Evidence map›Paper›PMID 34304318›Full record

ArticleThe international journal of cardiovascular imaging2022

Estimated pulse wave velocity (ePWV) as a potential gatekeeper for MRI-assessed PWV: a linear and deep neural network based approach in 2254 participants of the Netherlands Epidemiology of Obesity study.

Max J van Hout, Ilona A Dekkers, Ling Lin, Jos J Westenberg, Martin J Schalij, J Wouter Jukema, Ralph L Widya, Sebastiaan C Boone, Renée de Mutsert, Frits R Rosendaal and 2 more

Open access · hybridAbstract read
In one paragraph

Article in The international journal of cardiovascular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  9. Review
  10. Editor's choice to the January 2022 issue : Pulse wave velocity.The international journal of cardiovascular imaging · 2022
    Article
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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

12 authors at 1 institution in 1 country.

Max J van HoutDepartment of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. M.J.P.van_Hout@lumc.nl.
Ilona A DekkersDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Ling LinDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Jos J WestenbergDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Martin J SchalijDepartment of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
J Wouter JukemaDepartment of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Ralph L WidyaDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Sebastiaan C BooneDepartment of Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Renée de MutsertDepartment of Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Frits R RosendaalDepartment of Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Arthur J ScholteDepartment of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Hildo J LambDepartment of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Leiden University Medical Center · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pulse wave velocity (PWV) assessed by magnetic resonance imaging (MRI) is a prognostic marker for cardiovascular events. Prediction modelling could enable indirect PWV assessment based on clinical and anthropometric data. The aim was to calculate estimated-PWV (ePWV) based on clinical and anthropometric measures using linear ridge regression as well as a Deep Neural Network (DNN) and to determine the cut-off which provides optimal discriminative performance between lower and higher PWV values. In total 2254 participants from the Netherlands Epidemiology of Obesity study were included (age 45-65 years, 51% male). Both a basic and expanded prediction model were developed. PWV was estimated using linear ridge regression and DNN. External validation was performed in 114 participants (age 30-70 years, 54% female). Performance was compared between models and estimation accuracy was evaluated by ROC-curves. A cut-off for optimal discriminative performance was determined using Youden's index. The basic ridge regression model provided an adjusted R

Indexed as

Pulse Wave AnalysisVascular StiffnessAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNetherlandsNeural Networks, ComputerObesityPredictive Value of TestsMagnetic resonance imagingPrediction modellingPulse wave velocity

Identifiers

PMID34304318
PMCPMC8818644
OpenAlexW3183699290

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.