Dynamic Simulation Modeling to Analyze the Impact of Whole Genome Sequencing National Implementation Scenarios in Lung cancer on Time-to-Treatment, Costs and Patient Demand

Abstract

Background: Although Whole Genome Sequencing (WGS) is increasingly proposed to unravel molecular origins of advanced cancers, it is less clear if and how WGS should be routinely offered in the health service. The objective of this study is to investigate how the cost per patient and time-to-treatment is affected if WGS were implemented in the national health system and how these outcomes differ among subgroups of patients with lung cancer. This first-ever study used health systems simulation modeling to analyze implementation scenarios ensuring sustainable access to cancer treatment. Methods: A base case and three scenarios (varying stage of disease and hospitals offering WGS) the optimal placement of WGS in the diagnostic pathway was simulated using a dynamic simulation model. The model simulated lung cancer patients undergoing molecular diagnostic procedures in one or multiple hospitals. The model also included patient and healthcare provider heterogeneity as well as referral patterns of lung cancer (LC) patients using patient-level data obtained from the Netherlands Cancer Registry. Model outcomes were the time-to-treatment, total diagnostic cost, and the demand for WGS sequencing capacity including the expertise of a molecular tumor board. Results: The time-to-treatment ranged between 20-46 days for all four scenarios considered. The cost of molecular diagnostic testing per patient ranged from Euro 621 in the base case to Euro 1930 in the scenario where all LC patients (stage I-IV) receive upfront WGS. Compared to the base case, upfront testing using WGS in all LC patients led to a 33% reduction in the time-to-treatment, a 210% increase in the cost per patient and a six-fold increase in total diagnostic costs. Conclusions: This first-ever study investigating implementation scenarios demonstrated that upfront WGS for all lung cancer patients can reduce the time to treatment yet at a higher cost. However, upfront WGS also reduces diagnostic pathway complexity, which may improve care planning and treatment efficiency. The model is versatile in its approach to study the impact of price discounts or the amount of actionable targets tested for and further analysis showed discounts on consumables up to 50% imply WGS would the preferred strategy.