Impact of Statins in Outcomes of Septic Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The pleiotropic effects of statins have prompted considerable research in fields other than cardiovascular disease. We reviewed the literature aiming to summarize and critically evaluate the current evidence about the potential use of statins in sepsis. MATERIALS AND METHODS: We searched the Pubmed, SciELO, and Cochrane electronic databases from inception through November 1, 2013, for randomized controlled trials (RCTs) and cohort studies that examined the association between statin use (upon hospital admission or previous users) and the risk or outcome of sepsis. Data on study characteristics, measurement of statin use, and outcomes (adjusted for potential confounders) were extracted. We structured our review according to the Principles of Reporting in Systematic Reviews and Meta-Analysis criteria. Quality assessment of cohort studies was performed using the Ottawa-Newcastle Scale. RESULTS: Twenty-three cohort studies and 5 RCTs were eligible, comprising 42 549 statin users and 54 201 non-statin users, from 1995 to 2013. The populations included varied from patients admitted to general wards or intensive care units with bacterial infections, community-acquired pneumonia, ventilator-associated pneumonia, bacteremia, or sepsis, to outpatients with chronic kidney disease or established cardiovascular disease. Overall, 16 studies reported a benefit from statin use in morbidity or mortality outcomes (range of adjusted odds ratio, 0.06-0.62; α = 0.05). The remaining 12 studies found no protective effect associated with statin use upon hospital admission or previous users. Among the 5 RCTs, none demonstrated a reduction in mortality. CONCLUSION: There is insufficient evidence to support the use of statins in patients with sepsis, as the existing studies failed to prove a consistent mortality benefit. More clinical trials are warranted to provide more conclusive knowledge and ultimately change clinical practice.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".