Antimicrobial therapeutic determinants of outcomes from septic shock among patients with cirrhosis
Bibliographic record
Abstract
UNLABELLED: It is unclear whether practice-related aspects of antimicrobial therapy contribute to the high mortality from septic shock among patients with cirrhosis. We examined the relationship between aspects of initial empiric antimicrobial therapy and mortality in patients with cirrhosis and septic shock. This was a nested cohort study within a large retrospective database of septic shock from 28 medical centers in Canada, the United States, and Saudi Arabia by the Cooperative Antimicrobial Therapy of Septic Shock Database Research Group between 1996 and 2008. We examined the impact of initial empiric antimicrobial therapeutic variables on the hospital mortality of patients with cirrhosis and septic shock. Among 635 patients with cirrhosis and septic shock, the hospital mortality was 75.6%. Inappropriate initial empiric antimicrobial therapy was administered in 155 (24.4%) patients. The median time to appropriate antimicrobial administration was 7.3 hours (interquartile range, 3.2-18.3 hours). The use of inappropriate initial antimicrobials was associated with increased mortality (adjusted odds ratio [aOR], 9.5; 95% confidence interval [CI], 4.3-20.7], as was the delay in appropriate antimicrobials (aOR for each 1 hour increase, 1.1; 95% CI, 1.1-1.2). Among patients with eligible bacterial septic shock, a single rather than two or more appropriate antimicrobials was used in 226 (72.9%) patients and was also associated with higher mortality (aOR, 1.8; 95% CI, 1.0-3.3). These findings were consistent across various clinically relevant subgroups. CONCLUSION: In patients with cirrhosis and septic shock, inappropriate and delayed appropriate initial empiric antimicrobial therapy is associated with increased mortality. Monotherapy of bacterial septic shock is also associated with increased mortality. The process of selection and implementation of empiric antimicrobial therapy in this high-risk group should be restructured.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".