Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing.

As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.

Conceptualization of utility in translational clinical genomics research.

Prior to integration into clinical care, a novel medical innovation is typically assessed in terms of its balance of benefits and risks, often referred to as utility. Members of multidisciplinary research teams may conceptualize and assess utility in different ways, which has implications within the translational genomics community and for the evidence base upon which clinical guidelines groups and healthcare payers make decisions. Ambiguity in the conceptualization of utility in translational genomics research can lead to communication challenges within research teams and to study designs that do not meet stakeholder needs. We seek to address the ambiguity challenge by describing the conceptual understanding of utility and use of the term by scholars in the fields of philosophy, medicine, and the social sciences of decision psychology and health economics. We illustrate applications of each field's orientation to translational genomics research by using examples from the Clinical Sequencing Evidence-Generating Research (CSER) consortium, and we provide recommendations for increasing clarity and cohesion in future research. Given that different understandings of utility will align to a greater or lesser degree with important stakeholders' views, more precise use of the term can help researchers to better integrate multidisciplinary investigations and communicate with stakeholders.

Does real option value influence oncologists' treatment recommendations? A survey of U.S. oncologists.

OBJECTIVES: Survival benefit from anticancer treatments, even if modest, improves a patient's chances of accessing future innovations, thereby creating real option value (ROV). There is no empirical evidence on the impact of potential future innovations on oncologists' treatment recommendations. METHODS: We conducted a national online survey of practicing medical and hematological oncologists. We presented a hypothetical metastatic cancer patient with median survival of 6 months under four decision-making scenarios with varying expected efficacy and time to arrival of future innovations. We assessed the likelihood of discussing future innovations with their patients and the likelihood that future innovations would influence their current treatment recommendation, as well as factors associated with these 2 outcomes using multivariate logistic regressions. RESULTS: 201 oncologists completed the survey. When future innovations were expected to improve survival by 6 months and be available in 6 months, 76% of oncologists were likely or very likely to discuss the innovations with their patients, and 68% reported they would influence their current treatment recommendations. A one-month increase in the expected survival improvement of future innovation was associated with a 1.17 (95% CI: 1.1-1.25) greater odds of reporting likely or very likely to discuss future innovations with their patients, while a one-month increase in the expected time to arrival was associated with a 0.91 (95% CI: 0.88-0.94) lower odds. CONCLUSIONS: As potential future innovations appear to influence oncologists' treatments recommendations, evidence to inform clinical guidelines and value assessments should consider data on ROV impacts to support informed treatment decision-making.

The value of the accelerated approval pathway: real-world outcomes associated with reducing the time between innovations.

Aim: We investigated the effect of shortening time between innovations with the accelerated approval (AA) pathway on patient outcomes for three solid tumors. Methods: This real-world analysis evaluated patients receiving sequential AA pathway-approved innovations after initial treatment with existing therapies in three solid tumor case studies. Outcomes attributable to AA were estimated and assumed approval occurred at the time of conversion to approval and extrapolated to the US population. Results: Survival gains from accessing innovative therapies were 2.3-3.8-times higher when using the AA pathway. At the US population level, AA was associated with ∼8000 life-years gained across all three tumor case studies. Conclusion: In areas of rapid clinical development, the value of existing therapies can be enhanced by earlier access to AA pathway innovations and should be considered when evaluating the AA program.

What is this study about? The US Food and Drug Administration's accelerated approval pathway provides patients with access to innovative drugs sooner than standard regulatory pathways. Using three case studies in solid tumors, this study measured how many patients on current cancer drugs received future cancer drugs because of the accelerated approval pathway and asked whether quicker access to new drugs resulted in them living longer.What were the results? In three cancer case studies, the accelerated approval pathway led to more patients receiving future cancer drugs. Patients who received future drugs through the AA pathway lived longer than patients who did not have access to them.What do the results of the study mean? The accelerated approval pathway is important because it can improve outcomes of current cancer drugs by giving patients additional treatments to choose from in the future and therefore a chance to live longer. Policymakers should consider this when thinking about making changes to the accelerated approval pathway.

Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis.

