Supporting Shared Decision-making About Surveillance After Breast Cancer With Personalized Recurrence Risk Calculations: Development of a Patient Decision Aid Using the International Patient Decision AIDS Standards Development Process in Combination With a Mixed Methods Design.

BACKGROUND: Although the treatment for breast cancer is highly personalized, posttreatment surveillance remains one-size-fits-all: annual imaging and physical examination for at least five years after treatment. The INFLUENCE nomogram is a prognostic model for estimating the 5-year risk for locoregional recurrences and second primary tumors after breast cancer. The use of personalized outcome data (such as risks for recurrences) can enrich the process of shared decision-making (SDM) for personalized surveillance after breast cancer. OBJECTIVE: This study aimed to develop a patient decision aid (PtDA), integrating personalized risk calculations on risks for recurrences, to support SDM for personalized surveillance after curative treatment for invasive breast cancer. METHODS: For the development of the PtDA, the International Patient Decision Aids Standards development process was combined with a mixed methods design inspired by the development process of previously developed PtDAs. In the development, 8 steps were distinguished: establishing a multidisciplinary steering group; definition of the end users, scope, and purpose of the PtDA; assessment of the decisional needs of end users; defining requirements for the PtDA; determining the format and implementation strategy for the PtDA; prototyping; alpha testing; and beta testing. The composed steering group convened during regular working-group sessions throughout the development process. RESULTS: The "Breast Cancer Surveillance Decision Aid" consists of 3 components that support the SDM process: a handout sheet on which personalized risks for recurrences, calculated using the INFLUENCE-nomogram, can be visualized and which contains an explanation about the decision for surveillance and a login code for a web-based deliberation tool; a web-based deliberation tool, including a patient-reported outcome measure on fear of cancer recurrence; and a summary sheet summarizing patient preferences and considerations. The PtDA was assessed as usable and acceptable during alpha testing. Beta testing is currently ongoing. CONCLUSIONS: We developed an acceptable and usable PtDA that integrates personalized risk calculations for the risk for recurrences to support SDM for surveillance after breast cancer. The implementation and effects of the use of the "Breast Cancer Surveillance Decision Aid" are being investigated in a clinical trial.

Effectiveness and implementation of SHared decision-making supported by OUTcome information among patients with breast cancer, stroke and advanced kidney disease: SHOUT study protocol of multiple interrupted time series.

INTRODUCTION: Within the value-based healthcare framework, outcome data can be used to inform patients about (treatment) options, and empower them to make shared decisions with their health care professional. To facilitate shared decision-making (SDM) supported by outcome data, a multicomponent intervention has been designed, including patient decision aids on the organisation of post-treatment surveillance (breast cancer); discharge location (stroke) and treatment modality (advanced kidney disease), and training on SDM for health care professionals. The SHared decision-making supported by OUTcome information (SHOUT) study will examine the effectiveness of the intervention and its implementation in clinical practice. METHODS AND ANALYSIS: Multiple interrupted time series will be used to stepwise implement the intervention. Patients diagnosed with either breast cancer (N=630), stroke (N=630) or advanced kidney disease (N=473) will be included. Measurements will be performed at baseline, three (stroke), six and twelve (breast cancer and advanced kidney disease) months. Trends on outcomes will be measured over a period of 20 months. The primary outcome will be patients' perceived level of involvement in decision-making. Secondary outcomes regarding effectiveness will include patient-reported SDM, decisional conflict, role in decision-making, knowledge, quality of life, preferred and chosen care, satisfaction with the intervention, healthcare utilisation and health outcomes. Outcomes regarding implementation will include the implementation rate and a questionnaire on the health care professionals' perspective on the implementation process. ETHICS AND DISSEMINATION: The Medical research Ethics Committees United in Nieuwegein, the Netherlands, has confirmed that the Medical Research Involving Human Subjects Act does not apply to this study. Bureau Onderzoek & Innovatie of Santeon, the Netherlands, approved this study. The results will contribute to insight in and knowledge on the use of outcome data for SDM, and can stimulate sustainable implementation of SDM. TRIAL REGISTRATION NUMBER: NL8374, NL8375 and NL8376.

Development of an online patient decision aid for kidney failure treatment modality decisions.

