Evidence map›Paper›PMID 34709411›Full record

ReviewClinical neuroradiology2022

Preprocedural Imaging : A Review of Different Radiological Factors Affecting the Outcome of Thrombectomy.

Mingxue Jing, Joshua Y P Yeo, Staffan Holmin, Tommy Andersson, Fabian Arnberg, Paul Bhogal, Cunli Yang, Anil Gopinathan, Tian Ming Tu, Benjamin Yong Qiang Tan and 6 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

16 authors.

Mingxue JingDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Joshua Y P YeoDepartment of Medicine, National University Health System, Singapore, Singapore.
Staffan HolminDepartment of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, 171 76, Stockholm, Sweden.
Tommy AnderssonDepartment of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, 171 76, Stockholm, Sweden.
Fabian ArnbergDepartment of Clinical Neuroscience, Karolinska Institutet and Department of Neuroradiology, Karolinska University Hospital, 171 76, Stockholm, Sweden.
Paul BhogalDepartment of Neuroradiology, St.Bartholomew's and the Royal London Hospital, London, UK.
Cunli YangDepartment of Diagnostic Imaging, National University Health System, Singapore, Singapore.
Anil GopinathanDepartment of Diagnostic Imaging, National University Health System, Singapore, Singapore.
Tian Ming TuDepartment of Neurology, National Neuroscience Institute, Singapore, Singapore.
Benjamin Yong Qiang TanDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Ching Hui SiaNational University Heart Centre, National University Health System, Singapore, Singapore.
Hock Luen TeohDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Prakash R PaliwalDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Bernard P L ChanDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Vijay SharmaDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore.
Leonard L L YeoDivision of Neurology, Department of Medicine, National University Health System, 1 E Kent Ridge Road, 119228, Singapore, Singapore. leonardyeoll@gmail.com.

Funding

Egészségügyi Tudományos Tanács (HU) NMRC/FLWSHP/043/2017
6 · The paper itself

Abstract

backgroundEndovascular treatment (EVT) has strong evidence for its effectiveness in treatment of acute ischemic stroke (AIS); however, up to half of the patients who undergo EVT still do not have good functional outcomes. Various prethrombectomy radiological factors have been shown to be associated with good clinical outcomes and may be the key to better functional outcomes, reduced complications, and reduced mortality. In this paper, we reviewed the current literature on these imaging parameters so they can be employed to better estimate the probability of procedural success, therefore allowing for more effective preprocedural planning of EVT strategies. We reviewed articles in the literature related to imaging factors which have been shown to be associated with EVT success. The factors which are reviewed in this paper included: anatomical factors such as 1) the type of aortic arch and its characteristics, 2) the characteristics of the thrombus such as length, clot burden, permeability, location, 3) the middle cerebral artery features including the tortuosity and underlying intracranial stenosis, 4) perfusion scans estimating the volume of infarct and the penumbra and 5) the effect of collaterals on the procedure. The prognostic effect of each factor on the successful outcome of EVT is described. The identification of preprocedural thrombectomy imaging factors can help to improve the chances of recanalization, functional outcomes, and mortality. It allows the interventionist to make time-sensitive decisions in the treatment of acute ischemic stroke.

Indexed as

Brain IschemiaEndovascular ProceduresIschemic StrokeStrokeHumansThrombectomyTreatment OutcomeAcuteCTEndovascular treatmentIschemic strokeMRI

Identifiers

What OpenQuestion holds

Textmetadata
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.