Is Procalcitonin Biomarker-Guided Antibiotic Therapy a Cost-Effective Approach to Reduce Antibiotic Resistant and <i>Clostridium difficile</i> Infections in Hospitalized Patients?
Bibliographic record
Abstract
Antibiotics (AB) can reduce morbidity and mortality in the treatment of patients with sepsis and chronic obstructive pulmonary disease (COPD) exacerbations. Yet, AB overuse or misuse increases antibiotic resistance (ABR) and Clostridium difficile infections (CDI). This study projected the expected impact of a procalcitonin (PCT) biomarker testing strategy on incremental ABR cases and CDI, and costs of care in a population of patients hospitalized with suspected sepsis or a COPD exacerbation, in three European countries: the United Kingdom, Germany, and the Netherlands. Based on a systematic literature search and a decision model, we analyzed the number of ABR and CDI cases avoided and the incremental healthcare costs per patient from a societal perspective over the time horizon of a hospital stay. In the sepsis population, the PCT-guided antibiotic prescription strategy was projected to reduce the number of ABR cases with circa 6%, the number of CDI cases with 21%, and societal costs with circa €1300 per patient. In the COPD population, the number of ABR and CDI cases is reduced with circa 50%, and societal cost savings ranged €1701, €2473, and €2435 per patient in Germany, the Netherlands, and the United Kingdom, respectively. Model outcomes were most sensitive to the impact of the PCT-guided strategy on the number of intensive care unit days and general hospital ward days. Taken together, a PCT biomarker-guided antibiotic management strategy is likely to reduce the number of ABR and CDI cases and generate cost savings in a population of patients hospitalized with suspected sepsis or with a COPD exacerbation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".