Economic Comparison of an Empirical Versus Diagnostic-Driven Strategy for Treating Invasive Fungal Disease in Immunocompromised Patients
Bibliographic record
Abstract
PURPOSE: Patients with persistent or recurrent neutropenic fevers at risk of invasive fungal disease (IFD) are treated empirically with antifungal therapy (AFT). Early treatment using a diagnostic-driven (DD) strategy may reduce clinical and economic burdens. We compared costs and outcomes of both strategies from a UK perspective. METHODS: An empirical strategy with conventional amphotericin B deoxycholate (C-AmB), liposomal amphotericin B (L-AmB), or caspofungin was compared with a DD strategy (initiated based on positive ELISA results for galactomannan antigen) and/or positive results for Aspergillus species on polymerase chain reaction assay) using C-AmB, voriconazole, or L-AmB in a decision-analytic model. Rates of IFD incidence, overall mortality, and IFD-related mortality in adults expected to be neutropenic for ≥10 days were obtained. The empirical strategy was assumed to identify 30% of IFD and targeted AFT to improve survival by a hazard ratio of 0.589. AFT-specific adverse events were obtained from a summary of product characteristics. Resource use was obtained, and costs were estimated by using standard UK costing sources. All costs are presented in 2012 British pounds sterling. FINDINGS: Total costs were 32% lower for the DD strategy (£1561.29) versus the empirical strategy (£2301.93) due to a reduced incidence of adverse events and decreased use of AFT. Administration of AFT was reduced by 41% (DD strategy, 74 of 1000; empirical strategy, 125 of 1000), with similar survival rates. IMPLICATIONS: This study suggests that a DD strategy is likely to be cost-saving versus empirical treatment for immunocompromised patients with persistent or recurrent neutropenic fevers.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".