Evidence map›Paper›PMID 33630159›Full record

ArticleEuropean radiology2021

A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.

Xin Liu, Xukai Mo, Heye Zhang, Guang Yang, Changzheng Shi, William Kongtou Hau

Abstract read
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In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
5.6field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 36 citations in OpenAlex.

  1. Clinical Management Across CAD-RADS Categories in Patients with Intermediate CT-FFR.Journal of cardiovascular development and disease · 2026
    Article
  2. Article
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  4. Impact of Diabetes on Management and Outcomes in Patients with Borderline FFRJournal of cardiovascular development and disease · 2025
    Article
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  6. Computed Tomography and Coronary Plaque Analysis.Tomography (Ann Arbor, Mich.) · 2025
    Review
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  8. Article
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  13. Review
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  15. Energy loss is related to CT fractional flow reserve progression in type 2 diabetes mellitus patients.American heart journal plus : cardiology research and practice · 2023
    Article
  16. Article
  17. Review
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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 at 5 institutions in 2 countries.

Xin Liu *Guangdong Academy Research on VR Industry, Foshan University, #18 Jiangwan 1st Road, Foshan, 528000, Guangdong, China.
Xukai Mo *Medical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China.
Heye ZhangSchool of Biomedical Engineering, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, China.
Guang YangNational Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
Changzheng ShiMedical Imaging Center, The First Affiliated Hospital of Jinan University, No 613 Huangpu Dadao West, Guangzhou, 510630, China. tsczcn@jnu.edu.cn.
William Kongtou HauDepartment of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, 30-32 Ngan Shing St., Sha Tin, Hong Kong, SAR, China.
Chinese University of Hong Kong · CNFirst Affiliated Hospital of Jinan University · CNFoshan University · CNImperial College London · GBSun Yat-sen University · CN

Funding

British Heart Foundation PG/16/78/32402Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation 201905010003Guangzhou Science and Technology Plan Project 201807010046Medical Scientific Research Foundation of Guangdong Province of China A2018132Natural Science Foundation of Guangdong Province, China 2019A1515011463the National Natural Science Foundation of China 61771464, U1801265, 82001910the Science and Technology Planning Project of Guangdong Province, China 2018A050506031, 2019B010110001
6 · The paper itself

Abstract

objectiveThis study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in the selection of patients for coronary intervention. MATERIALS AND

methodsPatients (N = 296) with symptomatic coronary artery disease identified by coronary computed tomography angiography (CTA) with stenosis over 50% were retrospectively enrolled from a single centre in this study. ICA-guided interventions were performed in patients at admission, and DL-FFRCT was conducted retrospectively. The influences on decision-making by using DL-FFRCT and the clinical outcome were compared to those of ICA-guided care for symptomatic CAD at the 2-year follow-up evaluation.

resultTwo hundred forty-three patients were evaluated. Up to 72% of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. A similar major adverse cardiovascular event (MACE) rate was observed in patients who underwent revascularisation with a DL-FFRCT value ≤ 0.8 (2.9%) compared to that of ICA-guided interventions (3.3%) (stented lesions with ICA stenosis > 75%) (p = 0.838).

conclusionDL-FFRCT can reduce the need for diagnostic coronary angiography when identifying patients suitable for coronary intervention. A low MACE rate was found in a 2-year follow-up investigation. KEY POINTS: • Seventy-two percent of diagnostic ICA studies could have been avoided by using a DL-FFRCT value > 0.8 as a cut-off for intervention. • Coronary artery stenting based on the diagnosis by using a 320-detector row CT scanner and a positive DL-FFRCT value could potentially be associated with a lower occurrence rate of major adverse cardiovascular events (2.9%) within the first 2 years. • A low event rate was found when intervention was performed in tandem lesions with haemodynamic significance based on DL-FFRCT < 0.8 as a cut-off value.

Indexed as

Coronary Artery DiseaseCoronary StenosisDeep LearningFractional Flow Reserve, MyocardialAlgorithmsComputed Tomography AngiographyCoronary AngiographyHemodynamicsHumansPredictive Value of TestsRetrospective StudiesSeverity of Illness IndexTomography, X-Ray ComputedComputed tomography angiographyCoronary artery diseaseDeep learningMyocardial fractional flow reserveMyocardial revascularisation

Identifiers

PMID33630159
OpenAlexW3130824616

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.