Cost-effectiveness of primary prophylaxis (PP) with colony-simulating factor (CSF) when compared with secondary prophylaxis (SP) in elderly patients with diffuse aggressive lymphoma undergoing curative chemotherapy
Bibliographic record
Abstract
6558 Background: The 2006 ASCO guideline recommends PP with CSF for elderly patients with diffuse aggressive lymphoma, partially based on previous cost-minimization analyses showing that CSF saved costs when compared with no CSF by reducing hospitalization from febrile neutropenia (FN) when the risk of FN was > 20%. However, these studies examined only one cycle of chemotherapy and did not account for costs of CSF in subsequent cycles, did not consider SP, and did not consider patients’ preferences. Methods: We conducted a cost-utility analysis to compare PP with SP in this setting using a Markov model for a time horizon of 8 cycles of chemotherapy with a government payer perspective. Costs were adjusted to 2006 $CAD. Ontario health economic data were used. The cost of hospitalization for FN was obtained from Ontario Case Costing Initiative. Data for efficacies of CSF, probabilities and utilities were obtained from published literature. Sensitivity analyses were conducted using a threshold of $100,000/QALY. Results: The base case costs for PP and SP were $22,077 and $17,641. The QALYs of PP and SP were 0.254 and 0.248. The incremental cost effectiveness ratio of PP to SP was $739,999/QALY. One-way sensitivity analyses showed that in order for PP to be cost-effective, the cost of hospitalization per episode of FN had to be > $31,138 (i.e. 2.5 times > base case), the cost of CSF per cycle had to be < $896 (base case = $1,960), the risk of FN in the 1st cycle had to be > 48% (base case = 24%), or the relative risk reduction of FN with CSF had to be > 97% (base case = 41%). Our result was robust to all other cost, probability and utility variables. First order microsimulation showed that < 17% of simulations were cost-effective. Conclusions: PP is not cost-effective when compared with SP for this population under most assumptions. PP only becomes attractive in places where the cost of hospitalization for FN is much more than that of Ontario, or the cost of CSF is under $896 per cycle. The costs of CSF and hospitalization in all cycles (instead of just one cycle) should be accounted for in any economic evaluation of CSF. Current guidelines recommending PP in this population should be revisited. No significant financial relationships to disclose.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".