β-Blocker Dialyzability and Adverse Cardiovascular Outcomes in Hemodialysis Patients: A Meta-Analysis
Bibliographic record
Abstract
Background: β-blockers (BB) are one of the most common medications among hemodialysis (HD) patients. There are several BB with different pharmacokinetic properties. Particularly relevant for HD patients is BB dialyzability. In non-dialysis patients, abrupt withdrawal of BB has been associated with adverse cardiovascular events (CVE). HD patients receiving dialyzable BB may also be at increased risk for CVE. This systematic review aims to determine in HD patients if highly dialyzable BB (HDBB) (metoprolol, atenolol, and acebutolol) compared to poorly dialyzable BB (PDBB) (carvedilol, labetalol, bisoprolol, and propranolol) alters CVE and mortality. Methods: We searched MEDLINE from 1990 through February 2020 for studies of all forms. All cause mortality (ACM) and CVE were our primary outcomes. Random effects models were used to calculate pooled risk ratios (RR). Results: An initial search identified 1,066 articles. Exclusion criteria eliminated articles that did not include HD participants or did not compare at least two BB. Ultimately, three cohort studies comparing HDBB and PDBB were identified. All studies were retrospective cohort studies of large HD datasets of patients in the U.S. and Canada. The combined population size of the analyzed studies was 38,580 patients: 24,596 on HDBB and 13,984 on PDBB. There was significant heterogeneity between studies, with two suggesting harm associated with HDBB and one suggesting a reduction in mortality. The risk ratio derived from pooled data across these studies was 1.03 (95% CL: 0.88-1.22) for ACM and 0.94 (95% CL: 0.80-1.11) for CVE. Significant heterogeneity was seen with Iˆ2 values of 86% and 84% for ACM and CVE respectively. Conclusions: After a comprehensive search, only three cohort studies were identified comparing BB of different dialyzabilities. No randomized control trials were identified. The three cohort studies had varying results with two favoring HDBB and one favoring PDBB. Pooled results suggested a greater incidence of CVE in patients on PDBB compared to those on HDBB, while ACM is lower for PDBB than for HDBB. Given the heterogeneity of results it is unclear what type of BB should be used in HD patients. A randomized controlled trial comparing BB of different dialyzabilities is warranted. Funding: Veterans Affairs Support
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| 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".