Impact of inappropriate empirical antibiotic therapy on in-hospital mortality: a retrospective multicentre cohort study of patients with bloodstream infections in Chile, 2018–2022
Bibliographic record
Abstract
Introduction: Empirical antibiotic therapy is essential for treating bloodstream infections (BSI), yet there is limited evidence from resource-limited settings. We quantified the association of inappropriate empirical antibiotic therapy (IEAT) with in-hospital mortality and the associated burden on BSI patients in Chile. Methods: We used a retrospective multicentre cohort study of BSI cases in three Chilean tertiary hospitals (2018-2022) to assess the impact of IEAT on 30-day and overall in-hospital mortality and quantify excess disease and economic burdens associated with IEAT. We determined the appropriateness of pathogen-antimicrobial pairings based on in vitro susceptibilities and pathogen-corresponding antibiotic treatment, allowing a 48-hour window after the initial blood culture. We addressed confounding using propensity scores and inverse probability weights (IPW). We used IPW-weighted logistic competing-risk survival models, including time-varying independent variables after blood tests as controls. Results: Among 1323 BSI episodes, 432 (33%) received IEAT, with an average time to adequate therapy of 4.6 days. Compared with adequate treatment, IEAT was associated with 30-day and overall mortality risks that were 1.31 and 1.24 times higher, respectively. These risks were further inflated between twofold and fourfold when antibiotic-resistant bacteria (ARB) was included. Competing-risk models showed associations between IEAT and IEAT-ARB combinations with in-hospital mortality. Accounting for time-varying variables yielded similar results. The economic burden of IEAT resulted in an additional cost of ~US$9900 from premature mortality and 0.46 disability-adjusted life-years per patient with BSI. Conclusion: Approximately one in three patients received IEAT, often associated with ARB. IEAT was linked to increased mortality risk and higher economic costs. Timely appropriate treatment, early pathogen detection and resistance profiling are likely to improve health and financial outcomes at the population level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".