Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-14T06:58:11.578Z Has data issue: false hasContentIssue false

Constructive Technology Assessment (CTA) as a tool in Coverage with Evidence Development: The case of the 70-gene prognosis signature for breast cancer diagnostics

Published online by Cambridge University Press:  06 January 2009

Valesca P. Retèl
Affiliation:
Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital
Jolien M. Bueno-de-Mesquita
Affiliation:
Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital
Marjan J. M. Hummel
Affiliation:
University of Twente
Marc J. van de Vijver
Affiliation:
Academic Medical Center
Kirsten F. L. Douma
Affiliation:
Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital
Kim Karsenberg
Affiliation:
Stichting DES-centrum
Frits S. A. M. van Dam
Affiliation:
Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital
Cees van Krimpen
Affiliation:
Kennemer Gasthuis Haarlem
Frank E. Bellot
Affiliation:
Spaarne Hospital Hoofddorp
Rudi M. H. Roumen
Affiliation:
Máxima Medical Centre
Sabine C. Linn
Affiliation:
Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital
Wim H. van Harten
Affiliation:
Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital

Abstract

Objectives: Constructive Technology Assessment (CTA) is a means to guide early implementation of new developments in society, and can be used as an evaluation tool for Coverage with Evidence Development (CED). We used CTA for the introduction of a new diagnostic test in the Netherlands, the 70-gene prognosis signature (MammaPrint®) for node-negative breast cancer patients.

Methods: Studied aspects were (organizational) efficiency, patient-centeredness and diffusion scenarios. Pre-post structured surveys were conducted in fifteen community hospitals concerning changes in logistics and teamwork as a consequence of the introduction of the 70-gene signature. Patient-centeredness was measured by questionnaires and interviews regarding knowledge and psychological impact of the test. Diffusion scenarios, which are commonly applied in industry to anticipate on future development and diffusion of their products, have been applied in this study.

Results: Median implementation-time of the 70-gene signature was 1.2 months. Most changes were seen in pathology processes and adjuvant treatment decisions. Physicians valued the addition of the 70-gene signature information as beneficial for patient management. Patient-centeredness (n = 77, response 78 percent): patients receiving a concordant high-risk and discordant clinical low/high risk-signature showed significantly more negative emotions with respect to receiving both test-results compared with concordant low-risk and discordant clinical high/low risk-signature patients. The first scenario was written in 2004 before the introduction of the 70-gene signature and identified hypothetical developments that could influence diffusion; especially the “what-if” deviation describing a discussion on validity among physicians proved to be realistic.

Conclusions: Differences in speed of implementation and influenced treatment decisions were seen. Impact on patients seems especially related to discordance and its successive communication. In the future, scenario drafting will lead to input for model-based cost-effectiveness analysis. Finally, CTA can be useful as a tool to guide CED by adding monitoring and anticipation on possible developments during early implementation, to the assessment of promising new technologies.

