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2 results on '"Reitsma, S. H."'

1. Ethical aspects of hemophilia gene therapy:a qualitative interview study with stakeholders

Author

Baas, Lieke, Meijer, Karina, Driessens, Mariëtte, Bredenoord, Annelien L., van der Graaf, Rieke, Cnossen, M. H., Reitsma, S. H., Baas, Lieke, Meijer, Karina, Driessens, Mariëtte, Bredenoord, Annelien L., van der Graaf, Rieke, Cnossen, M. H., and Reitsma, S. H.

Abstract

Background: There are great expectations for the potential role of gene therapy in the treatment of hemophilia. At the same time, developments in the field of hemophilia gene therapy have always raised ethical issues. It remains unknown how these ethical issues are perceived by stakeholders, particularly regarding the most recent developments in the field. Objectives: To obtain insight into stakeholders’ morally reasoned opinions on gene therapy for hemophilia. Methods: We conducted qualitative research with Dutch people with hemophilia (n = 13), parents of children with hemophilia (n = 5), physicians (n = 4), nurses (n = 3), a regulator (n = 1), and a representative from a pharmaceutical company (n = 1). We conducted semistructured interviews based on a topic list and reported the results according to the Consolidated Criteria for Reporting Qualitative Research guidelines. Results: We identified 3 main themes. The theme freedom and independence describes the hope people with hemophilia have of increasing their freedom through gene therapy, as well as concerns that gene therapy increases their dependence on their treatment center. The theme trust and altruism describes how people with hemophilia have a high level of trust in their physician and treatment center as well as in scientific research. As a result of this trust, they are willing to participate in research to help other people with hemophilia. The theme incremental benefits describes doubts respondents have about the added value of gene therapy compared to standard treatment. Conclusion: Stakeholders embrace the theoretical potential of gene therapy, while several people with hemophilia question the added value of the current gene transfer products for themselves.

Published
2023

2. Application of SHAP values for inferring the optimal functional form of covariates in pharmacokinetic modeling.

Author

Janssen, Alexander, Hoogendoorn, Mark, Cnossen, Marjon H., Mathôt, Ron A. A., Cnossen, M. H., Reitsma, S. H., Leebeek, F. W. G., Mathôt, R. A. A., Fijnvandraat, K., Coppens, M., Meijer, K., Schols, S. E. M., Eikenboom, H. C. J., Schutgens, R. E. G., Beckers, E. A. M., Ypma, P., Kruip, M. J. H. A., Polinder, S., Tamminga, R. Y. J., and Brons, P.

Subjects
FIXED effects model, BLOOD coagulation factor VIII antibodies, PHARMACOKINETICS, BLOOD coagulation factor VIII, RANDOM forest algorithms, MACHINE learning
Abstract

In population pharmacokinetic (PK) models, interindividual variability is explained by implementation of covariates in the model. The widely used forward stepwise selection method is sensitive to bias, which may lead to an incorrect inclusion of covariates. Alternatives, such as the full fixed effects model, reduce this bias but are dependent on the chosen implementation of each covariate. As the correct functional forms are unknown, this may still lead to an inaccurate selection of covariates. Machine learning (ML) techniques can potentially be used to learn the optimal functional forms for implementing covariates directly from data. A recent study suggested that using ML resulted in an improved selection of influential covariates. However, how do we select the appropriate functional form for including these covariates? In this work, we use SHapley Additive exPlanations (SHAP) to infer the relationship between covariates and PK parameters from ML models. As a case‐study, we use data from 119 patients with hemophilia A receiving clotting factor VIII concentrate peri‐operatively. We fit both a random forest and a XGBoost model to predict empirical Bayes estimated clearance and central volume from a base nonlinear mixed effects model. Next, we show that SHAP reveals covariate relationships which match previous findings. In addition, we can reveal subtle effects arising from combinations of covariates difficult to obtain using other methods of covariate analysis. We conclude that the proposed method can be used to extend ML‐based covariate selection, and holds potential as a complete full model alternative to classical covariate analyses. [ABSTRACT FROM AUTHOR]

Published
2022
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