Evidence map›Paper›PMID 39931137›Full record

ArticleFrontiers in bioengineering and biotechnology2025

Enhancing cardiac assessments: accurate and efficient prediction of quantitative fractional flow reserve.

Arshia Eskandari, Sara Malek, Alireza Jabbari, Kian Javari, Nima Rahmati, Behrad Nikbakhtian, Bahram Mohebbi, Seyed Ehsan Parhizgar, Mona Alimohammadi

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Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Arshia EskandariDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Sara MalekDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Alireza JabbariDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Kian JavariDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Nima RahmatiDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Behrad NikbakhtianDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Bahram MohebbiRajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
Seyed Ehsan ParhizgarRajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
Mona AlimohammadiDepartment of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Obstruction within the left anterior descending coronary artery (LAD) is prevalent, serving as a prominent and independent predictor of mortality. Invasive Fractional flow reserve (FFR) is the gold standard for Coronary Artery Disease risk assessment. Despite advances in computational and imaging techniques, no definitive methodology currently assures clinicians of reliable, non-invasive strategies for future planning. Method: The present research encompassed a cohort of 150 participants who were admitted to the Rajaie Cardiovascular, Medical, and Research Center. The method includes a three-dimensional geometry reconstruction, computational fluid dynamics simulations, and methodology optimization for the computation time. Four patients are analyzed within this study to showcase the proposed methodology. The invasive FFR results reported by the clinic have validated the optimized model. Results: The computational FFR data derived from all methodologies are compared with those reported by the clinic for each case. The chosen methodology has yielded virtual FFR values that exhibit remarkable proximity to the clinically reported patient-specific FFR values, with the MSE of 6.186e-7 and R2 of 0.99 (p = 0.00434). Conclusion: This approach has shown reliable results for all 150 patients. The results are both computationally and clinically user-friendly, with the accumulative pre and post-processing time of 15 min on a desktop computer (Intel i7 processor, 16 GB RAM). The proposed methodology has the potential to significantly assist clinicians with diagnosis.

Indexed as

computational fluid dynamicscoronary artery diseasefractional flow reservemyocardial infarctionnoninvasive imagingvirtual surgery

Identifiers

PMID39931137
PMCPMC11808135

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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.