A deep neural network using standard 12-lead ECG signals accurately identified left ventricular systolic dysfunction with an AUROC of 0.95 and a sensitivity of 0.91.
Observational (n=380,675)
Yes
Does a deep neural network-based model using standard 12-lead ECG accurately screen for left ventricular systolic dysfunction and predict mortality in adult patients?
A deep neural network applied to standard 12-lead ECGs can accurately detect left ventricular systolic dysfunction and strongly predict all-cause and cardiovascular mortality.
Effect estimate: AUROC 0.95
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 <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. 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.
Huang et al. (Fri,) conducted a observational in Left ventricular systolic dysfunction (n=380,675). Deep neural network (DNN) model using standard 12-lead ECG vs. Echocardiogram (LVEF <40%) was evaluated on Identification of left ventricular systolic dysfunction (LVEF <40%) (AUROC 0.95). A deep neural network using standard 12-lead ECG signals accurately identified left ventricular systolic dysfunction with an AUROC of 0.95 and a sensitivity of 0.91.