Evidence map›Paper›PMID 39515268›Full record

ArticleComputers in biology and medicine2025

Biomechanical stress analysis of Type-A aortic dissection at pre-dissection, post-dissection, and post-repair states.

Christina Sun, Tongran Qin, Asanish Kalyanasundaram, John Elefteriades, Wei Sun, Liang Liang

Abstract read
In one paragraph

Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

6 authors.

Christina SunSutra Medical Inc, Lake Forest, CA, USA.
Tongran QinSutra Medical Inc, Lake Forest, CA, USA.
Asanish KalyanasundaramAortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA.
John ElefteriadesAortic Institute at Yale-New Haven Hospital, Yale University School of Medicine, New Haven, CT, USA.
Wei SunSutra Medical Inc, Lake Forest, CA, USA.
Liang LiangDepartment of Computer Science, University of Miami, Coral Gables, FL, USA. Electronic address: liang@cs.miami.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Machine learning-based biomechanical analysis for thoracic aortic aneurysm rupture risk assessmentR01HL158829 · NHLBI · UNIVERSITY OF MIAMI CORAL GABLES · PI Liang Liang · 2022 to 2026
$2.6M
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS R01 HL158829
6 · The paper itself

Abstract

Acute type A aortic dissection remains a deadly and elusive condition, with risk factors such as hypertension, bicuspid aortic valves, and genetic predispositions. As existing guidelines for surgical intervention based solely on aneurysm diameter face scrutiny, there is a growing need to consider other predictors and parameters, including wall stress, in assessing dissection risk. Through our research, we aim to elucidate the biomechanical underpinnings of aortic dissection and provide valuable insights into its prediction and prevention. We applied finite element analysis (FEA) to assess stress distribution on a rare dataset comprising computed tomography (CT) images obtained from eight patients at three stages of aortic dissection: pre-dissection (preD), post-dissection (postD), and post-repair (postR). Our findings reveal significant increases in both mean and peak aortic wall stresses during the transition from the preD state to the postD state, reflecting the mechanical impact of dissection. Surgical repair effectively restores aortic wall diameter to pre-dissection levels, documenting its effectiveness in mitigating further complications. Furthermore, we identified stress concentration regions within the aortic wall that closely correlated with observed dissection borders, offering insights into high-risk areas. This study demonstrates the importance of considering biomechanical factors when assessing aortic dissection risk. Despite some limitations, such as uniform wall thickness assumptions and the absence of dynamic blood flow considerations, our patient-specific FEA approach provides valuable mechanistic insights into aortic dissection. These findings hold promise for improving predictive models and informing clinical decisions to enhance patient care.

Indexed as

Aortic DissectionFinite Element AnalysisModels, CardiovascularStress, MechanicalAgedAortic AneurysmBiomechanical PhenomenaFemaleHumansMaleMiddle AgedTomography, X-Ray ComputedAortic dissectionFinite element analysisStress analysis

Identifiers

PMID39515268
PMCPMC11663132

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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.