Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

Abstract

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.

Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review

“…Attia et al [12] from the Mayo Clinic created an algorithm that was tested in eight studies [12][13][14][15][16][17], one group from Seoul (the republic of Korea) developed and tested their algorithms in two studies [18,19]. Five research groups (Sbrollini et al) developed in-house algorithms and tested them in separate studies [20][21][22][23][24]. A detailed overview of each study population and outcome can be found in "Supplementary results" (Supplementary Appendix 3), where details are provided for AUC, type of outcome, model adjustments and comparison to other test (e.g., BNP/NT-proBNP).…”

“…A retrospective design was used in eleven studies [12][13][14][17][18][19][20][21][22][23][24], one was a case series [15] and three were prospective cohort studies [16,25,26]. Apart from those 15 studies that fulfilled eligibility criteria for development and testing of algorithms, we identified one randomized controlled trial [1] and one cost-effectiveness study [27].…”

“…The diversity of study cohorts reflected a broad clinical spectrum, spanning from unselected digital ECG databases [12,15,16,20,[22][23][24], emergency department patients [13], cardiac intensive care unit patients [17], unspecified hospitalized patients [18,19,21], general populations [14,25] and patients diagnosed with Chagas disease [26].…”

“…Truly external validation was only applied to one algorithm by Attia et al [12] as this was the only algorithm to be tested in other studies with separate populations [13-17, 25, 26]. More than half of the algorithms were not validated in a truly external dataset as the same dataset was used for both development and validation (the same dataset was split into a training, internal validation, and external validation group) [20][21][22]24]. The remaining studies used different datasets for training/validation and external validation, respectively, but the majority of algorithms were not truly externally validated [18,19,23].…”

A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

“…The automated diagnosis appeared to influence the decision-making of the physicians, but overdiagnosis of STEMI by the automated ECG analysis, which was detected by a simple algorithm, was reported, with a false-positive rate up to 42% with acute pericarditis as one of the leading causes [ 12 , 13 , 14 ]. As artificial intelligence (AI) techniques rapidly evolved, several deep learning models (DLMs) were developed and shown to achieve the performance of human experts in detecting numerous cardiac diseases [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Several AI-based algorithms have been used to detect STEMI; however, to the best of our knowledge, there is no study regarding the application of AI to recognize acute pericarditis [ 22 ].…”