Cost-Utility Analysis of Prenatal Diagnosis of Congenital Cardiac Diseases using Deep Learning

Abstract

Abstract Background Deep learning (DL) is a new technology that can assist prenatal ultrasound (US) in the detection of congenital heart disease (CHD) at the prenatal stage. Hence, an economic-epidemiologic evaluation (aka Cost-Utility Analysis) is required to assist policymakers in deciding whether to adopt the new technology. Methods The cost-utility ratios (CUR) were calculated for the current provision of US plus pulse oximetry (POX),and with DL-assisted ultrasound (DL-US) plus POX by means of a spreadsheet model integrating demographic, economic epidemiological, health service utilization, screening performance, survival and lifetime quality of life data based on the standard formula: CUR = (Intervention Costs - Treatment Savings)/ Quality Adjusted Life Years (QALY) gained US screening data were based on data from real-world operational routine reports (as opposed to research studies). The DL screening cost of 145 USD was based on Israeli US costs plus 20.54 USD for reading and recording screens. Results The addition of DL-US, which is associated with increased sensitivity (95% vs 58.1%), resulted in far fewer undiagnosed infants (16 vs 102 [or 2.9% vs 15.4% of the 560 and 659 births, respectively). Adoption of DL-US will add 1,204 QALYs. The increased screening costs of DL-US (23.2 million USD) are largely offset by decreased treatment costs (20.8 million NIS). Therefore, the new DL-US technology is considered “very cost-effective”, costing only 6,441 NIS per QALY. For most performance combinations (sensitivity > 80%, specificity >90%), the adoption of DL-US is either cost effective or very cost- effective. For specificities greater than 98% (with sensitivities above 94%),DL-US (& POX) is said to “dominate” US (& POX) by providing more QALYs at a lower cost. Conclusion Our exploratory CUA calculations indicate the feasibility of DL-US as being at least cost-effective.