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Record W3212362631 · doi:10.1182/blood-2021-151701

Are Novel Therapies Worth the Cost for Young Patients?: A Cost-Effectiveness Analysis of Frontline Therapies with or without Radiation for Early-Stage Unfavourable Hodgkin Lymphoma

2021· article· en· W3212362631 on OpenAlexaffabout
Abi Vijenthira, David Hodgson, Matthew C. Cheung, Michael Crump, Anca Prica

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePopulationRadiation therapyCost-effectiveness analysisBreast cancerCost effectivenessOncologyCancerIntensive care medicineInternal medicineRisk analysis (engineering)Environmental health

Abstract

fetched live from OpenAlex

Abstract Background: A variety of frontline treatment regimens exist for early-stage unfavourable Hodgkin lymphoma (HL), offering personalization of risk versus benefit in this primarily young population of patients. While radiation therapy has been a mainstay of treatment due to improved progression-free survival (PFS), recent studies have challenged this paradigm, using a PET-driven approach (HD17) or incorporating novel agents (nivolumab-AVD (N-AVD), brentuximab-AVD (A-AVD)). Long term risks of radiation and chemotherapy include secondary breast and other cancers, and heart failure; however novel regimens are more costly with uncertainty surrounding long-term efficacy. Methods: A cost-effectiveness and cost-utility analysis was conducted to compare five published frontline approaches for early-stage unfavourable HL: HD17, two H10 approaches, N-AVD, and A-AVD without radiation (Table 1). A Markov model was constructed with a lifetime horizon using TreeAge Pro 2021 (Figure 1). The base case was a 20-year-old female with a mediastinal mass who would require chest field radiation. Baseline estimates in the model were derived from the literature, including risk of relapse after each line of therapy, risk of late complications (breast cancer, secondary cancer, and/or heart failure), risk of death (from complications, lymphoma, and background mortality), and health state utilities. A Canadian public health care payer's perspective was taken, and costs are estimated in 2021 Canadian dollars. Global discounting of 3% was used. Results: Probabilistic sensitivity analyses were performed (10,000 simulations). First, we evaluated the uncertainty of long-term PFS using novel regimens (N-AVD or A-AVD); at a willingness-to-pay of $50,000/QALY, N-AVD was the most cost-effective regimen when 5-year PFS was at least 92% (Table 2). If 5-year PFS with N-AVD was <92%, HD17 became the most cost-effective approach. There was no PFS threshold at which A-AVD was the most cost-effective regimen. Holding the 5-year PFS of novel regimens at 92%, the model remained robust to multiple deterministic sensitivity analyses testing key variables including health state utilities (of relapse post-transplant, breast cancer, second malignancy, heart failure), costs (of radiation, autologous stem cell transplant, breast cancer, second malignancy, heart failure), and risks (of breast cancer after radiation, cardiovascular disease after radiation and/or chemotherapy,). However, if the risk of developing second cancer was less than 2% after 5 years with HD17 approach (current estimates 1% at 48 months in HD17 to 2% at 43 months in HD14 (which used a similar regimen)), or if the median overall survival after secondary cancer was over 9 years, HD17 became the most cost-effective regimen. The threshold cost for brentuximab to make A-AVD the most cost-effective regimen was <$5000 per dose (current price $14,520 CAD). Conclusions: If the long term PFS of nivolumab-AVD is greater than 92%, it could be the most cost-effective regimen when treating a young female patient with early-stage unfavourable Hodgkin lymphoma. This model accounts for increased costs with nivolumab added to chemotherapy, due to potential reduced incidence of late effects. However, there remain uncertainties in efficacy and risk regarding novel therapies as only non-randomized Phase II studies with short follow-up durations have been published; further trials of these approaches are being planned. HD17 remains the most cost-effective approach among published Phase III regimens. Long term follow-up of HD17 will also be meaningful to understand the risk of second cancer with this approach, which may impact its cost-effectiveness. Figure 1 Figure 1. Disclosures Crump: Epizyme: Research Funding; Kyte/Gilead: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding. Prica: Astra-Zeneca: Honoraria; Kite Gilead: Honoraria.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.202
GPT teacher head0.383
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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