Priorities may differ depending on healthcare systems when comparing the current state of key drivers versus projected needs for supporting genomic medicine sustainability. Thus we provide gap-filling guidance based on IGNITE members' experiences. Although results are limited to findings from the IGNITE network, their implementation, scientific, and clinical experience may be used as a road map by others considering implementing genomic medicine programs.

The landscape of treatment for multiple myeloma (MM) has significantly changed over the last decade due to novel agents that have shown superiority in efficacy such as proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs) over traditional therapies. However, the real-world utilization of these new agents has not been studied well. This study evaluated year-to-year changes in treatment choices in a cohort of patients aged 66 or older in the Surveillance, Epidemiology, and End Results (SEER) registry linked with Medicare claims (SEER-Medicare) data who were diagnosed with MM between 2007 and 2011. We identified 2477 symptomatic newly diagnosed patients who were followed for 6 months or more postdiagnosis and treated with systemic therapies but not with stem cell transplantation. Symptomatic patients were identified by evidence of hypercalcemia, renal failure, anemia, or bone lesions (CRAB criteria). The minimum follow-up was imposed to ensure sufficient data to characterize treatment. Our analysis found that the proportion of treated patients increased from 75% in the 2007 cohort to 79% in the 2011 cohort. The share of PI-based regimens including PI plus alkylating agents, PI plus IMiD, and PI-only increased from 9% to 21%, 3% to 11%, and 16% to 22%, respectively, between 2007 and 2011. These findings translate to the share of PI-based regimens having increased from 28% to 55% and that of IMiDs-based regimens (excluding PI plus IMiD) having decreased from 43% to 27%. In conclusion, while the usage of PIs among elderly MM patients increased significantly replacing IMiD-based regimens (with or without alkylating agents but not with PI) between 2007 and 2011, this significant shift did not increase the proportion of treated patients.

Achieving comprehensive patient centricity in cost-effectiveness analyses (CEAs) requires a statistical approach that accounts for patients' preferences and clinical and demographic characteristics. Increased availability and accessibility of patient-level health-related utility data from clinical trials or observational database provide enhanced opportunities to conduct more patient-centered CEA. Regression-based approaches that incorporate patient-level data hold great promise for enhancing CEAs to be more patient centered; this paper provides guidance regarding two CEA approaches that apply regression-based approaches utilizing patient-level health-related utility and costs data. The first approach utilizes patient-reported preferences to determine patient-specific utility. This approach evaluates how individuals' unique clinical and demographic factors affect their utility and cost levels over the course of treatment. The underlying motivation of this approach is to produce CEA estimates that reflect patient-level utilities and costs while adjusting for socio-demographic and clinical factors to aid patient-centered coverage and treatment decision-making. In the second approach, patient utilities are estimated based on the clinically defined health states through which a patient may transition throughout the course of treatment. While this approach is grounded on the widely used Markov transition model, we refine the model to facilitate an enhancement in conducting regression-based analysis to achieve transparent understanding of differences in utilities and costs across diverse patient populations. We discuss the unique statistical challenges of each approach and describe how these analytical strategies are related to non-regression-based models in health services research.

Background Translating pharmacogenetic research findings that have shown clinical efficacy into sustainable, routine clinical care at the institutional level requires strong evidence of improved patient outcomes bolstered by equitable reimbursement and a sound financial analysis. Although extensive research on the clinical value of pharmacogenetics has been completed, adoption into clinical practice lags due to a lack of evidence of clinical effectiveness and limited reimbursement. Methods The Sustainability Working Group within the NHGRI IGNITE I Network conducted an online survey of funded and non-funded IGNITE members to determine which genes they are researching, which have been translated into clinical practice, and how tests are billed. Data from the online surveys was consolidated and analyzed with results being tabulated for key findings. Due to the limited sample size, statistical analysis was forgone and results should be considered directional in nature. Results Fifteen out of twenty (75%) online survey responses were received and analyzed from IGNITE member sites delivering clinical care. Forty different genes were identified as being used for either research or clinical care. Thirty-two different genes were reported as being used clinically, an average of 6.9 genes were reported per site. Twenty-two and twenty-one genes were reported as being billed to third party payers or patients respectively. Although the survey did not ask whether sites submitting for reimbursement received payment, Medicare and Medicaid only reimburse for 6 of the 40 (15%) genes being tested. Of the 40 genes, 18 are rated by CPIC as having A/B level of evidence with the remainder being rated as C/D or having no rating. Approximately 32% more A/B rated genes were being reported clinically than non-A/B. Conclusion Adoption of pharmacogenetic testing continues to lag even at sites where leading experts conduct research and have the capability to report tests clinically. Clinical research that supports CPIC A level of evidence is important for provider and payer support. Adoption of pharmacogentic testing must also be justified financially, reimbursement is one key factor, and more health economic studies are needed in order to capture the value created by preventing drug-gene adverse events, emergency room visits, and hospitalizations.

The original version of this Article contained an error in the spelling of the author Geoffrey S. Ginsburg, which was incorrectly given as Geoffrey Ginsburg. This has now been corrected in both the PDF and HTML versions of the Article.