Economic evaluations of exome sequencing for rare diseases within pediatric care

Abstract

In the last decade, the landscape of genetic testing has changed enormously. Genetic sequencing technologies are increasingly used for the diagnosis of rare genetic disorders. Exome sequencing (ES) is emerging as powerful diagnostic tool in identifying the genetic etiology of a rare disease, especially within pediatrics. There is however still an unmet need to better understand the economic impact of ES implementation in routine care, also including other possible benefits of ES, such as improvements in clinical decision-making, and reducing the burden to families. A cohort of patients in which these economic outcomes and clinical benefits might be best captured are neonates admitted to neonatal intensive care units (NICUs). For these neonates, it is expected that genetic test results may impact clinical decision-making and enable personalized medicine. Yet, genetic testing using ES has not reached widespread implementation in this setting. This is mainly due to the need of NICUs to have a fast ES result, requiring infrastructure on Dutch genetic laboratories that have only been set up recently. Since there are also still uncertainties regarding effect/outcome measures to be taken into account for economic evaluation when implementing novel genetic tests, this thesis assessed the economic impact of implementing ES in routine care for children admitted to the NICU. Overall, this thesis has shown that ES has great potential (both clinical and economic) to become part of routine genetic testing for critically ill neonates admitted to the neonatal intensive care unit. Especially if ES is implemented as first-tier diagnostic test for all patients with congenital anomalies, it will not lead to a significant increase in healthcare costs. However, the implementation of ES did not await the outcomes of these studies. With the technological revolution in the field of medical genetics still ongoing, future cost-effectiveness analyses should be performed earlier in the process towards implementation. Based on these early health economic evaluations, decisions regarding implementation need to be made, preferably using valid and transparent decision analytic models outlining future costs and effects. Thereafter, these models should be updated as new real-world data becomes available, and frameworks should be developed in order to enhance further implementation and even de-implementation of patient pathways that are ultimately deemed not cost-effective. This includes adding a layer of uncertainty (uncertainty about data maturity) to future decision-making. The use of long-term data from other studies and high-quality data from current practice can remove the uncertainty and early insight in the potential added value is in this case essential to further steer and guide the future of innovative diagnostic modalities.