Abstract P4-15-01: Cost-effectiveness of pre-implantation genetic diagnosis for BRCA mutation carriers
Bibliographic record
Abstract
Abstract Introduction: Management of BRCA mutation carriers is very expensive due to preventive surgeries and/or screening tests, as well as greater likelihood of cancer treatment. The related cancer burden and costs continue from generation to generation. One relatively new option for male or female BRCA mutation carriers, who wish to have children, is pre-implantation genetic diagnosis (PGD) of in vitro fertilized embryos. PGD eliminates the mutation from the descendants of these carriers. The purpose of this study was to model the cost-effectiveness of PGD. Methods: We developed a Markov Model using TreeAge Pro 2016 and compared incidence of cancers, cancers-related death, costs, quality adjusted life-years (QALY), and incremental cost-effectiveness ratio (ICER) in the 2nd generation associated with conventional management of BRCA mutation carriers vs. PGD using a U.S. third-party payer's perspective with a lifetime horizon at a discount rate of 3% per year. In the model, health states were implemented to reflect the natural history of breast and ovarian cancer for women, and prostate cancer (and breast cancer in BRCA2 mutation carriers) for men. Model data were obtained from published literature. Costs were determined from published data and insurance payment schedules. Results: Our preliminary results show that for BRCA1 mutation carriers, the PGD with IVF strategy is associated with an increase of 0.29 QALYs and costs an additional $292.68 per person, translating to an ICER of $1,014.25/QALY when compared with “No PGD”, making it highly cost-effective. For BRCA2 mutation carriers, the PGD with IVF strategy is associated with an increase of 0.17 QALYs and costs an additional $4,916.88 per person, translating to an ICER of $28,436.10/QALY when compared with “No PGD”, making it cost-effective. Table 1StrategyCost ($)Incr Cost ($)Eff (QALY)Incr Eff (QALY)Incr C/E ($/QALY) BRCA 1 No PGD323,347.22 28.41 PGD323,639.89292.6828.70.291,014.25BRCA 2 No PGD318,723.02 28.52 PGD323,639.894,916.8828.70.1728,436.10 Conclusion: PGD for both BRCA1 and BRCA2 mutation carriers reduces cancer burden, increases QALYs and, is very cost-effective in the 2nd generation cohort, making this an attractive option from the perspectives of patients and public payers. Our estimates are conservative because the cost-effectiveness of PGD will likely improve further if subsequent generations are included in the model, given the expected further reduction in cancer burden and associated cost-savings in subsequent generations. Citation Format: Lipton JH, Wong WWL, Warner E, Greenblatt EM, Lee EK, Chan KKW. Cost-effectiveness of pre-implantation genetic diagnosis for BRCA mutation carriers [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-15-01.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.014 | 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".