/ESKD were included, (2) treatment arms with statins were included, (3) study type was randomized control trial/cohort study and (4) outcomes of interest were hemoglobin (Hb), erythropoietin resistance index (ERI) and ferritin. Risk of bias tool 2.0 and the Newcastle–Ottawa Scale (NOS) were utilized to assess risk of bias in randomized controlled trials and cohort studies, respectively. Egger's test was used to test publication bias. In addition, our study was reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 statement. RESULTS We eventually included 10 studies (5258 participants), comprising three randomized controlled trials and seven cohort studies. Overall, Hb increased by 0.84 g/dL [95% confidence interval (95% CI) −0.02–1.70] in all groups of using statins, including single-arm cohorts, and by 0.72 g/dL (95% CI −0.02–1.46) in studies with placebo control. Hb levels were higher in the study group than in the control group, with a mean difference of 0.18 (95% CI 0.04–0.32) at baseline and 0.86 (95% CI 0.74–0.99) at the endpoint. Ferritin increased by 9.97 mg/dL (95% CI −5.36–25.29) in the study group and decreased by 34.01 mg/dL (95% CI −148.16–80.14) in the control group; ferritin fluctuation was higher in the control group. CONCLUSION Statin may improve renal anemia in patients with CKD and receiving regular erythropoietin-stimulating agents. Future studies with more rigorous methodology and larger sample size study should be performed to confirm this beneficial effect.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".