Key result
Deep learning ECG model detects LV dysfunction with ~0.95 AUC and predicts future MACE.
Why the study?
Although deep learning models can detect low ejection fraction from ECGs, their clinical impacts had not been widely investigated.
Does an AI-enabled ECG model estimating ejection fraction predict future cardiovascular adverse events and left ventricular dysfunction?
Cohort (n=88,597)
Does an AI-enabled ECG model estimating ejection fraction predict future cardiovascular adverse events and left ventricular dysfunction?
An AI-enabled ECG model can accurately estimate left ventricular ejection fraction and independently predict future major adverse cardiovascular events, potentially serving as a screening tool for asymptomatic left ventricular dysfunction.
May support ECG-AI screening for LV dysfunction; hypothesis-generating for MACE prediction, requiring prospective validation.
BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD.
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Chen et al. (2022) conducted a cohort in Left ventricular dysfunction (n=88,597). Deep learning model for ECG-based ejection fraction (ECG-EF) vs. Echocardiography-based ejection fraction (ECHO-EF) was evaluated on Future ECHO-EF changes and major adverse cardiovascular events (MACEs) (AUC 0.9472). A deep learning model estimating ejection fraction via ECG achieved an AUC of 0.9472 for detecting left ventricular dysfunction and independently predicted future major adverse cardiovascular events.
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