Renin-Angiotensin System Inhibitors and Major Cardiovascular Events after Sepsis
Bibliographic record
Abstract
Abstract Rationale Adult sepsis survivors have an increased risk of experiencing long-term cardiovascular events. Objectives To determine whether the cardiovascular risk after sepsis is mitigated by renin-angiotensin system inhibitors (RASi). Methods We conducted a population-based cohort study of adult sepsis survivors designed to emulate a target randomized trial with an active comparator and new-user design. We excluded patients with a first-line indication for prescription of RASi (e.g., coronary heart disease, heart failure, chronic kidney disease, and hypertension with diabetes mellitus). The main exposure of interest was a new prescription of a RASi within 30 days of hospital discharge. The active comparator was a new prescription of either a calcium channel blocker or a thiazide diuretic, also within 30 days of hospital discharge. The primary outcome of interest was the composite of myocardial infarction, stroke, and all-cause mortality during follow-up to 5 years. We used inverse probability weighting of a Cox proportional hazards model and reported results using hazard ratios with 95% confidence intervals. Results The cohort included 7,174 adult sepsis survivors, of whom 3,805 were new users of a RASi and 3,369 were new users of a calcium channel blocker or a thiazide diuretic. New users of a RASi experienced a lower hazard of major cardiovascular events than new users of a calcium channel blocker or a thiazide diuretic (hazard ratio, 0.93; 95% confidence interval, 0.87–0.99). This association was consistent across different follow-up intervals and multiple sensitivity analyses. Conclusions A new RASi prescription is associated with a reduction in major cardiovascular events after sepsis. A randomized controlled trial should be considered to confirm this finding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".