1995. Serial Procalcitonin Measurement in a Community Intensive Care Unit: Is There Value in the Setting of an Established Antibiotic Stewardship Program?
Bibliographic record
Abstract
Abstract Background Procalcitonin (PCT) monitoring has been shown to result in reduced antibiotic use without an impact on patient outcomes. However, the real-world value of this biomarker has yet to be determined, particularly when efforts to optimize antibiotic use are already in place. We evaluated the feasibility and impact of PCT-guided antibiotic duration combined with an established antibiotic stewardship program (ASP) in a community hospital intensive care unit (ICU) in Toronto, Canada. Methods We conducted a quality improvement initiative in our ICU from November 2017 to October 2018 measuring daily PCT levels for immunocompetent patients receiving antibiotic therapy for suspected or proven bacterial infection with an expected duration between 48 hours and 21 days. Our protocol recommended stopping antibiotic therapy if PCT fell below 0.5 μg/L (absolute threshold) or if it dropped more than 80% from its peak value (relative threshold). ASP rounds took place twice weekly since 2013, integrating a regular discussion about PCT levels once this initiative was implemented. We evaluated the adherence to stopping criteria within 48h, antibiotic use (days of therapy per 1,000 patient-days), length of stay, 48h re-admission, and ICU-mortality. Interrupted time series with segmented regression was performed to evaluate pre-post intervention differences compared with the 12-months prior to implementation. Results A total of 297 antibiotic courses were monitored with PCT in 217 patients. Respiratory (62%), unknown infection (11%), and intra-abdominal infection (7%) were the most common reasons for antibiotics. Protocol adherence was 34% (absolute threshold: 39%, relative threshold: 12%). Adherence by ICU physician varied widely between 24% and 52%. Antibiotic use pre-PCT was 1,002 DOTs/1,000 PDs and post-PCT was 817 DOTs/1,000 PDs (adjusted change −15%, 95% CI: −28% to +8%) (Figure 1). No statistically significant changes in clinical outcomes were noted. Conclusion In the context of an active ASP in a community hospital ICU, PCT monitoring was associated with a non-significant decrease in antibiotic use. Further evaluation of reasons for inter-physician variability in adherence and opportunities for improved and sustained overall adherence should be explored. Disclosures All authors: No reported disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".