BACKGROUND: The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown. OBJECTIVE: To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH). DESIGN: Decision analytic Markov model. DATA SOURCES: Published literature. TARGET POPULATION: Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults. TIME HORIZON: Lifetime. PERSPECTIVE: U.S. health care payer. INTERVENTION: Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands. OUTCOME MEASURES: Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs. RESULTS OF BASE-CASE ANALYSIS: Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900). RESULTS OF SENSITIVITY ANALYSIS: Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters. LIMITATIONS: Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments. CONCLUSION: Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions. PRIMARY FUNDING SOURCE: National Human Genome Research Institute.

Longitudinal adherence to breast cancer surveillance following cancer genetic testing in an integrated health care system.

PURPOSE: Screening with mammography and breast magnetic resonance imaging (MRI) is an important risk management strategy for individuals with inherited pathogenic variants (PVs) in genes associated with increased breast cancer risk. We describe longitudinal screening adherence in individuals who underwent cancer genetic testing as part of usual care in a vertically integrated health system. METHODS: We determined the proportion time covered (PTC) by annual mammography and breast MRI for individuals with PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, and ATM. We determined time covered by biennial mammography beginning at age 50 years for individuals who received negative results, uncertain results, or with PVs in genes without specific breast cancer screening recommendations. RESULTS: One hundred and forty individuals had PVs in TP53, BRCA1, BRCA2, PALB2, NF1, CHEK2, or ATM. Among these individuals, average PTC was 48% (range 0-99%) for annual screening mammography and 34% (range 0-100%) for annual breast MRI. Average PTC was highest for individuals with PVs in CHEK2 (N = 14) and lowest for individuals with PVs in TP53 (N = 3). Average PTC for biennial mammography (N = 1,027) was 49% (0-100%). CONCLUSION: Longitudinal screening adherence in individuals with PVs in breast cancer associated genes, as measured by the proportion of time covered, is low; adherence to annual breast MRI falls below that of annual mammography. Additional research should examine screening behavior in individuals with PVs in breast cancer associated genes with a goal of developing interventions to improve adherence to recommended risk management.

Returning integrated genomic risk and clinical recommendations: The eMERGE study.

PURPOSE: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.

Risk-reducing surgery in unaffected individuals receiving cancer genetic testing in an integrated health care system.

BACKGROUND: Germline genetic testing enables primary cancer prevention, including through prophylactic surgery. We examined risk-reducing surgeries in unaffected individuals tested for hereditary cancer susceptibly between 2010 and 2018 in the Kaiser Permanente Northwest health system. METHODS: We used an internal genetic testing database to create a cohort of individuals who received tests including one or more high-penetrance hereditary cancer susceptibility gene. We then identified, after testing, bilateral mastectomy, bilateral salpingo-oophorectomy (BSO), and total hysterectomy procedures in electronic health record and claims data through 2019. We describe surgery utilization by genetic test results and National Comprehensive Cancer Network (NCCN) guidelines. RESULTS: The cohort included 1020 individuals, 16% with pathogenic/likely pathogenic (P/LP) variants in one or more of the following genes: BRCA1, BRCA2, CHEK2, APC, MUTYH, ATM, MSH2, PALB2, BRIP1, MLH1, MSH6, EPCAM, FLCN, RAD51C, RAD51D, or TP53. Among individuals with P/LP variants making them candidates for mastectomy, BSO, or hysterectomy per NCCN guidelines, 34% (33/97), 24% (23/94), and 8% (1/12), respectively, underwent surgery during follow-up. Fifty-three percent (18/37) of hysterectomies were among APC, BRCA1, and BRCA2 P/LP variant heterozygotes, typically concurrent with BSO. Three individuals with variants of uncertain significance (only) and 22 with negative results had prophylactic surgery after genetic testing. CONCLUSIONS: Uptake of risk-reducing surgery following usual care genetic testing appears to be lower than in studies that actively recruit high-risk patients and provide testing and follow-up care in specialized settings. Factors in addition to genetic test results and NCCN guidelines motivate prophylactic surgery use and deserve further study.

Implementation of pharmacogenomic clinical decision support for health systems: a cost-utility analysis.

We constructed a cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in acute coronary syndrome (ACS) and atrial fibrillation (AF), from a health system perspective. We defaulted that 20% of 500,000 health-system members between the ages of 55 and 65 received PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Clinical events, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program.

