Impact of Antibiotic De-Escalation on Antibiotic Consumption, Length of Hospitalization, Mortality, and Cost: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Overuse and misuse of antibiotics have led to the emergence of antibiotic-resistant bacteria and pose a significant threat due to adverse drug reactions, increased healthcare costs, and poor patient outcomes. Antibiotic stewardship programs, including antibiotic de-escalation, aim to optimize antibiotic use and to reduce the development of antibiotic resistance. This systematic review and meta-analysis aim to fill the gap by analyzing the current literature on the implications of antibiotic de-escalation in patients on antibiotic use, duration of hospital stay, mortality, and cost; to update clinical practice recommendations for the proper use of antibiotics; and to offer insightful information about the efficacy of antibiotic de-escalation. Based on the PRISMA 2020 recommendations, a comprehensive literature search was conducted using electronic databases and reference lists of identified studies. Eligible studies were published in English, conducted in humans, and evaluated the impact of antibiotic de-escalation on antibiotic consumption, length of hospitalization, mortality, or cost in hospitalized adult patients. Data were extracted using a standardized form, and the quality of included studies was assessed using the Newcastle–Ottawa Scale. The data from 25 studies were pooled and analyzed using the Revman-5 software, and statistical heterogeneity was evaluated using a chi-square test and I2 statistics. Among the total studies, seven studies were conducted in pediatric patients and the remaining studies were conducted in adults. The studies showed a wide range of de-escalation rates, with most studies reporting a rate above 50%. In some studies, de-escalation was associated with a decrease in antimicrobial utilization and mean length of stay, but the impact on overall cost was mixed. Our pooled analysis for mortality reported that a significant difference was observed between the de-escalation group and the non-de-escalation group in a random effect model (RR = 0.67, 95% CI 0.52–0.86, p = 0.001). The results suggest that de-escalation therapy can be applied in different healthcare settings and patient populations. However, the de-escalation rate varied depending on the study population and definition of de-escalation. Despite this variation, the results of this systematic review support the importance of de-escalation as a strategy to optimize antibiotic therapy and to reduce the development of subsequent antibiotic resistance. Further studies are needed to evaluate the impact of de-escalation on patient outcomes and to standardize the definition of de-escalation to allow for better comparison of studies.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.055 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".