In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 10 ( 2022-10-27), p. e0276928-
Abstract:
Coronary angiography (CAG) is still considered the reference standard for coronary artery assessment, especially in the treatment of acute coronary syndrome (ACS). Although aging causes changes in coronary arteries, the age-related imaging features on CAG and their prognostic relevance have not been fully characterized. We hypothesized that a deep neural network (DNN) model could be trained to estimate vascular age only using CAG and that this age prediction from CAG could show significant associations with clinical outcomes of ACS. A DNN was trained to estimate vascular age using ten separate frames from each of 5,923 CAG videos from 572 patients. It was then tested on 1,437 CAG videos from 144 patients. Subsequently, 298 ACS patients who underwent percutaneous coronary intervention (PCI) were analysed to assess whether predicted age by DNN was associated with clinical outcomes. Age predicted as a continuous variable showed mean absolute error of 4 years with R squared of 0.72 ( r = 0.856). Among the ACS patients stratified by predicted age from CAG images before PCI, major adverse cardiovascular events (MACE) were more frequently observed in the older vascular age group than in the younger vascular age group (p = 0.017). Furthermore, after controlling for actual age, gender, peak creatine kinase, and history of heart failure, the older vascular age group independently suffered from more MACE (hazard ratio 2.14, 95% CI 1.07 to 4.29, p = 0.032). The vascular age estimated based on CAG imaging by DNN showed high predictive value. The age predicted from CAG images by DNN could have significant associations with clinical outcomes in patients with ACS.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0276928
DOI:
10.1371/journal.pone.0276928.g001
DOI:
10.1371/journal.pone.0276928.g002
DOI:
10.1371/journal.pone.0276928.g003
DOI:
10.1371/journal.pone.0276928.g004
DOI:
10.1371/journal.pone.0276928.g005
DOI:
10.1371/journal.pone.0276928.t001
DOI:
10.1371/journal.pone.0276928.t002
DOI:
10.1371/journal.pone.0276928.t003
DOI:
10.1371/journal.pone.0276928.t004
DOI:
10.1371/journal.pone.0276928.s001
DOI:
10.1371/journal.pone.0276928.s002
DOI:
10.1371/journal.pone.0276928.r001
DOI:
10.1371/journal.pone.0276928.r002
DOI:
10.1371/journal.pone.0276928.r003
DOI:
10.1371/journal.pone.0276928.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
Bookmarklink