Citations

Abstract Purpose: With improvements in breast cancer imaging, there has been a corresponding increase in false-positives and avoidable biopsies. There is a need to better differentiate when a breast biopsy is warranted and determine appropriate follow-up. This study describes the design and clinical performance of a combinatorial proteomic biomarker assay (CPBA), Videssa Breast, in women over age 50 years. Experimental Design: A BI-RADS 3, 4, or 5 assessment was required for clinical trial enrollment. Serum was collected prior to breast biopsy and subjects were followed for 6–12 months and clinically relevant outcomes were recorded. Samples were split into training (70%) and validation (30%) cohorts with an approximate 1:4 case:control ratio in both arms. Results: A CPBA that combines biomarker data with patient clinical data was developed using a training cohort (469 women, cancer incidence: 18.5%), resulting in 94% sensitivity and 97% negative predictive value (NPV). Independent validation of the final algorithm in 194 subjects (breast cancer incidence: 19.6%) demonstrated a sensitivity of 95% and a NPV of 97%. When combined with previously published data for women under age 50, Videssa Breast achieves a comprehensive 93% sensitivity and 98% NPV in a population of women ages 25–75. Had Videssa Breast results been incorporated into the clinical workflow, approximately 45% of biopsies might have been avoided. Conclusions: Videssa Breast combines serum biomarkers with clinical patient characteristics to provide clinicians with additional information for patients with indeterminate breast imaging results, potentially reducing false-positive breast biopsies.

Abstract This abstract was not presented at the symposium.

91 Background: Formal mechanisms to share data on quality remain immature in specialty palliative care. As the field grows, infrastructure that promotes collaboration among academic and community-based practice will be required to foster comparisons and benchmarking of data to inform areas for quality improvement. Further, such relationships will create a palliative care “quality improvement laboratory”, where proposed guidelines and best practices can be developed, implemented, and tested. Methods: We set out to bring together specialty palliative care practices with a shared vision for collaborative quality improvement. We modeled our approach after the Institute for Healthcare Improvement Breakthrough Series alongside our Rapid Learning Quality Improvement paradigm. We use a set of common data collection procedures, across an electronic point-of-care platform called Quality Data Collection Tool (QDACT), alongside a centralized data registry. Further, we meet and discuss challenges and issues, compare best practices, and brainstorm new projects through biweekly conference calls. Results: We have created a multi-institutional collaboration for quality assessment and improvement in specialty palliative care. Termed the Global Palliative Care Quality Alliance, we have brought together 11 academic and community organizations, both general and oncology-specific, across six states to study various areas of quality practice. Short-term, we will conduct rapid-cycling quality improvement projects addressing National Quality Forum domains for quality palliative care, including documentation of spiritual assessment and timely advance care planning. Long-term, we aim to study the link between quality measure adherence and outcomes and further align our initiatives with those of other large consortia, like the Palliative Care Research Cooperative and Palliative Care Quality Network. Conclusions: Collaborative quality improvement is needed in specialty palliative care across a national platform. Developing the infrastructure to perform standardized quality improvement is achievable across multiple palliative care settings.