Evidence map›Paper›PMID 38376719›Full record

ArticleThe international journal of cardiovascular imaging2024

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve.

Cosmin-Andrei Hatfaludi, Irina-Andra Tache, Costin-Florian Ciusdel, Andrei Puiu, Diana Stoian, Lucian Calmac, Nicoleta-Monica Popa-Fotea, Vlad Bataila, Alexandru Scafa-Udriste, Lucian Mihai Itu

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Article in The international journal of cardiovascular imaging, 2024. 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. Article
  2. 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

10 authors.

Cosmin-Andrei HatfaludiAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania. cosmin.hatfaludi@unitbv.ro.
Irina-Andra TacheAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
Costin-Florian CiusdelAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
Andrei PuiuAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
Diana StoianAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
Lucian CalmacDepartment of Cardiology, Emergency Clinical Hospital, 8 Calea Floreasca, Bucharest, 014461, Romania.
Nicoleta-Monica Popa-FoteaDepartment of Cardiology, Emergency Clinical Hospital, 8 Calea Floreasca, Bucharest, 014461, Romania.
Vlad BatailaDepartment of Cardiology, Emergency Clinical Hospital, 8 Calea Floreasca, Bucharest, 014461, Romania.
Alexandru Scafa-UdristeDepartment of Cardiology, Emergency Clinical Hospital, 8 Calea Floreasca, Bucharest, 014461, Romania.
Lucian Mihai ItuAdvanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.

Funding

EEA Grants 2014-2021 EEA Grants 2014-2021, under Project contract no. 33/2021Romanian National Authority for Scientific Research and Innovation project number ERANET-PERMED-PROGRESS, within PNCDI III
6 · The paper itself

Abstract

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Indexed as

Coronary AngiographyCoronary Artery DiseaseCoronary VesselsFractional Flow Reserve, MyocardialPredictive Value of TestsTomography, Optical CoherenceAgedCardiac CatheterizationDatabases, FactualDatasets as TopicDeep LearningFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedCoronary angiographyCoronary artery diseaseDeep neural networksEnsemble modelsFew-shot learningFractional flow reserveOptical coherence tomography

Identifiers

PMID38376719

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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.