Evidence map›Paper›PMID 37931581›Full record

ArticleComputer methods and programs in biomedicine2024

Simulation of coronary capillary transit time based on full vascular model of the heart.

Haifeng Wang, Lei Fan, Jenny S Choy, Ghassan S Kassab, Lik Chuan Lee

Open access · greenAbstract read
In one paragraph

Article in Computer methods and programs in biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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  4. Article
  5. Article
  6. Preconditioning with selective autoretroperfusion:Frontiers in bioengineering and biotechnology · 2024
    Article
  7. Review
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

5 authors at 3 institutions in 1 country.

Haifeng WangDepartment of Mechanical Engineering, Michigan State University, East Lansing, MI, USA. Electronic address: wangh125@msu.edu.
Lei FanThe Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Jenny S ChoyCalifornia Medical Innovations Institute, San Diego, California, USA.
Ghassan S KassabCalifornia Medical Innovations Institute, San Diego, California, USA.
Lik Chuan LeeDepartment of Mechanical Engineering, Michigan State University, East Lansing, MI, USA.
California Medical Innovations Institute · USMichigan State University · USMarquette University · US

Funding

Mechanisms of coronary flow heterogeneity: Implications for coronary sinus occlusion therapyR01HL160997 · NHLBI · CALIFORNIA MEDICAL INNOVATIONS INSTITUTE · PI KASSAB, GHASSAN S, LEE, LIK CHUAN · 2022 to 2025
$2.7M
Data-driven optimization of therapy for heart failureR01HL163977 · NHLBI · UNIVERSITY OF KENTUCKY · PI CAMPBELL, KENNETH S, LEE, LIK CHUAN · 2022 to 2025
$2.4M
NHLBI NIH HHS R01 HL160997NHLBI NIH HHS R01 HL163977
6 · The paper itself

Abstract

Capillary transit time (CTT) is a fundamental determinant of gas exchange between blood and tissues in the heart and other organs. Despite advances in experimental techniques, it remains difficult to measure coronary CTT in vivo. Here, we developed a novel computational framework that couples coronary microcirculation with cardiac mechanics in a closed-loop system that enables prediction of hemodynamics in the entire coronary network, including arteries, veins, and capillaries. We also developed a novel "particle-tracking" approach for computing CTT where "virtual tracers" are individually tracked as they traverse the capillary network. Model predictions compare well with blood pressure and flow rate distributions in the arterial network reported in previous studies. Model predictions of transit times in the capillaries (1.21 ± 1.5 s) and entire coronary network (11.8 ± 1.8 s) also agree with measurements. We show that, with increasing coronary artery stenosis (as quantified by fractional flow reserve, FFR), intravascular pressure and flow rate downstream are reduced but remain non-stationary even at 100 % stenosis because some flow (∼3 %) is redistributed from the non-occluded to the occluded territories. Importantly, the model predicts that occlusion of a large artery results in higher CTT. For moderate stenosis (FFR > 0.6), the increase in CTT (from 1.21 s without stenosis to 2.23 s at FFR=0.6) is caused by a decrease in capillary flow rate. In severe stenosis (FFR = 0.1), the increase in CTT to 14.2 s is due to both a decrease in flow rate and an increase in path length taken by "virtual tracers" in the capillary network.

Indexed as

Coronary StenosisFractional Flow Reserve, MyocardialCapillariesConstriction, PathologicCoronary AngiographyCoronary VesselsHumansPredictive Value of TestsSeverity of Illness IndexCapillary transit timeCoronary arterial-capillary-venous networkCoronary artery stenosisCoronary microcirculationLumped model

Identifiers

PMID37931581
PMCPMC10872892
OpenAlexW4388068393

What OpenQuestion holds

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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.