Type
General Essays
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Bleiker, EM, Hendriks, JH, Otten, JD, et al. Personality factors and breast cancer risk: A 13-year follow-up. J Natl Cancer Inst. 2008;100:213218.CrossRefGoogle Scholar
2. Bogaerts, J, Cardoso, F, Buyse, M, et al. Gene signature evaluation as a prognostic tool: Challenges in the design of the MINDACT trial. Nat Clin Pract Oncol. 2006;3:540551.CrossRefGoogle ScholarPubMed
3. Bueno-de-Mesquita, JM, Linn, SC, Keijzer, R, et al. Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat. 2008.CrossRefGoogle Scholar
4. Bueno-de-Mesquita, JM, van Harten, W, Retel, V, et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: A prospective community-based feasibility study (RASTER). Lancet Oncol. 2007;8:10791087.CrossRefGoogle ScholarPubMed
5. Buyse, M, Loi, S, van't Veer, L, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98:11831192.CrossRefGoogle ScholarPubMed
6. Douma, KF, Karsenberg, K, Hummel, MJ, et al. Methodology of constructive technology assessment in health care. Int J Technol Assess Health Care. 2007;23:162168.CrossRefGoogle ScholarPubMed
7. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet. 2005;365:16871717.CrossRefGoogle Scholar
8. Goldhirsch, A, Glick, JH, Gelber, RD, et al. Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer. Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer. J Clin Oncol. 2001;19:38173827.CrossRefGoogle ScholarPubMed
9. Hutton, J, Trueman, P, Henshall, C. Coverage with evidence development: An examination of conceptual and policy issues. Int J Technol Assess Health Care. 2007;23:425432.CrossRefGoogle ScholarPubMed
10. Institute of Medicine (IOM). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press; 2001.Google Scholar
11. Ioannidis, JP. Is molecular profiling ready for use in clinical decision making? Oncologist. 2007;12:301311.CrossRefGoogle ScholarPubMed
12. Kwaliteitsinstituut voor de Gezondheidszorg CBO VvIK. Conceptrichtlijn Mammacarcinoom 2008. 2008:123–145.Google Scholar
13. Kwaliteitsinstituut voor de Gezondheidszorg CBO, Vereniging voor Integrale Kankercentra: Adjuvante Systemische Therapie voor het Operabel Mammacarcinoom. Richtlijn Behandeling van het Mammacarcinoom 2005. 2005:46–70.Google Scholar
14. Lerman, C, Seay, J, Balshem, A, et al. Interest in genetic testing among first-degree relatives of breast cancer patients. Am J Med Genet. 1995;57:385392.CrossRefGoogle ScholarPubMed
15. Lynch, HT, Lemon, SJ, Durham, C, et al. A descriptive study of BRCA1 testing and reactions to disclosure of test results. Cancer. 1997;79:22192228.3.0.CO;2-Y>CrossRefGoogle ScholarPubMed
16. Mook, S, Van't Veer, LJ, Rutgers, EJ, et al. Individualization of therapy using Mammaprint: From development to the MINDACT Trial. Cancer Genomics Proteomics. 2007;4:147155.Google Scholar
17. Mowatt, G, Bower, DJ, Brebner, JA, et al. When and how to assess fast-changing technologies: A comparative study of medical applications of four generic technologies. Health Technol Assess. 1997;1:i-149.CrossRefGoogle ScholarPubMed
18. Olivotto, IA, Bajdik, CD, Ravdin, PM, et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005;23:27162725.CrossRefGoogle ScholarPubMed
19. Ravdin, PM, Siminoff, LA, Davis, GJ, et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol. 2001;19:980991.CrossRefGoogle Scholar
20. Retel, VP, Hummel, MJ, van Harten, WH. Early phase technology assessment of nanotechnology in oncology. Tumori. 2008;94:284290.CrossRefGoogle ScholarPubMed
21. Rogers, EM. Diffusion of innovations. 5th ed. New York: Free Press; 2003.Google Scholar
22. Schot, J, Rip, A. The Past and Future of Constructive Technology Assessment. Technol Forecast Soc Change. 1996;54:251268.CrossRefGoogle Scholar
23. Schot, JW. Constructive technology assessment and technology dynamics: The case of clean technologies. Sci Technol Human Values. 1992;17:3656.CrossRefGoogle Scholar
24. Tunis, SR, Chalkidou, K. Coverage with evidence development: A very good beginning, but much to be done. Commentary to Hutton et al. Int J Technol Assess Health Care. 2007;23:432435.CrossRefGoogle ScholarPubMed
25. Tversky, A, Kahneman, D. The framing of decisions and the psychology of choice. Science. 1981;211:453458.CrossRefGoogle ScholarPubMed
26. van't Veer, LJ, Dai, H, van de Vijver, MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530536.CrossRefGoogle ScholarPubMed
27. van de Vijver, MJ, He, YD, , van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:19992009.CrossRefGoogle ScholarPubMed
Supplementary material: File

Retel supplementary list

Retel supplementary list

Download Retel supplementary list(File)
File 51.2 KB
Supplementary material: File

Retel supplementary table

Retel supplementary table

Download Retel supplementary table(File)
File 100.4 KB