Antihypertensives and myocardial infarction risk: the modifying effect of history of drug use
Bibliographic record
Abstract
PURPOSE: Confounding by indication is common in observational studies of outcomes that treatment is intended to affect. In light of the stepped-care approach to hypertension management, we reexamined the controversy around myocardial infarction (MI) risk in relation to antihypertensive agents by considering past drug history both as a confounder and as an effect modifier. METHODS: Case-control design nested within a cohort of 19,501 adults initiating therapy with angiotensin-converting enzyme inhibitors (ACEI), calcium channel blockers (CCB) or beta-blockers in Saskatchewan (1990-93) and followed up to 1997. MI cases were identified using death certificates and hospital discharge diagnoses (ICD-9,410). Four controls were matched to each case to account for duration and timing of follow-up. RESULTS: 812 MI cases were identified, of which 26% were fatal. At first, current use of CCB and ACEI (versus beta-blockers) appeared to be associated with an increased risk of MI (RR = 2.2; 95% CI = 1.8-2.7 and RR = 1.3; CI = 1.0-1.6 respectively). Adjustment for drug use history attenuated both associations (RR = 1.6; CI = 1.1-2.2 and RR = 1.0; CI = 0.7-1.4). Moreover, the risk for CCB use disappeared when restricted to patients who had already used these agents in the past (RR = 1.1; CI = 0.77-1.7) whereas a high risk of MI for ACEI was found in digoxin users (RR = 9.4; CI = 3.2-27.5). CONCLUSION: Past drug history can be both a confounder and an effect modifier in observational studies. We found adjustment for medication history to attenuate the associations between antihypertensive agents and MI risk. In addition, the estimates significantly varied across drug history profiles thus suggesting the presence of preferential prescribing of specific drug classes to high-risk patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".