Modeling Costs and Life-Years Gained by Population-Wide Next-Generation Sequencing or Single-Gene Testing in Nonsquamous Non-Small-Cell Lung Cancer in the United States.

PURPOSE: Many patients with actionable driver oncogenes (ADOs) are never identified and thus never receive targeted treatment. This study evaluated the economic impact and the potential life-years gained (LYG) that can be attributed to the extent of next-generation sequencing (NGS) testing in the United States compared with single-gene testing (SGT) in patients with metastatic nonsquamous non-small-cell lung cancer in the United States. METHODS: A model was developed to evaluate incremental rates of SGT or NSG testing on the basis of LYG and cost per LYG. ADOs included for NGS included EGFR, ALK, ROS1, BRAF, RET, MET, and NTRK. SGT included EGFR and ALK. Assumptions were made for expected incidence of ADOs. Survival distributions were fit to published trial averages of median and 5-year overall survival. Treatment costs were estimated from drug cost averages. Reimbursement costs were based on data from the Center for Medicare and Medicaid Services. RESULTS: Each incremental 10% increase in NGS testing produces an average of 2,627.4 additional LYG, with an average cost savings per LYG of $75 US dollars (USD). Replacing SGT at the current rate of 80% with NGS testing would result in an average additional 21,09.6 LYG and reduce cost per LYG by an average of $599 USD. If 100% of eligible patients were tested with NGS and each identified patient had matched treatment, the total average cost per LYG would be $16,641.57 USD. CONCLUSION: On the basis of current evidence, population-level simulations demonstrate that clinically relevant gains in survival with non-negligible reduction in costs are obtainable from widespread adoption of NGS testing and appropriate treatment matching for patients with advanced nonsquamous non-small-cell lung cancer.

Impact of Socioeconomic Factors on Overall Survival in SCLC.

Objectives: To determine how the incidence and demographics of SCLC have changed over time and to evaluate whether patient demographics, disease presentation, and treatment characteristics affect patient outcomes. Methods: We identified patients with SCLC in the National Cancer Database from 2004 to 2016. Differences in demographics, disease, and treatment characteristics were assessed by year of diagnosis using chi-square test. The effect of age, race, insurance status, income, distance to treatment center, and education level on overall survival (OS) was evaluated by multivariable Cox proportional hazard model. Results: Patients diagnosed after 2010 were significantly older, more frequently treated at academic centers, had more comorbidities, had government payer insurance, had more stage IV disease, and lived further from treatment centers. More females, African Americans, patients without high school diplomas, and those from rural areas were diagnosed after 2010. In patients diagnosed between 2004 and 2010, 5-year OS was 6.8% (95% confidence interval: 6.6-6.9), and after 2010, 5-year OS was 8.7% (95% confidence interval: 8.5-8.9), despite an increase in stage IV disease in the latter group. Older patients, males, Caucasians, patients with stage IV disease, those with government primary payer insurance, and those from rural areas had significantly worse OS. Patients without comorbidities and treated at academic centers had significantly better OS. OS significantly increased with community income and education level. Conclusions: Despite improvement in OS, disparities were noted in demographics which may complicate patient and provider access to health care resources, including rural communities, distance to academic centers, income, insurer, and education level. Efforts to affect these variables will improve outcomes for patients with SCLC.

Impact of Workforce Challenges and Funds Flow on Cancer Clinical Trial Development and Conduct.

The conduct of clinical cancer research has faced considerable challenges in recent years, and the situation has only been exacerbated by the global pandemic. The growing complexity of clinical trials and rising administrative burdens had been causing greater expense and difficulty in recruiting and retaining an appropriately trained workforce even before the well-publicized increase in turnover caused by the pandemic. Longstanding issues such as restrictive inclusion criteria and complicated trial designs have negatively affected already low clinical trial accrual rates, limited sites capable of opening studies and enrolling patients, and worsened disparities in trial participation. Opposing these elements are efforts by ASCO and other organizations to increase affordability, access, and equity in clinical trial enrollment. To provide diverse perspectives on how these challenges are affecting cancer research as we emerge from the pandemic, we asked a panel of experienced clinical research leaders from both academic and community cancer centers to answer questions they felt most pressing about the business of conducting clinical research today and where they felt the field was moving in the near future.

Modeling the Cost-Effectiveness of Adjuvant Osimertinib for Patients with Resected EGFR-mutant Non-Small Cell Lung Cancer.

INTRODUCTION: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor osimertinib was recently approved for resected EGFR-mutant stages IB-IIIA non-small cell lung cancer due to improved disease-free survival (DFS) in this population compared with placebo. This study aimed to evaluate the cost-effectiveness (CE) of this strategy. MATERIALS AND METHODS: We constructed a Markov model using post-resection health state transitions with digitized DFS data from the ADAURA trial to compare cost and quality-adjusted life years (QALYs) of 3 years of adjuvant osimertinib versus placebo over a 10-year time horizon. An overall survival (OS) benefit of 5% was assumed. Costs and utility values were derived from Medicare reimbursement data and literature. A CE threshold of 3 times the gross domestic product per capita was used. Sensitivity analyses were performed. RESULTS: The incremental cost-effectiveness ratio for adjuvant osimertinib was $317 119 per QALY-gained versus placebo. Initial costs of osimertinib are higher in years 1-3. Costs due to progressive disease (PD) are higher in the placebo group through the first 6.5 years. Average pre-PD, post-PD, and total costs were $2388, $379 047, and $502 937, respectively, in the placebo group, and $505 775, $255 638, and $800 697, respectively, in the osimertinib group. Sensitivity analysis of OS gains reaches CE with an hazard ratio (HR) of 0.70-0.75 benefit of osimertinib over placebo. A 50% discount to osimertinib drug cost yielded an ICER of $115 419. CONCLUSIONS: Three-years of adjuvant osimertinib is CE if one is willing to pay $317 119 more per QALY-gained. Considerable OS benefit over placebo or other economic interventions will be needed to reach CE.

Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States.

Importance: The COVID-19 pandemic has had a distinct spatiotemporal pattern in the United States. Patients with cancer are at higher risk of severe complications from COVID-19, but it is not well known whether COVID-19 outcomes in this patient population were associated with geography. Objective: To quantify spatiotemporal variation in COVID-19 outcomes among patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients with a historical diagnosis of invasive malignant neoplasm and laboratory-confirmed SARS-CoV-2 infection between March and November 2020. Data were collected from cancer care delivery centers in the United States. Exposures: Patient residence was categorized into 9 US census divisions. Cancer center characteristics included academic or community classification, rural-urban continuum code (RUCC), and social vulnerability index. Main Outcomes and Measures: The primary outcome was 30-day all-cause mortality. The secondary composite outcome consisted of receipt of mechanical ventilation, intensive care unit admission, and all-cause death. Multilevel mixed-effects models estimated associations of center-level and census division-level exposures with outcomes after adjustment for patient-level risk factors and quantified variation in adjusted outcomes across centers, census divisions, and calendar time. Results: Data for 4749 patients (median [IQR] age, 66 [56-76] years; 2439 [51.4%] female individuals, 1079 [22.7%] non-Hispanic Black individuals, and 690 [14.5%] Hispanic individuals) were reported from 83 centers in the Northeast (1564 patients [32.9%]), Midwest (1638 [34.5%]), South (894 [18.8%]), and West (653 [13.8%]). After adjustment for patient characteristics, including month of COVID-19 diagnosis, estimated 30-day mortality rates ranged from 5.2% to 26.6% across centers. Patients from centers located in metropolitan areas with population less than 250 000 (RUCC 3) had lower odds of 30-day mortality compared with patients from centers in metropolitan areas with population at least 1 million (RUCC 1) (adjusted odds ratio [aOR], 0.31; 95% CI, 0.11-0.84). The type of center was not significantly associated with primary or secondary outcomes. There were no statistically significant differences in outcome rates across the 9 census divisions, but adjusted mortality rates significantly improved over time (eg, September to November vs March to May: aOR, 0.32; 95% CI, 0.17-0.58). Conclusions and Relevance: In this registry-based cohort study, significant differences in COVID-19 outcomes across US census divisions were not observed. However, substantial heterogeneity in COVID-19 outcomes across cancer care delivery centers was found. Attention to implementing standardized guidelines for the care of patients with cancer and COVID-19 could improve outcomes for these vulnerable patients.

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19.

BACKGROUND: Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking. OBJECTIVES: To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19. METHODS: Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap. RESULTS: From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission. CONCLUSIONS: Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Economic Impact of Next-Generation Sequencing Versus Single-Gene Testing to Detect Genomic Alterations in Metastatic Non-Small-Cell Lung Cancer Using a Decision Analytic Model.

PURPOSE: The aim of the current study was to assess the economic impact of using next-generation sequencing (NGS) versus single-gene testing strategies among patients with metastatic non-small-cell lung cancer (mNSCLC) from the perspective of the Centers for Medicare & Medicaid Services (CMS) and US commercial payers. METHODS: A decision analytic model considered patients who were newly diagnosed with mNSCLC who received programmed death ligand 1 and genomic alteration tests-EGFR, ALK, ROS1, BRAF, MET, HER2, RET, and NTRK1-using upfront NGS (all alterations tested simultaneously plus KRAS), sequential testing (sequence of single-gene tests), exclusionary testing (KRAS plus sequential testing), and hotspot panels (EGFR, ALK, ROS1, and BRAF tested simultaneously plus single-gene tests or NGS for MET, HER2, RET, and NTRK1). Model outcomes for each strategy were time-to-test results, the proportion of patients identified harboring alterations with or without US Food and Drug Administration-approved therapies, and total testing costs. A budget impact analysis assessed the economic effects of increasing the proportion of NGS-tested patients. RESULTS: In a hypothetical 1,000,000-member health plan, 2,066 Medicare-insured patients and 156 commercially insured patients were estimated to have mNSCLC and to be eligible for testing. Time-to-test results were 2.0 weeks for NGS and the hotspot panel, faster than exclusionary and sequential testing by 2.7 and 2.8 weeks, respectively. NGS was associated with cost savings for both CMS ($1,393,678; $1,530,869; and $2,140,795 less than exclusionary, sequential testing, and hotspot panels, respectively) and commercial payers ($3,809; $127,402; and $250,842 less than exclusionary, sequential testing, and hotspot panels, respectively). Increasing the proportion of NGS-tested patients translated into substantial cost savings for both CMS and commercial payers. CONCLUSION: Use of upfront NGS testing in patients with mNSCLC was associated with substantial cost savings and shorter time-to-test results for both CMS and commercial payers.