Prevalence and prediction of medical distrust in a diverse medical genomic research sample.

PURPOSE: Medical distrust has been identified as a persistent barrier to medical care, affecting preventative screening, treatment uptake, and treatment adherence. Despite this, little research to date has examined medical distrust in a genomic medicine context. The goal of this work was to assess the prevalence of medical distrust in a genomic medicine research study and examine patient-level demographic, access-related, and health-status characteristics that predict medical distrust. METHODS: We assessed medical distrust in a research sample of adults (N = 967) receiving genomic sequencing to screen for hereditary risk of cancer syndromes in the United States. We used multiple predictive variable selection models to determine predictors of medical distrust followed by marginal mean analyses to characterize the relationships. RESULTS: The prevalence of medical distrust was 32%. The final model indicated that Black and African American race/ethnicity; trans, nonbinary, or nonidentifying gender identity; high education; low income; low access to health care; and poor Short Form 12 mental health composite scores predict medical distrust. CONCLUSION: Medical distrust may pose similar challenges to genomic sequencing, as it does in other medical contexts. The pattern of variables that predict distrust suggest that increasing access and accommodation for stigmatized and underserved communities may help overcome the negative effects of medical distrust.

Cost-effectiveness of population-wide genomic screening for Lynch syndrome in the United States.

PURPOSE: Genomic screening for Lynch syndrome (LS) could prevent colorectal cancer (CRC) by identifying high-risk patients and instituting intensive CRC screening. We estimated the cost-effectiveness of a population-wide LS genomic screening vs family history-based screening alone in an unselected US population. METHODS: We developed a decision-analytic Markov model including health states for precancer, stage-specific CRC, and death and assumed an inexpensive test cost of $200. We conducted sensitivity and threshold analyses to evaluate model uncertainty. RESULTS: Screening unselected 30-year-olds for LS variants resulted in 48 (95% credible range [CR] = 35-63) fewer overall CRC cases per 100,000 screened individuals, leading to 187 quality-adjusted life-years (QALYs; 95% CR = 123-260) gained at an incremental cost of $24.6 million (95% CR = $20.3 million-$29.1 million). The incremental cost-effectiveness ratio was $132,200, with an 8% and 71% probability of being cost-effective at $100,000 and $150,000 per QALY willingness-to-pay thresholds, respectively. CONCLUSION: Population LS screening may be cost-effective in younger patient populations under a $150,000 willingness-to-pay per QALY threshold and with a relatively inexpensive test cost. Further reductions in testing costs and/or the inclusion of LS testing within a broader multiplex screening panel are needed for screening to become highly cost-effective.

Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways: A scoping review and recommended methods.

PURPOSE: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. METHODS: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. RESULTS: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. CONCLUSION: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.

A Pragmatic Guide to Assessing Real Option Value for Medical Technologies.

OBJECTIVES: This study aimed to provide recommendations for identifying and implementing real option value (ROV) calculations in value assessment. METHODS: We identified the primary mechanisms through which ROV can be created based on a theoretical framework for ROV, assessed approaches for predicting future innovations and improvements in health, and described the steps for estimating ROV in a cost-effectiveness analysis framework. RESULTS: The 3 primary mechanisms by which ROV can be created are when a current treatment (1) prolongs survival to increase the proportion of patients who can receive future innovations, (2) slows disease progression to increase patients' eligibility for future innovations, and (3) directly affects the efficacy of future innovations. We provide 5 recommendations for implementing ROV in value assessment. First, the decision to quantify ROV should be based on a qualitative evaluation of whether the treatment can enable greater benefits from future innovations. Second, ROV should be quantified in the same value assessment framework (eg, cost-effectiveness analysis using quality-adjusted life-year) as the conventional value. Third, method for quantifying ROV should consider data availability, rate of innovation, and sources of future health improvements. Fourth, ROV estimate should be presented alongside the conventional value as a separate element due to its inherently large uncertainty. Finally, generalizability of ROV estimate should be evaluated, and local data should be used when available. CONCLUSIONS: ROV can arise from a variety of mechanisms that should be considered before investing in an ROV analysis. Calculating ROV includes exploring different approaches for forecasting future innovations and future improvements in health.

