Impact on costs and outcomes of multi-gene panel testing for advanced solid malignancies: a cost-consequence analysis using linked administrative data.

Background: To date, economic analyses of tissue-based next generation sequencing genomic profiling (NGS) for advanced solid tumors have typically required models with assumptions, with little real-world evidence on overall survival (OS), clinical trial enrollment or end-of-life quality of care. Methods: Cost consequence analysis of NGS testing (555 or 161-gene panels) for advanced solid tumors through the OCTANE clinical trial (NCT02906943). This is a longitudinal, propensity score-matched retrospective cohort study in Ontario, Canada using linked administrative data. Patients enrolled in OCTANE at Princess Margaret Cancer Centre from August 2016 until March 2019 were matched with contemporary patients without large gene panel testing from across Ontario not enrolled in OCTANE. Patients were matched according to 19 patient, disease and treatment variables. Full 2-year follow-up data was available. Sensitivity analyses considered alternative matched cohorts. Main Outcomes were mean per capita costs (2019 Canadian dollars) from a public payer's perspective, OS, clinical trial enrollment and end-of-life quality metrics. Findings: There were 782 OCTANE patients with 782 matched controls. Variables were balanced after matching (standardized difference <0.10). There were higher mean health-care costs with OCTANE ($79,702 vs. $59,550), mainly due to outpatient and specialist visits. Publicly funded drug costs were less with OCTANE ($20,015 vs. $24,465). OCTANE enrollment was not associated with improved OS (restricted mean survival time [standard error]: 1.50 (±0.03) vs. 1.44 (±0.03) years, log-rank p = 0.153), varying by tumor type. In five tumor types with ≥35 OCTANE patients, OS was similar in three (breast, colon, uterus, all p > 0.40), and greater in two (ovary, biliary, both p < 0.05). OCTANE was associated with greater clinical trial enrollment (25.4% vs. 9.5%, p < 0.001) and better end-of-life quality due to less death in hospital (10.2% vs. 16.4%, p = 0.003). Results were robust in sensitivity analysis. Interpretation: We found an increase in healthcare costs associated with multi-gene panel testing for advanced cancer treatment. The impact on OS was not significant, but varied across tumor types. OCTANE was associated with greater trial enrollment, lower publicly funded drug costs and fewer in-hospital deaths suggesting important considerations in determining the value of NGS panel testing for advanced cancers. Funding: T.P H holds a research grant provided by the Ontario Institute for Cancer Research through funding provided by the Government of Ontario (#IA-035 and P.HSR.158) and through funding of the Canadian Network for Learning Healthcare Systems and Cost-Effective 'Omics Innovation (CLEO) via Genome Canada (G05CHS).

Undisclosed financial conflicts of interest among authors of American Society of Clinical Oncology clinical practice guidelines.

BACKGROUND: Clinical practice guidelines (CPGs) are crucial to the practice of evidence-based medicine. Declared author financial conflicts of interest (FCOIs) are common in CPGs and have been associated with endorsement of treatment. Less is known about undeclared FCOIs. METHODS: The American Society of Clinical Oncology (ASCO) website was searched to identify all CPGs for systemic therapy published between August 2013 and June 2018. Data on self-reported author FCOIs and funding sources were extracted. The Open Payments database was then searched to identify compensation to CPG authors. Concordance between declared and undeclared but verified FCOIs was assessed with Cohen's κ. RESULTS: For 26 CPGs, 314 nonduplicate authors were identified; 184 of these authors (59%) disclosed FCOIs. Among the remaining 130 authors, data in Open Payments were unavailable for 71 authors (non-US residents or authors affiliated with a nonprofit organization). Among the 59 authors who declared no FCOIs and for whom Open Payments data were available, 55 (93%) had received payment from industry. The κ value for agreement between disclosed and verified FCOIs was 0.092. Among the 243 authors with FCOIs verifiable via Open Payments, 239 (98%) received payment from industry. Thirty-four authors (62%) received more than $1000 in nonresearch funding, and 19 (35%) received more than $5000. Among the 52 first and last authors, 44 (85%) received payment from industry; 14 of these payments (32%) were not declared. CONCLUSIONS: FCOIs among authors of ASCO CPGs are common and are not disclosed by a substantial proportion of authors with Open Payments data. Improved transparency of FCOIs should become standard practice among CPG authors. Professional societies and journal editors need to create a mechanism to verify self-reported FCOIs.

The Impact of Big Data Research on Practice, Policy, and Cancer Care.

The concept of "big data" research-the aggregation and analysis of biologic, clinical, administrative, and other data sources to drive new advances in biomedical knowledge-has been embraced by the cancer research enterprise. Although much of the conversation has concentrated on the amalgamation of basic biologic data (e.g., genomics, metabolomics, tumor tissue), new opportunities to extend potential contributions of big data to clinical practice and policy abound. This article examines these opportunities through discussion of three major data sources: aggregated clinical trial data, administrative data (including insurance claims data), and data from electronic health records. We will discuss the benefits of data use to answer key oncology practice and policy research questions, along with limitations inherent in these complex data sources. Finally, the article will discuss overarching themes across data types and offer next steps for the research, practice, and policy communities. The use of multiple sources of big data has the promise of improving knowledge and providing more accurate data for clinicians and policy decision makers. In the future, optimization of machine learning may allow for current limitations of big data analyses to be attenuated, thereby resulting in improved patient care and outcomes.

Mindfulness-Based Laboratory Reduction: Reducing Utilization Through Trainee-Led Daily 'Time Outs'.

BACKGROUND: Overuse of laboratory investigations is widely prevalent in hospitalized patients, leads to discomfort, and increases direct and indirect costs. OBJECTIVE: We implemented a simple, inexpensive, mindfulness strategy on our inpatient medical clinical teaching unit to reduce unnecessary laboratory orders through education, a forcing function, and daily structured laboratory "time outs." METHODS: On a 26-bed unit in an academic hospital center, the per-period laboratory costs per patient were compared pre- and postintervention using segmented regression analysis of an interrupted time series. RESULTS: The average cost per admitted patient decreased from $117 to $66, with an estimated savings of $50,657 over 985 admissions. After adjusting for fiscal period and the presence of our intervention, there was a significant reduction in the per-patient number of total tests, complete blood counts, and electrolyte panels performed (P <.001 for all level and time trend changes). CONCLUSION: This trainee-designed and -led intervention, centered around structured, mindfulness-based laboratory test ordering, was successful at decreasing the overuse of common daily blood work in hospitalized patients.