The Cost-Effectiveness of Axicabtagene Ciloleucel as Second-Line Therapy in Patients with Large B-Cell Lymphoma in the United States: An Economic Evaluation of the ZUMA-7 Trial
Bibliographic record
Abstract
Axicabtagene ciloleucel (axi-cel) was found to have superior clinical outcomes compared to standard of care (SOC; salvage chemoimmunotherapy, followed by high-dose therapy with autologous stem cell rescue for responders) for second-line large B-cell lymphoma (2L LBCL) in the pivotal ZUMA-7 trial. The aim of this analysis was to evaluate the cost effectiveness of using axi-cel compared to the current standard 2L LBCL therapy. A 3-state partitioned-survival model estimated the cost effectiveness and budget impact from a payer perspective in the United States. Clinical outcomes were extrapolated based on the pivotal trial. The model calculated expected quality-adjusted life years (QALYs), total costs (in United States dollars [USD], and the incremental cost-effectiveness ratio (ICER), along with the budget impact. Sensitivity and scenario analyses were performed. The proportion alive at 10 years was estimated as 48% for axi-cel and 38% for SOC; median overall survival was estimated at 59 and 24 months for axi-cel and SOC, respectively. Over a lifetime horizon, the model estimated a total of 5.56 and 7.08 QALYs for SOC and axi-cel, respectively, of which 41% and 74% were in the event-free state, respectively. Incremental QALYs and costs were 1.51 and $100,366 USD, resulting in an ICER of $66,381 USD per QALY for axi-cel versus SOC. Despite crossover to subsequent CAR T in the SOC arm, second-line CAR T use was found to improve the quality and length of life compared to SOC. Cost offsets due to subsequent CAR T use led to a limited incremental cost difference. Treatment with axi-cel is a cost-effective option that addresses an important unmet clinical need for patients with LBCL who relapse or are refractory to front-line therapy.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".