BACKGROUND: Patient decision aids (PtDAs) support patients and clinicians in shared decision-making (SDM). Real-world outcome information may improve patients' risk perception, and help patients make decisions congruent with their expectations and values. Our aim was to develop an online PtDA to support kidney failure treatment modality decision-making, that: 1) provides patients with real-world outcome information, and 2) facilitates SDM in clinical practice. METHODS: The International Patient Decision Aids Standards (IPDAS) development process model was complemented with a user-centred and convergent mixed-methods approach. Rapid prototyping was used to develop the PtDA with a multidisciplinary steering group in an iterative process of co-creation. The results of an exploratory evidence review and a needs-assessment among patients, caregivers, and clinicians were used to develop the PtDA. Seven Dutch teaching hospitals and two national Dutch outcome registries provided real-world data on selected outcomes for all kidney failure treatment modalities. Alpha and beta testing were performed to assess the prototype and finalise development. An implementation strategy was developed to guide implementation of the PtDA in clinical practice. RESULTS: The 'Kidney Failure Decision Aid' consists of three components designed to help patients and clinicians engage in SDM: 1) a paper hand-out sheet, 2) an interactive website, and 3) a personal summary sheet. A 'patients-like-me' infographic was developed to visualise survival probabilities for each treatment modality on the website. Other treatment outcomes were incorporated as event rates (e.g. hospitalisation rates) or explained in text (e.g. the flexibility of each treatment modality). No major revisions were needed after alpha and beta testing. During beta testing, some patients ignored the survival probabilities because they considered these too confronting. Nonetheless, patients agreed that every patient has the right to choose whether they want to view this information. Patients and clinicians believed that the PtDA would help patients make informed decisions, and that it would support values- and preferences-based decision-making. Implementation of the PtDA has started in October 2020. CONCLUSIONS: The 'Kidney Failure Decision Aid' was designed to facilitate SDM in clinical practice and contains real-world outcome information on all kidney failure treatment modalities. It is currently being investigated for its effects on SDM in a clinical trial.

Single-cell approaches identify the molecular network driving malignant hematopoietic stem cell self-renewal.

Recent advances in single-cell technologies have permitted the investigation of heterogeneous cell populations at previously unattainable resolution. Here we apply such approaches to resolve the molecular mechanisms driving disease in mouse hematopoietic stem cells (HSCs), using JAK2V617F mutant myeloproliferative neoplasms (MPNs) as a model. Single-cell gene expression and functional assays identified a subset of JAK2V617F mutant HSCs that display defective self-renewal. This defect is rescued at the single HSC level by crossing JAK2V617F mice with mice lacking TET2, the most commonly comutated gene in patients with MPN. Single-cell gene expression profiling of JAK2V617F-mutant HSCs revealed a loss of specific regulator genes, some of which were restored to normal levels in single TET2/JAK2 mutant HSCs. Of these, Bmi1 and, to a lesser extent, Pbx1 and Meis1 overexpression in JAK2-mutant HSCs could drive a disease phenotype and retain durable stem cell self-renewal in functional assays. Together, these single-cell approaches refine the molecules involved in clonal expansion of MPNs and have broad implications for deconstructing the molecular network of normal and malignant stem cells.

Index sorting resolves heterogeneous murine hematopoietic stem cell populations.

Recent advances in the cellular and molecular biology of single stem cells have uncovered significant heterogeneity in the functional properties of stem cell populations. This has prompted the development of approaches to study single cells in isolation, often performed using multiparameter flow cytometry. However, many stem cell populations are too rare to test all possible cell surface marker combinations, and virtually nothing is known about functional differences associated with varying intensities of such markers. Here we describe the use of index sorting for further resolution of the flow cytometric isolation of single murine hematopoietic stem cells (HSCs). Specifically, we associate single-cell functional assay outcomes with distinct cell surface marker expression intensities. High levels of both CD150 and EPCR associate with delayed kinetics of cell division and low levels of differentiation. Moreover, cells that do not form single HSC-derived clones appear in the 7AAD(dim) fraction, suggesting that even low levels of 7AAD staining are indicative of less healthy cell populations. These data indicate that when used in combination with single-cell functional assays, index sorting is a powerful tool for refining cell isolation strategies. This approach can be broadly applied to other single-cell systems, both to improve isolation and to acquire additional cell surface marker information.