Antihypertensive agents acting on the renin–angiotensin system and the risk of sepsis
Bibliographic record
Abstract
AIMS: In response to safety concerns from two large randomized controlled trials, we investigated whether the use of telmisartan, an angiotensin receptor blocker (ARB), ARBs as a class and angiotensin-converting enzyme inhibitors (ACEIs) increase the risk of sepsis, sepsis-associated mortality and renal failure in hypertensive patients. METHODS: We performed a nested case-control study from a retrospective cohort of adults with hypertension from the UK General Practice Research Database diagnosed between 1 January 2000 and 30 June 2009. All subjects hospitalized with sepsis during follow-up were matched for age, sex, practice and duration of follow-up with 10 control subjects. Exposure was defined as current use of antihypertensive drugs. RESULTS: From the cohort of 550 436 hypertensive patients, 1965 were hospitalized with sepsis during follow-up (rate 6.9 per 10 000 per year), of whom 824 died and 346 developed acute renal failure within 30 days. Compared with use of β-blockers, calcium-channel blockers or diuretics, use of ARBs, including telmisartan, was not associated with an elevated risk of sepsis (relative risk 1.09; 95% confidence interval 0.83-1.43); but use ACEIs was (relative risk 1.65; 95% confidence interval 1.42-1.93). Users of ARBs, β-blockers, calcium-channel blockers or diuretics, but not users of ACEIs, had lower rates of hospitalization for sepsis compared with untreated hypertensive patients. Findings were similar for sepsis-related 30 day mortality and renal failure. CONCLUSIONS: Hypertensive patients treated with ARBs, including telmisartan, do not appear to be at increased risk of sepsis or sepsis-related 30 day mortality or renal failure. On the contrary, users of ACEIs may have an increased risk.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".