<i>Clostridioides difficile</i> infection in a skilled nursing facility (SNF): cost savings of an automated, standardized probiotic antimicrobial stewardship programme (ASP) policy
Bibliographic record
Abstract
Background: infection (CDI) from 2009 to 2021 at an SNF. Probiotics were initially added to a bundle of antimicrobial stewardship programme (ASP) CDI prevention strategies. Formulations and durations of probiotics were standardized for both oral and enteral administration. To reach all eligible patients, an ASP probiotic policy provided probiotics with every antibiotic course. Objectives: To assess the value of providing probiotic therapy to SNF patients at risk for CDI. Patients and methods: Patients receiving oral or enteral feeding with antibiotics ordered were eligible to receive probiotics. The incremental cost of CDI prevention, treatment and related care were calculated and compared for each phase of probiotic policy change and feeding type. ASP records for the oral probiotic and level of treatment were used in modelling the cost-effectiveness. Results: From quality improvement initiatives aimed at preventing facility-onset (FO) CDI, to ASP policies, probiotic formulations and delegation of ordering authority, the days of acute care treatment required was significantly reduced over the different phases of implementation [152 to 48, OR = 0.22 (0.16-0.31) to 4, OR = 0.08 (0.03-0.23)] after reducing total CDI from 5.8 to 0.3 cases per 10 000 patient-days. The annual cost of oral probiotics increased from $6019 to $14 652 but the modelled net annual savings for the facility was $72 544-$154 085. Conclusions: With optimization, the use of probiotics for CDI prevention at an SNF was safe, efficacious and cost-effective.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".