{
  "abstract": "Background The posterior communicating artery (PComA) is among the most common intracranial aneurysm locations, but flow diverter (FD) treatment with the widely used pipeline embolization device (PED) remains an off-label treatment that is not well understood. PComA aneurysm flow diversion is complicated by the presence of fetal posterior circulation (FPC), which has an estimated prevalence of 4–29% and is more common in people of black (11.5%) than white (4.9%) race. We present the FD-PComA in-silico trial (IST) into FD treatment performance in PComA aneurysms. ISTs use computational modeling and simulation in cohorts of virtual patients to evaluate medical device performance.Methods We modeled FD treatment in 118 virtual patients with 59 distinct PComA aneurysm anatomies, using computational fluid dynamics to assess post-treatment outcome. Boundary conditions were prescribed to model the effects of non-fetal and FPC, allowing for comparison between these subgroups.Results FD-PComA predicted reduced treatment success in FPC patients, with an average aneurysm space and time-averaged velocity reduction of 67.8% for non-fetal patients and 46.5% for fetal patients (P <0.001). Space and time-averaged wall shear stress on the device surface was 29.2 Pa averaged across fetal patients and 23.5 Pa across non-fetal (P<0.05) patients, suggesting FD endothelialization may be hindered in FPC patients. Morphological variables, such as the size and shape of the aneurysm and PComA size, did not affect the treatment outcome.Conclusions FD-PComA had significantly lower treatment success rates in PComA aneurysm patients with FPC. We suggest that FPC patients should be treated with an alternative to single PED flow diversion.",
  "authors": [
    {
      "affiliations": [
        "Centre for Computational Imaging and Modelling in Medicine (CIMIM), University of Manchester, Manchester, UK",
        "EPSRC Centre for Doctoral Training in Fluid Dynamics, University of Leeds, Leeds, UK",
        "Department of Computer Science, School of Engineering, University of Manchester, Manchester, UK"
      ],
      "name": "Michael MacRaild"
    },
    {
      "affiliations": [
        "Centre for Computational Imaging and Modelling in Medicine (CIMIM), University of Manchester, Manchester, UK",
        "School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK"
      ],
      "name": "Ali Sarrami-Foroushani"
    },
    {
      "affiliations": [
        "School of Computing, University of Leeds, Leeds, UK"
      ],
      "name": "Shuang Song"
    },
    {
      "affiliations": [
        "School of Computing, University of Leeds, Leeds, UK"
      ],
      "name": "Qiongyao Liu"
    },
    {
      "affiliations": [
        "School of Computing, University of Leeds, Leeds, UK"
      ],
      "name": "Christopher Kelly"
    },
    {
      "affiliations": [
        "School of Computing, University of Leeds, Leeds, UK"
      ],
      "name": "Nishant Ravikumar"
    },
    {
      "affiliations": [
        "Interventional Neuroradiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK"
      ],
      "name": "Tufail Patankar"
    },
    {
      "affiliations": [
        "School of Computing, University of Leeds, Leeds, UK"
      ],
      "name": "Toni Lassila"
    },
    {
      "affiliations": [
        "School of Mechanical Engineering, University of Leeds, Leeds, UK"
      ],
      "name": "Zeike A Taylor"
    },
    {
      "affiliations": [
        "Centre for Computational Imaging and Modelling in Medicine (CIMIM), University of Manchester, Manchester, UK",
        "Department of Computer Science, School of Engineering, University of Manchester, Manchester, UK",
        "School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK",
        "Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium",
        "Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium"
      ],
      "name": "Alejandro F Frangi"
    }
  ],
  "title": "Off-label in-silico flow diverter performance assessment in posterior communicating artery aneurysms",
  "uid": "3b3e1618-201f-543f-a905-9ca2507bddb1"
}