Chow, Selina

Introduction: Testing healthcare delivery interventions in rigorous clinical trials is a critical step in improving patient care, but conducting multisite randomized clinical trials to test the effect of care delivery interventions has unique challenges and requires foresight and planning. Methods: We conducted the first care delivery trial (A191402CD) in the Alliance for Clinical Trials in Oncology, a National Cancer Institute Community Oncology Research Program research base, which tested the effectiveness of two different decision aids for supporting shared decision-making about prostate cancer treatment. Our experience illustrates the kind of challenges that confront care delivery researchers as they seek to test interventions to improve the experiences of patients. Results: Lessons learned include the following: cluster-randomized designs introduce complexity; workflow disruption can discourage site participation; evidence-based methods may not always be sufficient. Conclusion: We conclude with the following recommendations: assessing feasibility requires special rigor; relationships and interpersonal dynamics must be leveraged. Our experiences may inform future care delivery research.

Introduction Socioeconomic disparities for breast cancer surgical care exist. Although the aetiology of the observed socioeconomic disparities is likely multifactorial, patient engagement during the surgical consult is critical. Shared decision-making may reduce health disparities by addressing barriers to patient engagement in decision-making that disproportionately impact socioeconomically disadvantaged patients. In this trial, we test the impact of a decision aid on increasing socioeconomically disadvantaged patients’ engagement in breast cancer surgery decision-making. Methods and analysis This multisite randomised trial is conducted through 10 surgical clinics within the National Cancer Institute Community Oncology Research Program (NCORP). We plan a stepped-wedge design with clinics randomised to the time of transition from usual care to the decision aid arm. Study participants are female patients, aged ≥18 years, with newly diagnosed stage 0–III breast cancer who are planning breast surgery. Data collection includes a baseline surgeon survey, baseline patient survey, audio-recording of the surgeon–patient consultation, a follow-up patient survey and medical record data review. Interviews and focus groups are conducted with a subset of patients, surgeons and clinic stakeholders. The effectiveness of the decision aid at increasing patient engagement (primary outcome) is evaluated using generalised linear mixed-effects models. The extent to which the effect of the decision aid intervention on patient engagement is mediated through the mitigation of barriers is tested in joint linear structural equation models. Qualitative interviews explore how barriers impact engagement, especially for socioeconomically disadvantaged women. Ethics and dissemination This protocol has been approved by the National Cancer Institute Central Institutional Review Board, and Certificate of Confidentiality has been obtained. We plan to disseminate the findings through journal publications and national meetings, including the NCORP network. Our findings will advance the science of medical decision-making with the potential to reduce socioeconomic health disparities. Trial registration number ClinicalTrials.gov Registry (NCT03766009).

Abstract Introduction: The preferred post-remission therapy for older patients (pts) with AML remains uncertain. We compared outcomes for older AML pts in CR1 receiving HCT reported to the CIBMTR to older AML pts achieving CR1 on National Clinical Trials Network induction and non-HCT consolidation therapy (CT) trials. Methods: This study focused on pts 60-75 years of age treated between 2004 to 2013. CT pts (n=211) underwent induction and consolidation on Alliance for Clinical Trials in Oncology, ECOG-ACRIN or SWOG clinical trials for initial therapy for newly diagnosed AML; the CIBMTR provided data for HCT pts (n=431). CT patients received at least one cycle of CT on study and were excluded if HCT occurred at any time. Time to event started at CR1 and pts entered at CT or HCT, respectively, using left-truncation to account for differential entry times. Results: For the CT cohort, first consolidation included standard therapy (e.g., cytarabine or a hypomethylating agent) and additional study drug (e.g., bortezomib, dasatinib, sorafenib, and Zosuquidar, gemtuzumab) or tipifarnib alone. Among HCT pts, the donor was a HLA-matched sibling or unrelated donor (URD) in 66% and the others were partially HLA-matched/mismatched URD (10%) or cord blood (24%). HCT pts were younger and more frequently had high-risk AML (high WBC, secondary AML and unfavorable cytogenetics) (Table). The median time from CR1 to HCT and CT was 3.2 and 0.5 months, respectively. Allogeneic HCT showed worse overall survival (OS) (HR=1.52, p=0.02) prior to 9 months and better OS thereafter (HR= 0.53, p <0.0001) relative to CT (figure 1A). Treatment-related mortality (TRM) was worse after HCT in the first 9 months (HR=2.8, CI: 1.5 -5.2, p=.0009), while relapse was less frequent beyond 9 months after treatment (HR = 0.42, CI: 0.29 to 0.61, p<.0001). Despite higher early TRM, HCT recipients went on to manifest superior OS [5 year OS: HCT 29% (24-34%), CT 13.8% (9 -22%)] (Figure 1A). The benefit of HCT for survival after 9 months was more prominent in those with unfavorable cytogenetics (Figure 1B). Multivariate analysis for OS showed no statistically significant effect of age or performance status, while unfavorable cytogenetics were detrimental (HR=1.74, p<.0001). Conclusions. Allogeneic HCT led to heightened early risks from TRM, but resulted in superior long-term survival in older AML pts receiving HCT relative to CT by reducing relapse. Efforts to attenuate early TRM after allogeneic HCT may further improve HCT outcomes for older pts. Disclosures Ustun: novartis: Speakers Bureau. Attar:Agios: Employment, Equity Ownership. Larson:Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BristolMyers Squibb: Consultancy, Research Funding; Ariad/Takeda: Consultancy, Research Funding. Roboz:Roche/Genentech: Consultancy; Eisai: Consultancy; Cellectis: Research Funding; Aphivena Therapeutics: Consultancy; Sandoz: Consultancy; Janssen Pharmaceuticals: Consultancy; Otsuka: Consultancy; Jazz Pharmaceuticals: Consultancy; Sandoz: Consultancy; AbbVie: Consultancy; Celltrion: Consultancy; Cellectis: Research Funding; Eisai: Consultancy; Astex Pharmaceuticals: Consultancy; Argenx: Consultancy; Pfizer: Consultancy; Celgene Corporation: Consultancy; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy; Bayer: Consultancy; Bayer: Consultancy; Roche/Genentech: Consultancy; Orsenix: Consultancy; Astex Pharmaceuticals: Consultancy; Celgene Corporation: Consultancy; Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Janssen Pharmaceuticals: Consultancy; Otsuka: Consultancy; Orsenix: Consultancy; Daiichi Sankyo: Consultancy; Celltrion: Consultancy; Novartis: Consultancy; Aphivena Therapeutics: Consultancy; Argenx: Consultancy; AbbVie: Consultancy. Uy:GlycoMimetics: Consultancy; Curis: Consultancy. Stone:Fujifilm: Consultancy; Ono: Consultancy; Orsenix: Consultancy; Otsuka: Consultancy; Agios: Consultancy, Research Funding; AbbVie: Consultancy; Argenx: Other: Data and Safety Monitoring Board; Celgene: Consultancy, Other: Data and Safety Monitoring Board, Steering Committee; Arog: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Jazz: Consultancy; Astellas: Consultancy; Merck: Consultancy; Pfizer: Consultancy; Sumitomo: Consultancy; Amgen: Consultancy; Cornerstone: Consultancy. Foran:Agios: Research Funding; Xencor, Inc.: Research Funding. Weisdorf:Seattle Genetics: Consultancy; Equillium: Consultancy; FATE: Consultancy; SL Behring: Consultancy; Pharmacyclics: Consultancy.