Prosthetic Valve in Chronic Dialysis: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Many patients with end stage kidney disease (ESKD) have valvular heart disease requiring surgery. The optimal prosthetic valve is not established in this population. We performed a systematic review and meta-analysis assessing outcomes of patients with dialysis-dependent ESKD who received mechanical or bioprosthetic valves. Methods: We searched Cochrane CENTRAL, MEDLINE, and EMBASE from inception to January 2020. We performed screening, full-text assessment, risk of bias, and data-collection independently and in duplicate. We evaluated risk of bias using the ROBINS-I tool and certainty in evidence with GRADE. Data were pooled using a random-effects model. Results: We identified 28 observational studies (n=9857; 6680 mechanical and 3717 bioprosthetic) with a median follow-up of 3.45 years. Due to confounding, 22 studies were at “high” and one at “critical” risk of bias. Certainty in evidence for all outcomes, except for bleeding, was very-low. Mechanical valves were associated with reduced mortality at 30 days (RR0.79, 95%CI[0.65,0.97], I2=0, absolute effect 27 fewer deaths per 1000) and at ≥ 6 years (mean 9.7 years, RR0.83, 95%CI[0.72,0.96], I2=79%, absolute effect 145 fewer deaths per 1000), but increased bleeding (RR2.46, 95%CI[1.35,4.48], I2=69% absolute effect 113 more events per 1000) and stroke (RR1.53, 95%CI[1.13,2.07], I2=0%, absolute effect 21 more events per 1000). Conclusion: Mechanical valves are associated with reduced mortality, but increased risks of bleeding and stroke. Given very-low certainty for mortality and stroke, patients and clinicians may choose a prosthetic valve based on factors such as bleeding risk and valve longevity.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".