Evidence map›Paper›PMID 33097626›Full record

SynthesisJournal of neurointerventional surgery2021

Implementation of computer simulation to assess flow diversion treatment outcomes: systematic review and meta-analysis.

Mingzi Zhang, Simon Tupin, Hitomi Anzai, Yutaro Kohata, Masaaki Shojima, Kosuke Suzuki, Yoshihiro Okamoto, Katsuhiro Tanaka, Takanobu Yagi, Soichiro Fujimura and 1 more

Open access · hybridAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of neurointerventional surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Hemodynamics in Cerebral Aneurysms and Parent Arteries With Incompletely Expanded Flow Diverter Stents.International journal for numerical methods in biomedical engineering · 2025
    Article
  6. Patient-specific cerebral 3D vessel model reconstruction using deep learning.Medical & biological engineering & computing · 2024
    Article
  7. Article
  8. Experimental study using phantom models of cerebral aneurysms and 4D-DSA to measure blood flow on 3D-color-coded images.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 8 institutions in 2 countries.

Mingzi ZhangBiomedical Flow Dynamics Laboratory, Tohoku University Institute of Fluid Science, Sendai, Miyagi, Japan.ORCID http://orcid.org/0000-0002-0118-1160
Simon TupinBiomedical Flow Dynamics Laboratory, Tohoku University Institute of Fluid Science, Sendai, Miyagi, Japan.ORCID http://orcid.org/0000-0003-0982-8210
Hitomi AnzaiBiomedical Flow Dynamics Laboratory, Tohoku University Institute of Fluid Science, Sendai, Miyagi, Japan.
Yutaro KohataBiomedical Flow Dynamics Laboratory, Tohoku University Institute of Fluid Science, Sendai, Miyagi, Japan.ORCID http://orcid.org/0000-0002-4612-7440
Masaaki ShojimaDepartment of Neurosurgery, Saitama Medical University Saitama Medical Center, Kawagoe, Saitama, Japan.ORCID http://orcid.org/0000-0002-4388-151X
Kosuke SuzukiDepartment of Mechanical and Electrical Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan.
Yoshihiro OkamotoDivision of Medical Devices, National Institute of Health Sciences, Kawasaki, Kanagawa, Japan.
Katsuhiro TanakaDepartment of Neurosurgery, Mie Central Medical Center, Tsu, Mie, Japan.
Takanobu YagiCenter for Advanced Biomedical Sciences (TWIns), Waseda University, Shinjuku, Tokyo, Japan.
Soichiro FujimuraDepartment of Innovation for Medical Information Technology, Jikei University School of Medicine, Minato, Tokyo, Japan.
Makoto OhtaBiomedical Flow Dynamics Laboratory, Tohoku University Institute of Fluid Science, Sendai, Miyagi, Japan makoto.ohta@tohoku.ac.jp.ORCID http://orcid.org/0000-0001-8571-6400
Tohoku University · JPJikei University School of Medicine · JPMie Chuo Medical Center · JPNagoya Institute of Technology · JPNational Institute of Health Sciences · JPSocial Insurance Saitama Chuo Hospital · JPUniversité Claude Bernard Lyon 1 · FRWaseda University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite a decade of research into virtual stent deployment and the post-stenting aneurysmal hemodynamics, the hemodynamic factors which correlate with successful treatment remain inconclusive. We aimed to examine the differences in various post-treatment hemodynamic parameters between successfully and unsuccessfully treated cases, and to quantify the additional flow diversion achievable through stent compaction or insertion of a second stent.

methodsA systematic review and meta-analysis were performed on eligible studies published from 2000 to 2019. We first classified cases according to treatment success (aneurysm occlusion) and then calculated the pooled standardized mean differences (SMD) of each available parameter to examine their association with clinical outcomes. Any additional flow diversion arising from the two common strategies for improving the stent wire density was quantified by pooling the results of such studies.

resultsWe found that differences in the aneurysmal inflow rate (SMD -6.05, 95% CI -10.87 to -1.23, p=0.01) and energy loss (SMD -5.28, 95% CI -7.09 to -3.46, p<0.001) between the successfully and unsuccessfully treated groups were indicative of statistical significance, in contrast to wall shear stress (p=0.37), intra-aneurysmal average velocity (p=0.09), vortex core-line length (p=0.46), and shear rate (p=0.09). Compacting a single stent could achieve additional flow diversion comparable to that by dual-stent implantation.

conclusionsInflow rate and energy loss have shown promise as identifiers to discriminate between successful and unsuccessful treatment, pending future research into their diagnostic performance to establish optimal cut-off values.

Indexed as

Computer SimulationBlood Flow VelocityHemodynamicsHumansIntracranial AneurysmSelf Expandable Metallic StentsStress, MechanicalTreatment Outcomeaneurysmblood flowflow diverter

Identifiers

PMID33097626
PMCPMC7848055
OpenAlexW3094560680

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.