The cost of clinical trials is ever-increasing. In-silico trials rely on virtual populations and interventions simulated using patient-specific models and may offer a solution to lower these costs. We present the flow diverter performance assessment (FD-PASS) in-silico trial, which models the treatment of intracranial aneurysms in 164 virtual patients with 82 distinct anatomies with a flow-diverting stent, using computational fluid dynamics to quantify post-treatment flow reduction. The predicted FD-PASS flow-diversion success rates replicate the values previously reported in three clinical trials. The in-silico approach allows broader investigation of factors associated with insufficient flow reduction than feasible in a conventional trial. Our findings demonstrate that in-silico trials of endovascular medical devices can: (i) replicate findings of conventional clinical trials, and (ii) perform virtual experiments and sub-group analyses that are difficult or impossible in conventional trials to discover new insights on treatment failure, e.g. in the presence of side-branches or hypertension.
Although the cost of clinical trials is ever-increasing, in-silico trials, which rely on virtual populations and interventions simulated using patient-specificc 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 effcacy 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 insuficient flow reduction and increased stroke risk after flow diversion than would be feasible in a conventional trial. These ndings demonstrate for the rst time that in-silico trials of medical devices can (i) replicate ndings of conventional clinical trials and (ii) incorporate virtual experiments that are impossible in conventional trials.
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