Antihypertensive drug use and the risk of sepsis-associated acute renal failure in the elderly
Bibliographic record
Abstract
Objective: To examine the association between exposure to antihypertensive drugs and the occurrence of acute renal failure during sepsis. Study design: A cohort study of 25 830 Québec residents, aged over 65 years, discharged from an acute care hospital with a diagnosis of sepsis between 1997 2004. Outcomes: Discharge diagnosis of acute renal failure and in-hospital renal replacement therapy. Exposure: Outpatient exposure to antihypertensive drugs in the 60 days prior to hospital admission. Statistical analysis: Crude and adjusted odds ratios for acute renal failure and renal replacement therapy associated with antihypertensive drug exposure were estimated using multivariate logistic regression models including baseline characteristics and comorbidities. Results: Thiazide diuretics, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers were associated with an increased risk of both acute renal failure and renal replacement therapy, except in the subgroup with chronic renal failure, for which an increased risk could not be demonstrated. Exposure to loop diuretics was associated with an increased risk of renal replacement therapy, but did not modify the risk of acute renal failure. Simultaneous use of more than two antihypertensive drugs increased the risk of both acute renal failure and of renal replacement therapy. Conclusion: During sepsis, prior exposure to thiazide diuretics, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and antihypertensive polytherapy (>2 drugs) increase the risk of sepsis-related acute renal failure and renal replacement therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".