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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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.

How common is spontaneous thrombosis (ST) formation in intracranial aneurysms (IAs); and how ST differs in normo- and hypertensive conditions? We address the former question by a thorough analysis of published datasets to identify the incidence of ST across different patients and demographics. Based on the statistical ST incidence in large and giant (LG) aneurysms (>10 mm), we perform an in-silico observational study in 89 virtual patients to calibrate two trigger thresholds, residence time (RT) and shear rate (SR). We then address the second question by using a calibrated thrombosis model to study the effects of hypertension on thrombus formation. According to our statistical analysis, the clinical ST (partial or complete) incidence for LG IAs is 40.67% (85/290) ±5.59%, with 90% confidence. The accuracy of our calibrated model in predicting ST is 75.8% by comparison with 62 documented clinical cases. Our model predicts a lower ST incidence in hypertensive patients due to the smaller maximum RT in the aneurysm sac caused by hypertension. This study not only collates ST literature and improves our clotting model by providing more accurate threshold parameters, but also reveals that ST may be less common in hypertensive patients.

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first in silico observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time and shear rate, thus addressing the second question. We then address the third question by utilizing this calibrated model to provide new insight into the effects of hypertension on spontaneous thrombosis. We demonstrate how a mechanistic thrombosis model calibrated on an intracranial aneurysm cohort can help estimate spontaneous thrombosis prevalence in a broader aneurysm population. This study is enabled through a fully automatic multi-scale modeling pipeline. We use the clinical spontaneous thrombosis data as an indirect population-level validation of a complex computational modeling framework. Furthermore, our framework allows exploration of the influence of hypertension in spontaneous thrombosis. This lays the foundation for in silico clinical trials of cerebrovascular devices in high-risk populations, e.g., assessing the performance of flow diverters in aneurysms for hypertensive patients.

How prevalent is spontaneous thrombosis (ST) in intracranial aneurysms (IAs) for an all-size pop- ulation? How can we calibrate computational models of thrombosis based on published data from size-specific aneurysms cohorts? How does ST differ in normo- and hypertensive subjects? We ad- dress the first question by a thorough analysis of published that provide ST rates across different patient demographics and aneurysm characteristics. This analysis provides data for a subgroup of the gen- eral population, viz. large and giant aneurysms (>10 mm). Based on these observed ST rates, our novel computational modelling platform enables the first in-silico observational study of ST prevalence across a broader set of IA phenotypes. We generate 109 virtual patients and use a novel approach to calibrate two trigger thresholds: residence time (RT) and shear rate (SR), thus addressing the second question. We then address the third question by utilising this calibrated thrombosis model to provide new insights on the effects of hypertension on ST. We demonstrate how a mechanistic ST model calibrated on a reduced IA cohort can help esti- mate ST prevalence in a broader IA population. This study was enabled through a comprehensive and fully automatic multi-scale modeling pipeline. We use an emerging property, viz. ST, as an indirect population-level validation of a complex computational modeling framework. Furthermore, our frame- work allows exploration of the influence of hypertension in ST. This lays the foundation for in-silico clinical trials of cerebrovascular devices in high-risk populations, e.g. assessing the performance of flow diverters in cerebral aneurysms for hypertensive patients.