Mathematical model of the cost-effectiveness of the BioFire FilmArray Blood Culture Identification (BCID) Panel molecular rapid diagnostic test compared with conventional methods for identification of <i>Escherichia coli</i> bloodstream infections
Bibliographic record
Abstract
BACKGROUND: Gram-negative pathogens, such as Escherichia coli, are common causes of bloodstream infections (BSIs) and increasingly demonstrate antimicrobial resistance. Molecular rapid diagnostic tests (mRDTs) offer faster pathogen identification and susceptibility results, but higher costs compared with conventional methods. We determined the cost-effectiveness of the BioFire FilmArray Blood Culture Identification (BCID) Panel, as a type of mRDT, compared with conventional methods in the identification of E. coli BSIs. METHODS: We constructed a decision analytic model comparing BCID with conventional methods in the identification and susceptibility testing of hospitalized patients with E. coli BSIs from the perspective of the public healthcare payer. Model inputs were obtained from published literature. Cost-effectiveness was calculated by determining the per-patient admission cost, the QALYs garnered and the incremental cost-effectiveness ratios (ICERs) where applicable. Monte Carlo probabilistic sensitivity analyses and one-way sensitivity analyses were conducted to assess the robustness of the model. All costs reflect 2019 Canadian dollars. RESULTS: The Monte Carlo probabilistic analyses resulted in cost savings ($27 070.83 versus $35 649.81) and improved QALYs (8.65 versus 7.10) in favour of BCID. At a willingness to pay up to $100 000, BCID had a 72.6%-83.8% chance of being cost-effective. One-way sensitivity analyses revealed length of stay and cost per day of hospitalization to have the most substantial impact on costs and QALYs. CONCLUSIONS: BCID was found to be cost-saving when used to diagnose E. coli BSI compared with conventional testing. Cost savings were most influenced by length of stay and cost per day of hospitalization.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".