Procalcitonin for reduced antibiotic exposure in the critical care setting: A systematic review and an economic evaluation*
Bibliographic record
Abstract
OBJECTIVE: Procalcitonin may be associated with reduced antibiotic usage compared to usual care. However, individual randomized controlled trials testing this hypothesis were too small to rule out harm, and the full cost-benefit of this strategy has not been evaluated. The purpose of this analysis was to evaluate the effect of a procalcitonin-guided antibiotic strategy on clinical and economic outcomes. INTERVENTIONS: The use of procalcitonin-guided antibiotic therapy. METHODS AND MAIN RESULTS: We searched computerized databases, reference lists of pertinent articles, and personal files. We included randomized controlled trials conducted in the intensive care unit that compared a procalcitonin-guided strategy to usual care and reported on antibiotic utilization and clinically important outcomes. Results were qualitatively and quantitatively summarized. On the basis of no effect in hospital mortality or hospital length of stay, a cost or cost-minimization analysis was conducted using the costs of procalcitonin testing and antibiotic acquisition and administration. Costs were determined from the literature and are reported in 2009 Canadian dollars. Five articles met the inclusion criteria. Procalcitonin-guided strategies were associated with a significant reduction in antibiotic use (weighted mean difference -2.14 days, 95% confidence interval -2.51 to -1.78, p < .00001). No effect was seen of a procalcitonin-guided strategy on hospital mortality (risk ratio 1.06, 95% confidence interval 0.86-1.30, p = .59; risk difference 0.01, 95% confidence interval -0.04 to +0.07, p = .61) and intensive care unit and hospital lengths of stay. The cost model revealed that, for the base case scenario (daily price of procalcitonin Can$49.42, 6 days of procalcitonin measurement, and 2-day difference in antibiotic treatment between procalcitonin-guided therapy and usual care), the point at which the cost of testing equals the cost of antibiotics saved is when daily antibiotics cost Can$148.26 (ranging between Can$59.30 and Can$296.52 on the basis of different assumptions in sensitivity analyses). CONCLUSIONS: Procalcitonin-guided antibiotic therapy is associated with a reduction in antibiotic usage that, under certain assumptions, may reduce overall costs of care. However, the overall estimate cannot rule out a 7% increase 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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".