Cost-Effectiveness of Hospital-at-Home and Fecal Microbiota Transplantation in Treating Older Patients With <i>Clostridioides difficile</i>
Bibliographic record
Abstract
BACKGROUND: Clostridioides difficile infection (CDI) primarily affects older patients with comorbid conditions and has a high mortality rate. Fecal microbiota transplantation (FMT) is effective and cost-effective for CDI. In a recent study, we demonstrated the clinical benefits of combining hospital-at-home care with FMT for older patients with CDI, but its cost-effectiveness remains unknown. The current study aimed to evaluate the cost-effectiveness of the intervention in patients aged ≥70 years with CDI, compared with standard treatment. METHODS: The cost-utility analysis was conducted using data from a randomized clinical trial enrolling 217 patients, assessing the cost-effectiveness of the intervention over 90 days. Resource use was assessed from a healthcare sector perspective. Missing data were handled with proxy replacement and multiple imputation. Sensitivity analyses included probabilistic analysis, complete case analysis, adjustment of key unit prices, and a hospital perspective. A willingness-to-pay threshold was set to €22 994 or $24 863 per quality-adjusted life year (QALY). RESULTS: In the base case analysis, the intervention was dominant, with mean cost savings of €2556 ($2764) and a mean gain of 0.004 QALY. Although resource use was higher, the intervention resulted in an average reduction of 6 hospital admission days per patient and increased odds of clinical resolution. The results remained robust across different perspectives, the exclusion of patients with missing data, and variations in hospital admission costs. CONCLUSIONS: In patients aged ≥70 years with CDI, an intervention combining hospital-at-home care and FMT is cost-effective compared with standard treatment. The cost-effectiveness is mainly driven by fewer hospital admission days.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".