Hypertension in Thoracic Aortic Dissection: A Meta-Analysis-Based Consideration in the Choice of Antihypertensive Agents
Bibliographic record
Abstract
BACKGROUND: Thoracic aortic dissection (TAD) is a potentially fatal condition. It has been linked with hypertension, and guidelines recommend antihypertensives. METHODS: Electronic searches were conducted in MEDLINE and EMBASE with the following search strategy: (("thoracic aortic dissection"[Mesh]) AND ("antihypertensive agents"[Mesh] from database inception to August 2024. RESULTS: Hypertension is associated with a significant risk of TAD with a hazard ratio (HR) of 2.51 (95% CI: 1.75-3.60). Beta-blocker treatment produces a significant (P < 0.01) lower risk of an MACE HR of 0.55 (95% CI = 0.39-0.77). Angiotensin receptor blockers (ARBs) or ACE inhibitors also lower the risk of a major adverse cardiac event with a HR of 0.67 (95% CI = 0.58-0.78). Calcium channel blockers (CCB) significantly (P =0.0007) lowered MACE outcomes with a HR of 0.66 (95% CI = 0.53-0.84). A network meta-analysis was performed to evaluate the relative risk of aortic events associated with commonly prescribed antihypertensive agents, using beta-blockers (BB) as the reference comparator. Compared to BB, angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (ACE/ARB) were associated with a non-significant increase in risk (HR 1.28, 95% confidence interval (CI): 0.91-1.81). CCB also demonstrated a non-significant reduction in risk (HR 0.68, 95% CI: 0.33-1.40) to BBs. CONCLUSIONS: Hypertension is strongly associated with a risk of TAD. Beta-blockers are associated with the greatest reduction in MACE and remain the most effective first-line therapy for patients at risk of TAD. ACE inhibitors and ARBs also demonstrate benefit.
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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 | 0.036 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.051 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".