Do Angiotensin‐Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers Decrease the Risk of Hospitalization Secondary to Community‐Acquired Pneumonia? A Nested Case‐Control Study
Bibliographic record
Abstract
STUDY OBJECTIVE: As studies have shown that angiotensin-converting enzyme (ACE) inhibitors may lower the risk of developing pneumonia by increasing the cough reflex, we sought to explore the potential association between use of ACE inhibitors and the risk of hospitalization secondary to community-acquired pneumonia (CAP). To test this hypothesis further, we also looked at the risk for CAP in those taking angiotensin II receptor blockers (ARBs), as these drugs have a similar mechanism of action to that of ACE inhibitors but have minimal or no effect on the cough reflex. In addition, the putative protection against pneumonia may instead be related to general inhibition of the renin-angiotensin system. DESIGN: Nested case-control study. DATA SOURCE: Universal Quebec, Canada, administrative health databases. PATIENTS: From a cohort of 47,148 patients with coronary artery disease who had a revascularization procedure between 1996 and 2000, 1666 patients with CAP and 33,315 time-matched control subjects (20 controls for each case) were identified. MEASUREMENTS AND MAIN RESULTS: Conditional logistic regression analysis was used to estimate rate ratios, while controlling for potential confounders. No association was observed between patients receiving ACE inhibitors and hospitalization for CAP (rate ratio [RR] 0.98, 95% confidence interval [CI] 0.69-1.40). A similar lack of association was noted for those receiving ARBs (RR 1.02, 95% CI 0.70-1.49). CONCLUSION: In this case-control study, no association was found between use of ACE inhibitors or ARBs and risk of hospitalization secondary to CAP. Future studies are necessary to explore this association further.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".