Kostick-Quenet Lab

Project Description

Recent progress in complex trait genetics, coupled with the technical ability to generate accurate genome-wide genotypes from single-cell input, has made it possible to genetically screen embryos for common polygenic traits and disease risk. However, little empirical work has been done to quantify the utility of polygenic embryo screening (PES), examine its ethical implications, and assess stakeholder perspectives. Our study is designed to provide the necessary empirical basis for informed ethical discussion.

Supported by: R01HG011711-01, National human genome research institute

Publications

Barlevy D, Cenolli I, Campbell T, et al. Divergence Between Clinician and Patient Perspectives on Polygenic Embryo Screening: A Qualitative Study. Preprint. medRxiv. 2023;2023.10.12.23296961. Published 2023 Oct 13. doi:10.1101/2023.10.12.23296961 

Pereira S, Carmi S, Altarescu G, et al. Polygenic embryo screening: four clinical considerations warrant further attention. Hum Reprod. 2022;37(7):1375-1378. doi:10.1093/humrep/deac110.

Pereira S, Carmi S, Altarescu G, et al. Polygenic embryo screening: four clinical considerations warrant further attention. Hum Reprod. 2022;37(7):1375-1378. doi:10.1093/humrep/deac110

Barlevy D, Lencz T, Carmi S, et al. Capacities and Limitations of Using Polygenic Risk Scores for Reproductive Decision Making. Am J Bioeth. 2022;22(2):42-45. doi:10.1080/15265161.2021.2013983

Lázaro-Muñoz, Pereira, S., Carmi, S., & Lencz, T. (2021). Screening embryos for polygenic conditions and traits: ethical considerations for an emerging technology. Genetics in Medicine, 23(3), 432–434.

Lencz, Lazaro-Munoz, G., Pereira, S., & Carmi, S. (2021). Polygenic embryo screening: science fiction or science Fact? European Neuropsychopharmacology, 51, e21–e22.2

Project Description

The development and efficacy-testing of a holistic, personalized, electronically integrated clinical decision support system for left-ventricular assist device candidates will help to ensure that heart failure patients receive tailored treatments that lead to optimal and values-based outcomes. Our study involves utilization of an AI/machine learning system that predicts personalized risks using big data. Specifically, it applies the most advanced personalized risk prediction technologies and decision support available to make sure that evidence about cardiac outcomes is used by both patients and clinicians in the service of shared decision making that leads to more informed and value-concordant health decisions. The impact of this personalized approach to clinical decision making addresses the urgent need to better identify and respond to the specific and dynamic nature of patient needs in seeking treatment for advanced HF.

We will do this by updating and integrating a validated online risk prediction and communication tool, the Heart Mate 3 Risk Score calculator developed by Dr. Mandeep Mehra and colleagues at Brigham and Women's Hospital, with our efficacy-tested decision aid (Deciding Together) for LVAD. 

This five-year project builds on 6 years of research on the development, implementation and dissemination of LVAD decision support and a decade of research into accurate risk prediction models for LVAD.

Supported by: R01 HS027784, Agency for Healthcare Research and Quality

Publications

Kristin M. Kostick-Quenet, Benjamin Lang, Natalie Dorfman, Jerry Estep, Mandeep R. Mehra, Arvind Bhimaraj, Andrew Civitello, Ulrich Jorde, Barry Trachtenberg, Nir Uriel, Holland Kaplan, Eleanor Gilmore-Szott, Robert Volk, Mahwash Kassi, J.S. Blumenthal-Barby. Patients’ and physicians’ beliefs and attitudes towards integrating personalized risk estimates into patient education about left ventricular assist device therapy. Patient Education and Counseling, Volume 122, 2024, 108157, ISSN 0738-3991. https://doi.org/10.1016/j.pec.2024.108157.

Kostick-Quenet K, Lang B, Smith J, Hurley M, Blumenthal-Barby JS. Trust Criteria for Artificial Intelligence in Health: Normative and Epistemic Considerations. Journal of Medical Ethics (November 18, 2023). https://doi.org/10.1136/jme-2023-109338.

Lang, Benjamin H., Blumenthal-Barby, J.S. Nyholm, Sven. Responsibility Gaps and Black Box Healthcare AI: Shared Responsibilization as a Solution. DISO 2, 52 (November 16, 2023). https://doi.org/10.1007/s44206-023-00073-z.

Hurley, Meghan E., Benjamin H. Lang, and Jared N. Smith. “Therapeutic Artificial Intelligence: Does Agential Status Matter?” The American Journal of Bioethics 23, no. 5 (May 4, 2023): 33–35. https://doi.org/10.1080/15265161.2023.2191037. 

Kostick-Quenet, Kristin M., Benjamin Lang, Natalie Dorfman, and J. S. Blumenthal-Barby. “A Call for Behavioral Science in Embedded Bioethics.” Perspectives in Biology and Medicine 65, no. 4 (September 2022): 672–79. https://doi.org/10.1353/pbm.2022.0059.

Blumenthal-Barby, J., Lang, B., Dorfman, N., Kaplan, H., Hooper, W. B., & Kostick-Quenet, K. (2022). "Research on the Clinical Translation of Health Care Machine Learning: Ethicists’ Experiences on Lessons Learned." American Journal of Bioethics, 22(5), 1–3. doi: 10.1080/15265161.2022.2059199.

Kostick, K.M., Cohen, G., Gerke, S., Lo, B., Antaki, J., Movahedi, F., Njah, H., Schoen, L., Estep, J. & Blumenthal-Barby, J.S. (2022). "Mitigating Racial Bias in Machine Learning." Special issue of the Journal of Law, Medicine & Ethics, 50(1), 92-100. doi:10.1017/jme.2022.13

Kostick, K.M., & Blumenthal-Barby, J.S. (2021). "Avoiding “Toxic Knowledge”: The importance of framing personalized risk information in clinical decision-making." Personalized medicine, 18(2), 91–95. https://doi.org/10/2217/pme-2020-0174