Epidemiology and Outcomes of Antibiotic De-escalation in Patients With Suspected Sepsis in US Hospitals
Bibliographic record
Abstract
BACKGROUND: Little is known about the frequency, hospital-level variation, predictors, and outcomes of antibiotic de-escalation in suspected sepsis. METHODS: We retrospectively analyzed adults admitted to 236 US hospitals from 2017-2021 with suspected sepsis (defined by blood culture draw, lactate measurement, and intravenous antibiotic administration) who were initially treated with ≥2 days of anti-methicillin-resistant Staphylococcus aureus (MRSA) and anti-pseudomonal antibiotics but had no resistant organisms that required these agents identified through hospital day 4. De-escalation was defined as stopping anti-MRSA and anti-pseudomonal antibiotics or switching to narrower antibiotics by day 4. We created a propensity score for de-escalation using 82 hospital and clinical variables; matched de-escalated to non-de-escalated patients; and assessed associations between de-escalation and outcomes. RESULTS: Among 124 577 patients, antibiotics were de-escalated in 36 806 (29.5%): narrowing in 27 177 (21.8%), cessation in 9629 (7.7%). De-escalation rates varied between hospitals (median, 29.4%; interquartile range, 21.3%-38.0%). Predictors of de-escalation included less severe disease on day 3-4, positive cultures for nonresistant organisms, and negative/absent MRSA nasal swabs. De-escalation was more common in medium, large, and teaching hospitals in the Northeast and Midwest. De-escalation was associated with lower adjusted risks for acute kidney injury (AKI) (odds ratio [OR], 0.80; 95% confidence interval [CI], .76-.84), intensive-care unit (ICU) admission after day 4 (OR, 0.59; 95% CI, .52-.66), and in-hospital mortality (OR, 0.92; 95% CI, .86-.996). CONCLUSIONS: Antibiotic de-escalation in suspected sepsis is infrequent, variable across hospitals, linked with clinical and microbiologic factors, and associated with lower risk for AKI, ICU admission, and in-hospital mortality.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".