Empiric Antimicrobial Therapy in Critical Illness: Results of a Surgical Infection Society Survey
Bibliographic record
Abstract
BACKGROUND: Antibiotics are prescribed commonly in the intensive care unit (ICU). Often, therapy is initiated empirically; practice patterns are not well characterized. We documented approaches to empiric antibiotic therapy among members of the Surgical Infection Society (SIS). METHODS: We sent a scenario-based questionnaire to all SIS members. The hypothetical cases addressed empiric broad-spectrum therapy for a patient with pyrexia and leukocytosis and the use of vancomycin for central venous catheter infection. RESULTS: The 113 respondents were primarily surgeons (96%) with a university-based practice (92%). Most attended in the ICU (72%), and they had practiced for 14 +/- 8 years. Whereas 63% of the respondents identified overuse of antibiotics as a problem in their ICU, only 19% said inadequate treatment of infection was a concern. For a febrile patient with negative cultures who was receiving antibiotics, estimates of the likelihood of infection increased across the three scenarios as the degree of organ failure increased (p < 0.0001; chi-square test). Deteriorating organ function was associated with a decision to broaden empiric therapy (58% vs. 33%; p < 0.0001) and to initiate anti-fungal therapy (27% vs. 9%; p < 0.0001) rather than to stop antibiotics and re-culture (15% vs. 51%; p < 0.0001). There was considerable variability in management strategy across the scenarios: Even in the face of organ dysfunction, 58% of physicians would add or change empiric therapy, whereas 30% would not. For each scenario, 23 to 25 antibiotic regimens were designated as optimal therapy. Only 45% of the respondents would initiate empiric vancomycin for suspected central line infection. Variability in approach was not explained by critical care practice, academic position, or country. CONCLUSIONS: Clinical deterioration is a strong determinant of a decision to initiate or broaden empiric antibiotic therapy during critical illness. The substantial variability in approach suggests a state of clinical equipoise that calls for more rigorous evaluation through a randomized controlled trial.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".