Physiologic and compositional coronary artery disease extension in patients with takotsubo syndrome assessed using artificial intelligence: an optical coherence tomography study. (Record no. 582)

MARC details
000 -LEADER
fixed length control field 03163nam a22004337a 4500
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fixed length control field 220511s20222022 xxu||||| |||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 0954-6928
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 00019501-990000000-00002 [pii]
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 10.1097/MCA.0000000000001130 [doi]
040 ## - CATALOGING SOURCE
Original cataloging agency Ovid MEDLINE(R)
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
PMID 35383583
245 ## - TITLE STATEMENT
Title Physiologic and compositional coronary artery disease extension in patients with takotsubo syndrome assessed using artificial intelligence: an optical coherence tomography study.
251 ## - Source
Source Coronary Artery Disease. 2022 Apr 05
252 ## - Abbreviated Source
Abbreviated source Coron Artery Dis. 2022 Apr 05
253 ## - Journal Name
Journal name Coronary artery disease
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Year 2022
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Manufacturer FY2022
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Publication date 2022 Apr 05
265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE]
Publication status aheadofprint
266 ## - Date added to catalog
Date added to catalog 2022-05-11
520 ## - SUMMARY, ETC.
Abstract BACKGROUND: Takotsubo syndrome (TTS) is an acute and reversible ventricular motion abnormality without epicardial coronary obstruction. Optical flow ratio (OFR) is an approach to evaluate the coronary stenosis significance based on three-dimensional optical coherence tomography (3D-OCT). The aim of this study is to utilize OCT and an artificial intelligence plaque characterization model to show the prevalence and composition of atherosclerotic disease in coronary vessels of patients with TTS.
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Abstract CONCLUSION: Utilizing automatic plaque characterization on OCT images by artificial intelligence, we found that TTS patients have coronary artery disease (i.e. presence of lipid, calcified, or fibrous tissue). The advent of artificial intelligence methods may allow for large-scale studies of patients with TTS. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
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Abstract METHODS: This is a retrospective and observational study which enrolled patients with TTS who underwent coronary angiography and OCT examination. OCT images were analyzed for tissue characterization and OFR computation using a novel artificial intelligence algorithm.
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Abstract RESULTS: A total of 37 patients and 49 vessels were studied. All patients were imaged in the left anterior descending coronary artery (LAD) and about two-thirds were also imaged in the left circumflex coronary artery (LCX). Most patients were women (n = 35), and apical was the most common takotsubo type. Tissue composition analysis yielded the following overall plaque types: fibrous (67.1%), lipid (15.5%), and calcium (3.77%). The mean OFR for LAD and LCX was 0.97 +/- 0.04 and 0.98 +/- 0.02, respectively.
546 ## - LANGUAGE NOTE
Language note English
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element IN PROCESS -- NOT YET INDEXED
651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Institution MedStar Heart & Vascular Institute
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Department Internal Medicine Residency
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Department MedStar Georgetown University Hospital/MedStar Washington Hospital Center
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Medline publication type Journal Article
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Local Authors Garcia-Garcia, Hector M
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Local Authors Kahsay, Yirgan
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Local Authors Kuku, Kayode
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Local Authors Kumar, Sant
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Local Authors Kumar, Sant
790 ## - Authors
All authors Alfonso F, Chu M, Garcia-Garcia HM, Gonzalo N, Kahsay Y, Kuku KO, Kumar S, Salazar CH, Tu S
856 ## - ELECTRONIC LOCATION AND ACCESS
DOI <a href="https://dx.doi.org/10.1097/MCA.0000000000001130">https://dx.doi.org/10.1097/MCA.0000000000001130</a>
Public note https://dx.doi.org/10.1097/MCA.0000000000001130
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
Item type description Article
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          MedStar Authors Catalog MedStar Authors Catalog 05/11/2022   35383583 35383583 05/11/2022 05/11/2022 Journal Article

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