Evidence map›Paper›PMID 33933080›Full record

ArticleBiomedical engineering online2021

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease.

Dietmar Frey, Michelle Livne, Heiko Leppin, Ela M Akay, Orhun U Aydin, Jonas Behland, Jan Sobesky, Peter Vajkoczy, Vince I Madai

Open access · goldAbstract read
In one paragraph

Article in Biomedical engineering online, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 17 citations in OpenAlex.

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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

9 authors at 3 institutions in 2 countries.

Dietmar FreyCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany. dietmar.frey@charite.de.ORCID http://orcid.org/0000-0001-5407-2331
Michelle LivneCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Heiko LeppinCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Ela M AkayCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Orhun U AydinCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Jonas BehlandCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Jan SobeskyJohanna Etienne Hospital Neuss, Berlin, Germany.
Peter VajkoczyDepartment of Neurosurgery, Charité University Medicine Berlin, Berlin, Germany.
Vince I MadaiCharite Lab for Artificial Intelligence in Medicine, Department of Neurosurgery, Charité University Medicine Berlin, Chariteplatz 1, 10115, Berlin, Germany.
Charité - Universitätsmedizin Berlin · DEBirmingham City University · GBJohanna-Etienne-Krankenhaus · DE

Funding

Bundesministerium für Bildung und Forschung (DE) 031B0154H2020 Societal Challenges 777107
6 · The paper itself

Abstract

backgroundCerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available.

resultsWe present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modeling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low-to-moderate sensitivity to detect perfusion changes in classic perfusion imaging.

conclusionsThe presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete.

Indexed as

Cerebrovascular DisordersHemodynamicsPrecision MedicineAgedCerebrovascular CirculationComputer SimulationFemaleHumansMaleMiddle AgedAnnotationCerebrovascular diseaseHemodynamicsMachine learningMedical softwareMR imagingPrecision medicineSegmentationSimulation

Identifiers

PMID33933080
PMCPMC8088619
OpenAlexW3159072128

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.