Transcatheter aortic valve implantation versus surgical aortic valve replacement in severe aortic stenosis patients at low surgical mortality risk: a cost-effectiveness analysis in Belgium.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) with the SAPIEN 3 device has recently shown significant clinical benefits, compared to surgical aortic valve replacement (SAVR), in patients at low risk for surgical mortality (PARTNER 3 trial, NCT02675114). Currently in Belgium, TAVI use is restricted to high-risk or inoperable patients with severe symptomatic aortic stenosis (sSAS). This cost-utility analysis aimed to assess whether TAVI with SAPIEN 3 could lead to potential cost-savings compared with SAVR, in the low-risk sSAS population in Belgium. METHODS: A previously published, two-stage, Markov-based cost-utility model was used. Clinical outcomes were captured using data from PARTNER 3 and the model was adapted for the Belgian context using cost data from the perspective of the Belgian National Healthcare System, indexed to 2022. A lifetime horizon was chosen. The model outputs included changes in direct healthcare costs, survival and health-related quality of life using TAVI versus SAVR. RESULTS: TAVI with SAPIEN 3 provides meaningful clinical and cost benefits over SAVR, in terms of an increase in quality-adjusted life years (QALYs) of 0.94 and cost-saving of €3 013 per patient. While initial procedure costs were higher for TAVI compared with SAVR, costs related to rehabilitation, disabling stroke, treated atrial fibrillation, and rehospitalization were lower. The cost-effectiveness of TAVI over SAVR remained robust in sensitivity analyses. CONCLUSION: TAVI with SAPIEN 3 may offer a meaningful alternative intervention to SAVR in Belgian low-risk patients with sSAS, showing both clinical benefits and cost savings associated with post-procedure patient management.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

The number of markers measured in both flow and mass cytometry keeps increasing steadily. Although this provides a wealth of information, it becomes infeasible to analyze these datasets manually. When using 2D scatter plots, the number of possible plots increases exponentially with the number of markers and therefore, relevant information that is present in the data might be missed. In this article, we introduce a new visualization technique, called FlowSOM, which analyzes Flow or mass cytometry data using a Self-Organizing Map. Using a two-level clustering and star charts, our algorithm helps to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. R code is available at https://github.com/SofieVG/FlowSOM and will be made available at Bioconductor.