Beta-Blockers for the Secondary Prevention of Myocardial Infarction in People with Dementia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease remains the most common cause of death in industrialized countries. The use of beta-blockers is well established as a secondary prevention of myocardial infarction. However, little is known about the benefits of beta-blockers for people living with dementia. OBJECTIVE: To evaluate the use of beta-blockers in people with dementia who have had a myocardial infarction, in order to identify associations between medication use, mortality, re-infarction and functional decline. METHODS: We searched for all studies (randomized trials, observational cohorts) reporting beta-blocker use in populations with both dementia and previous myocardial infarction. Relevant keywords were used in Medline, Embase, and Web of Science up to October 2018. Titles and abstracts were independently screened by two reviewers. Quality of eligible studies was assessed using the Newcastle-Ottawa Scale. PRISMA recommendations were followed throughout. RESULTS: Two observational studies were included, representing 10,992 individuals in a community setting and 129,092 individuals from a hospital record-linkage study. One showed use of beta-blockers reduced all-cause mortality (HR 0.74 (95% CI 0.64- 0.86) alongside evidence for an increased rate of functional decline in individuals aged≥65 with moderate to severe cognitive impairment (OR 1.34 (95% CI 1.11- 1.61)). The second study did not find an association between beta-blocker use and mortality in the population living with dementia. CONCLUSION: There is insufficient evidence to support use of beta-blockers to persons living with dementia. A single study provides limited evidence that beta-blockers improve survival rates but with associated detrimental effects on functional status in nursing home residents with cognitive impairment. Decisions to continue beta-blockers in persons living with dementia should be made on an individual basis.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".