Acetylcholinesterase inhibitors and risk of bleeding and acute ischemic events in non‐hypertensive Alzheimer's patients
Bibliographic record
Abstract
INTRODUCTION: Acetylcholinesterase inhibitors (AChEIs) are commonly used to treat mild to moderate cases of Alzheimer disease (AD). To the best of our knowledge, there has been no study estimating the risk of bleeding and cardiovascular events in patients with non-hypertensive AD. Therefore, this study aimed to estimate the association between AChEIs and the risk of bleeding and cardiovascular ischemic events in patients with non-hypertensive AD. METHODS: A nested case-control study was conducted to estimate the risk of bleeding and ischemic events (angina, myocardial infarction [MI], and stroke) in patients with AD. This study was conducted using the UK Clinical Practice Research Datalink and Hospital Episode Statistics (HES) databases. The study cohort consisted of AD patients ≥65 years of age. The case groups included all AD subjects in the database who had a bleeding or ischemic event during the cohort follow-up. Four controls were selected for each case. Patients were classified as current users or past users based on a 60-day threshold of consuming the drug. Simple and multivariable conditional logistic regression analyses were used to calculate the adjusted odds ratio for bleeding events and cardiovascular events. RESULTS: We identified 507 cases and selected 2028 controls for the bleeding event cohort and 555 cases and 2220 controls for the ischemic event cohort. The adjusted odds ratio (OR) (95% confidence interval [CI]) for the association of AChEI use was 0.93 (0.75 to 1.16) for bleeding events, 2.58 (1.01 to 6.59) for angina, and 1.89 (1.07 to 3.33) for MI. Past users of AChEIs were also at increased risk of stroke (1.51 [1.00 to 2.27]). DISCUSSION: This is the first study assessing the risk of bleeding and cardiovascular events in patients with non-hypertensive AD. Our findings could be of great interest for clinicians and researchers working on AD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".