Modification of cardiovascular risk in hemodialysis patients: An evidence‐based review
Bibliographic record
Abstract
Cardiovascular disease accounts for 40% to 50% of deaths in dialysis populations. Overall, the risk of cardiac mortality is 10-fold to 20-fold greater in dialysis patients than in age and sex-matched controls without chronic kidney disease. The aim of this paper is to review critically the evidence that cardiac outcomes in dialysis patients are modified by cardiovascular risk factor interventions. There is limited, but as yet inconclusive controlled trial evidence that cardiovascular outcomes in dialysis populations may be improved by antioxidants (vitamin E or acetylcysteine), ensuring that hemoglobin levels do not exceed 120 g/L (especially in the setting of known cardiovascular disease), prescribing carvedilol in the setting of dilated cardiomyopathy, and by using cinacalcet in uncontrolled secondary hyperparathyroidism. Similarly, there are a number of negative controlled trials, which have demonstrated that statins, high-dose folic acid, angiotensin-converting enzyme inhibitors, multiple risk factor intervention via multidisciplinary clinics, and high-dose or high-flux dialysis are ineffective in preventing cardiovascular disease. Although none of these studies could be considered conclusive, the negative trials to date should raise significant concerns about the heavy reliance of current clinical practice guidelines on extrapolation of findings from cardiovascular intervention trials in the general population. It may be that cardiovascular disease in dialysis populations is less amenable to intervention, either because of the advanced stage of chronic kidney disease or because the pathogenesis of cardiovascular disease in dialysis patients is different from that in the general population. Large, well-conducted, multicenter randomized-controlled trials in this area are urgently required.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".