Faculty, Staff and Student Publications
Publication Date
1-1-2023
Journal
Frontiers in Cardiovascular Medicine
Abstract
BACKGROUND: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis.
METHODS: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF
RESULTS: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets.
CONCLUSION: Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.
DOI
10.3389/fcvm.2023.1070641
PMID
36960474
PMCID
PMC10029758
PubMedCentral® Posted Date
3-3-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Biomedical Informatics Commons, Cardiology Commons, Cardiovascular Diseases Commons, Data Science Commons