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities.

OBJECTIVES: Clinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods. METHODS: We used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use. RESULTS: We found that the measurement of core elements of value-health outcomes and cost-are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians' productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians' workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians' work can improve productivity but may reduce skills. CONCLUSIONS: The potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.

A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.

OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study. METHODS: We used longitudinal RWD for a cohort of adults (n = 4247) from the Cystic Fibrosis Foundation Patient Registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in 1 second (FEV1) alone and (2) heterogenous usual care (UC). We then developed a patient-level simulation model to project number of patients referred for LTx and 5-year survival, accounting for transplant availability, organ allocation policy, and heterogenous treatment effects. RESULTS: Only 12% of patients (95% confidence interval 11%-13%) were referred for LTx over 5 years under UC, compared with 19% (18%-20%) under FEV1 and 20% (19%-22%) under ML. Of 309 patients who died before LTx referral under UC, 31% (27%-36%) would have been referred under FEV1 and 40% (35%-45%) would have been referred under ML. Given a fixed supply of organs, differences in referral time did not lead to significant differences in transplants, pretransplant or post-transplant deaths, or overall survival in 5 years. CONCLUSIONS: Health outcomes modeling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility and rule out further investment in models that are unlikely to offer meaningful real-world benefits.

Assessing Payers' Preferences for Real-World Evidence in the United States: A Discrete Choice Experiment.

OBJECTIVES: To rank the US payers' preferences for attributes of real-world evidence (RWE) studies in the context of chronic disease and to quantify trade-offs among them. METHODS: We conducted a discrete choice experiment in which 180 employees from payer organizations were tasked to choose between 2 RWE studies assuming they were assessing evidence to inform formulary decisions for chronic disease treatment. Each RWE study was characterized by 7 attributes with 3 levels each: very informative, moderately informative, and not measured. We used a D-optimal main-effects design. Survey data were fitted to a conditional logit model to obtain a relative measure of the ranking of importance for each attribute. RESULTS: Clinical outcomes were the most preferred attribute. It was 4.68 times as important as productivity outcomes-the least preferred attribute. It was followed by health-related quality of life (2.78), methodologic rigor (2.09), resource utilization (1.71), and external validity (1.56). CONCLUSIONS: This study provides a quantification of the value payers place on key RWE attributes. Across attributes, payers have higher preferences for clinical and health-related quality of life outcomes than the other attributes. Between attributes' levels, payers prefer high levels of information in clinical outcomes and methodologic rigor but are indifferent in other attributes. Our results bridge the gap between the information that payers seek and the attributes that RWE studies prioritize and effectively guide future research design.

Prescribing Alzheimer's Disease treatments by provider type and geographic region: a comparison among physicians, nurse practitioners, and physician assistants.

BACKGROUND: The estimated increase in Alzheimer's Disease (AD) caseload may present a logistical challenge to the US healthcare system. While nurse practitioners (NPs) and physician assistants (PAs) are increasingly delivering primary care to patients with chronic diseases, the nature of their prescribing of AD medications is largely unknown. The primary objective of this study was to compare the prescribing of AD medications across provider types (physician, NP, and PA) and geographic regions. METHODS: We conducted a retrospective cohort study using IBM MarketScan® commercial and Medicare supplemental claims to examine unique AD prescriptions prescribed between January 1, 2016, and December 31, 2019. Parallel analysis of prescriptions for another geriatric condition, osteoporosis (OP), was also conducted for comparison. RESULTS: A total of 103,067 AD prescriptions and 131,773 OP prescriptions were included in analyses. Physicians prescribed most AD prescriptions (95.65%), followed by NPs (3.37%) and PAs (0.98%). Small differences were identified among individual AD medications prescribed by physicians compared to NP/PAs. NPs/PAs prescribed a significantly higher proportion of AD prescriptions in rural as compared to urban areas (z = 0.023, 95%CI [0.018, 0.028]). CONCLUSION: Minimal variation exists in AD prescribing among physicians, NPs, and PAs, but NPs/PAs prescribe more AD prescriptions in rural areas. NPs/PAs, especially in rural areas, may play critical roles in alleviating projected workforce constraints. Further research assessing AD care, health outcomes, and costs by provider type and region is necessary to better guide healthcare workforce planning for AD care.

