Evidence map›Paper›PMID 31997622›Full record

ArticleYonsei medical journal2020

Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System.

Hyung Bok Park, Yeonggul Jang, Reza Arsanjani, Minh Tuan Nguyen, Sang Eun Lee, Byunghwan Jeon, Sunghee Jung, Youngtaek Hong, Seongmin Ha, Sekeun Kim and 2 more

Abstract read
In one paragraph

Article in Yonsei medical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

12 authors.

Hyung Bok Park *Connect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-3773-2665
Yeonggul Jang *Connect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5805-7494
Reza ArsanjaniMayo Clinic, Division of Cardiology, Department of Internal Medicine, Scottsdale, AZ, USA.ORCID https://orcid.org/0000-0001-7081-4286
Minh Tuan NguyenSchool of Mechanical Engineering, University of Ulsan, Ulsan, Korea.ORCID https://orcid.org/0000-0002-2915-9133
Sang Eun LeeConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6645-4038
Byunghwan JeonConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-0414-1762
Sunghee JungConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-8042-457X
Youngtaek HongConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-2104-5905
Seongmin HaConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-0731-2301
Sekeun KimConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-4196-6242
Sang Wook LeeSchool of Mechanical Engineering, University of Ulsan, Ulsan, Korea. leesw@ulsan.ac.kr.ORCID https://orcid.org/0000-0002-8600-9991
Hyuk Jae ChangConnect-AI Research Center, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-6139-7545

Funding

Institute for Information and communications Technology Promotion R0101-16-0171
6 · The paper itself

Abstract

purposeTo evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA). MATERIALS AND

methodsWe analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75.

resultsThe average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75.

conclusionOur novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.

Indexed as

Computer SimulationFractional Flow Reserve, MyocardialAgedAlgorithmsArea Under CurveFemaleHumansLinear ModelsMalePredictive Value of TestsProspective StudiesROC CurveSeverity of Illness IndexTomography, X-Ray Computedcomputed tomography angiographyFractional flow reserve, myocardialpatient-specific computational modeling

Identifiers

PMID31997622
PMCPMC6992455

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.