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Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00

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Pjotr Hilhorst (Eindhoven University of Technology)
Wouter Huberts (Eindhoven University of Technology/Maastricht University)
Rajarajeswari Ganesan (Eindhoven University of Technology)
Pim Tonino (Catharina Hospital Eindhoven)
Frans van de Vosse (Eindhoven University of Technology)

Abstract:
In Silico clinical trials have great potential for replacing clinical trials. In this study, we aim to demonstrate the feasibility of conducting in Silico clinical trials by generating virtual patients and reproducing a clinical trial in which the clinical benefit of fractional flow reserve (FFR) measurements was demonstrated (i.e., the FAME study [1]) for patients suffering from coronary artery disease. Here, we will present the strategy we envision to demonstrate the clinical benefit of the FFR using in Silico trials only. In addition, we will present preliminary results regarding model development. A one-dimensional pulse wave propagation model (PWPM) that is capable of computing patient-specific FFRs has been developed. Sensitivity analysis will be conducted to prioritize parameters for model personalization. Geometric information will be extracted from angiograms, whereas patient-specific parameters will be estimated by using a machine-learning model that is trained using angiograms, demographics, and pressure losses across stenoses. The latter will be based on FFR measurements or 3D computational fluid dynamic simulations. A synthetically generated training set will be used to assure a large enough dataset and sufficient coverage of the heterogeneity within the population. Secondly, the parameters can be varied to generate virtual patients. In the future, the model output will be transformed into a clinically relevant output (i.e., mortality and morbidity) through a transfer function. Furthermore, the approach will be evaluated on an independent set of real clinical trial data. The results showed that the PWPM can accurately model coronary pathophysiology. The computed FFR values were plausible compared to clinical findings, which typically show an FFR below 0.8 around a stenosis severity of ≥ 50/60% [2]. Overall, it could be concluded that this model is ready for the next step in our devised methodology and can be used as a virtual cohort generator to recreate the FAME1 study in Silico.