The Clinical Sequencing Evidence-Generating Research Consortium: Integrating Genomic Sequencing in Diverse and Medically Underserved Populations.

The Clinical Sequencing Evidence-Generating Research (CSER) consortium, now in its second funding cycle, is investigating the effectiveness of integrating genomic (exome or genome) sequencing into the clinical care of diverse and medically underserved individuals in a variety of healthcare settings and disease states. The consortium comprises a coordinating center, six funded extramural clinical projects, and an ongoing National Human Genome Research Institute (NHGRI) intramural project. Collectively, these projects aim to enroll and sequence over 6,100 participants in four years. At least 60% of participants will be of non-European ancestry or from underserved settings, with the goal of diversifying the populations that are providing an evidence base for genomic medicine. Five of the six clinical projects are enrolling pediatric patients with various phenotypes. One of these five projects is also enrolling couples whose fetus has a structural anomaly, and the sixth project is enrolling adults at risk for hereditary cancer. The ongoing NHGRI intramural project has enrolled primarily healthy adults. Goals of the consortium include assessing the clinical utility of genomic sequencing, exploring medical follow up and cascade testing of relatives, and evaluating patient-provider-laboratory level interactions that influence the use of this technology. The findings from the CSER consortium will offer patients, healthcare systems, and policymakers a clearer understanding of the opportunities and challenges of providing genomic medicine in diverse populations and settings, and contribute evidence toward developing best practices for the delivery of clinically useful and cost-effective genomic sequencing in diverse healthcare settings.

The FamilyTalk randomized controlled trial: patient-reported outcomes in clinical genetic sequencing for colorectal cancer.

As genetics gains favor in clinical oncology, it is important to address patient concerns around confidentiality, privacy, and security of genetic information that might otherwise limit its utilization. We designed a randomized controlled trial to assess the social impact of an online educational tool (FamilyTalk) to increase family communication about colorectal cancer (CRC) risk and screening. Of 208 randomized participants, 149 (71.6%) returned six-month surveys. Overall, there was no difference in CRC screening between the study arms. Privacy and confidentiality concerns about medical and genetic information, reactions to genetic test results, and lifestyle changes did not differ between arms. Participants with pathogenic or likely pathogenic (P/LP) and variant of uncertain significance (VUS) results were more likely than those with negative results to report that the results accurately predicted their disease risks (OR 5.37, p = 0.02 and OR 3.13, p = 0.02, respectively). This trial demonstrated no evidence that FamilyTalk impacted patient-reported outcomes. Low power, due to the limited number of participants with P/LP results in the overall sample, as well as the short follow-up period, could have contributed to the null findings.

Modeling the Ex Post Real Option Value in Metastatic Melanoma Using Real-World Data.

OBJECTIVES: Real option value (ROV) is created when a drug enables a patient to live long enough to benefit from a future innovation. Few studies have quantified ROV in the real world. We aimed to estimate the ex post ROV for ipilimumab in metastatic melanoma using real-world data (RWD). METHODS: We developed a framework for calculating ROV using RWD, accounting for the health gain in the standard therapy arm and the uptake of future innovations. A Markov model was developed to estimate the quality-adjusted life-years (QALYs) gained with ipilimumab compared with chemotherapy for patients with or without subsequent cancer immunotherapy (CIT). A nationwide electronic health record-derived, deidentified database was used to estimate survival and uptake of CIT. RESULTS: The incremental QALYs gained for ipilimumab compared with chemotherapy without subsequent CIT were 1.74. With subsequent CIT, the incremental QALYs compared with chemotherapy increased by 0.92, 0.60, 0.33, 0.18, 0.10, and 0.02 when CIT became available 0, 3, 6, 9, 12, and 24 months after the initiation of first-line treatment, respectively. The results were most sensitive to the survival benefit of ipilimumab, the survival benefit of subsequent CIT, and the uptake of CIT. CONCLUSIONS: This is the first study to estimate ex post ROV using RWD. The ex post ROV was between 1% and 54% of conventional value for patients who received a diagnosis within 2 years before CIT availability. Further studies are needed to understand ROV in other disease areas, particularly those with longer survival times.