Evidence map›Paper›PMID 29274686›Full record

ArticleJournal of biomechanics2018

Fluid-structure interaction models based on patient-specific IVUS at baseline and follow-up for prediction of coronary plaque progression by morphological and biomechanical factors: A preliminary study.

Liang Wang, Dalin Tang, Akiko Maehara, Zheyang Wu, Chun Yang, David Muccigrosso, Jie Zheng, Richard Bach, Kristen L Billiar, Gary S Mintz

Open access · greenAbstract read
In one paragraph

Article in Journal of biomechanics, 2018. 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
2.5field-weighted citation impact, top 11% 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, 22 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

10 authors at 5 institutions in 2 countries.

Liang WangSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, China; Mathematical Sciences Department, Worcester Polytechnic Institute, MA, USA.
Dalin TangSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, China; Mathematical Sciences Department, Worcester Polytechnic Institute, MA, USA. Electronic address: dtang@wpi.edu.
Akiko MaeharaColumbia University, The Cardiovascular Research Foundation, NY, NY, USA.
Zheyang WuMathematical Sciences Department, Worcester Polytechnic Institute, MA, USA.
Chun YangMathematical Sciences Department, Worcester Polytechnic Institute, MA, USA.
David MuccigrossoMallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
Jie ZhengMallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
Richard BachCardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA.
Kristen L BilliarDepartment of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
Gary S MintzColumbia University, The Cardiovascular Research Foundation, NY, NY, USA.
Worcester Polytechnic Institute · USMallinckrodt (United States) · USCardiovascular Research Foundation · USColumbia University · USWashington University in St. Louis · US

Funding

In Vivo IVUS Image-Based Modeling for Human Coronary Plaque AssessmentR01EB004759 · NIBIB · WORCESTER POLYTECHNIC INSTITUTE · PI TANG, DALIN, ZHENG, JIE · 2004 to 2013
$2.4M
NIBIB NIH HHS R01 EB004759
6 · The paper itself

Abstract

Plaque morphology and biomechanics are believed to be closely associated with plaque progression. In this paper, we test the hypothesis that integrating morphological and biomechanical risk factors would result in better predictive power for plaque progression prediction. A sample size of 374 intravascular ultrasound (IVUS) slices was obtained from 9 patients with IVUS follow-up data. 3D fluid-structure interaction models were constructed to obtain both structural stress/strain and fluid biomechanical conditions. Data for eight morphological and biomechanical risk factors were extracted for each slice. Plaque area increase (PAI) and wall thickness increase (WTI) were chosen as two measures for plaque progression. Progression measure and risk factors were fed to generalized linear mixed models and linear mixed-effect models to perform prediction and correlation analysis, respectively. All combinations of eight risk factors were exhausted to identify the optimal predictor(s) with highest prediction accuracy defined as sum of sensitivity and specificity. When using a single risk factor, plaque wall stress (PWS) at baseline was the best predictor for plaque progression (PAI and WTI). The optimal predictor among all possible combinations for PAI was PWS + PWSn + Lipid percent + Min cap thickness + Plaque Area (PA) + Plaque Burden (PB) (prediction accuracy = 1.5928) while Wall Thickness (WT) + Plaque Wall Strain (PWSn) + Plaque Area (PA) was the best for WTI (1.2589). This indicated that PAI was a more predictable measure than WTI. The combination including both morphological and biomechanical parameters had improved prediction accuracy, compared to predictions using only morphological features.

Indexed as

Disease ProgressionMechanical PhenomenaModels, BiologicalPatient-Specific ModelingBiomechanical PhenomenaCoronary Artery DiseaseFemaleFollow-Up StudiesHumansMaleMiddle AgedPlaque, AtheroscleroticRisk FactorsStress, MechanicalUltrasonographyCoronaryFluid–structure interactionFollow-up studyIVUSPlaque progression

Identifiers

PMID29274686
PMCPMC5783767
OpenAlexW2774002517

What OpenQuestion holds

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Registered trials

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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.