In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials

Abstract

Although the cost of clinical trials is ever-increasing, in-silico trials, which rely on virtual populations and interventions simulated using patient-specific models, may offer a solution to contain these costs. However, in-silico trial endpoints need to be compared to those available from conventional clinical trials to ensure that the predictions of safety or efficacy from the in-silico approach are valid. Here, we present the flow diverter performance assessment (FDPASS) in-silico trial, which modelled the treatment of intracranial aneurysms in 82 virtual patients with a flow-diverting stent, using computational fluid dynamics (CFD) to quantify post-treatment flow reduction in the aneurysm sac. The predicted FD-PASS flow-diversion success rate replicated the values previously reported in three reference clinical trials. The in-silico approach allowed broader investigation of factors associated with insufficient flow reduction and increased stroke risk after flow diversion than would be feasible in a conventional trial. These findings demonstrate for the first time that in-silico trials of medical devices can (i) replicate findings of conventional clinical trials and (ii) incorporate virtual experiments that are impossible in conventional trials.