Impact of Increasing Wait Times on Overall Mortality of Chimeric Antigen Receptor T-Cell Therapy in Large B-Cell Lymphoma: A Discrete Event Simulation Model
Bibliographic record
Abstract
PURPOSE: The development of chimeric antigen receptor (CAR) T cells has transformed oncology treatment, with the potential to cure certain cancers. Although shown to be effective in selected populations and studies, CAR T-cell technology requires considerable health care resources, which may lead to additional wait times to access this type of treatment in future. The objective of our study was to estimate the potential impact of increasing wait times on CAR T-cell therapy effectiveness compared with standard chemotherapy for patients with relapsed/refractory diffuse large B-cell lymphoma. METHODS: A health system-level discrete event simulation model was developed to project the potential impact of wait times on CAR T-cell therapy for patients with relapsed/refractory diffuse large B-cell lymphoma. Waiting queues and health states related to treatment and clinical progression were implemented. Using data from the literature, we evaluated nine scenarios of using CAR T-cell therapy with wait times ranging from 1 to 9 months. The outcome of interest was 1-year all-cause mortality. RESULTS: Increasing the wait time of receiving CAR T-cell therapy from 1 to 9 months increased the predicted 1-year mortality rate from 36.1% to 76.3%. Baseline 1-year mortality was 34.0% in patients receiving CAR T-cell therapy with no wait times and 75.1% in patients treated with chemotherapy. This resulted in an increased relative mortality rate of 6.2% to 124.5% over a 1- to 9-month wait time compared with no wait time. CONCLUSION: We found that modest delays in CAR T-cell therapy significantly hinder its effectiveness. Because CAR T-cell therapy offers a potential cure, it is expected that the uptake rate will be substantially increased once the therapy is regularly funded by a health care system. Wait times may be prolonged if system resource availability does not match the demand. Strategies must be developed to minimize the impact of delays and reduce complications during waiting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".