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Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs. | LitMetric

AI Article Synopsis

  • The study aimed to determine if a deep neural network could identify regional wall motion abnormalities (RWMAs) using standard 12-lead electrocardiograms (ECGs) to help diagnose and assess cardiovascular disease risks.
  • Conducted at Beth Israel Deaconess Medical Center, the study analyzed patients from 2008 to 2019, using ECG data paired with echocardiograms to train the model for detecting various cardiac dysfunctions.
  • The model showed strong accuracy in identifying cardiac conditions, with high area under the curve (AUC) scores, and it indicated that patients diagnosed with RWMAs were at a significantly higher risk for future acute coronary events compared to those without such abnormalities.

Article Abstract

Background: Identifying regional wall motion abnormalities (RWMAs) is critical for diagnosing and risk stratifying patients with cardiovascular disease, particularly ischemic heart disease. We hypothesized that a deep neural network could accurately identify patients with regional wall motion abnormalities from a readily available standard 12-lead electrocardiogram (ECG).

Methods: This observational, retrospective study included patients who were treated at Beth Israel Deaconess Medical Center and had an ECG and echocardiogram performed within 14 days of each other between 2008 and 2019. We trained a convolutional neural network to detect the presence of RWMAs, qualitative global right ventricular (RV) hypokinesis, and varying degrees of left ventricular dysfunction (left ventricular ejection fraction [LVEF] ≤50%, LVEF ≤40%, and LVEF ≤35%) identified by echocardiography, using ECG data alone. Patients were randomly split into development (80%) and test sets (20%). Model performance was assessed using area under the receiver operating characteristic curve (AUC). Cox proportional hazard models adjusted for age and sex were performed to estimate the risk of future acute coronary events.

Results: The development set consisted of 19,837 patients (mean age 66.7±16.4; 46.7% female) and the test set comprised of 4,953 patients (mean age 67.5±15.8 years; 46.5% female). On the test dataset, the model accurately identified the presence of RWMA, RV hypokinesis, LVEF ≤50%, LVEF ≤40%, and LVEF ≤35% with AUCs of 0.87 (95% CI 0.858-0.882), 0.888 (95% CI 0.878-0.899), 0.923 (95% CI 0.914-0.933), 0.93 (95% CI 0.921-0.939), and 0.876 (95% CI 0.858-0.896), respectively. Among patients with normal biventricular function at the time of the index ECG, those classified as having RMWA by the model were 3 times the risk (age- and sex-adjusted hazard ratio, 2.8; 95% CI 1.9-3.9) for future acute coronary events compared to those classified as negative.

Conclusions: We demonstrate that a deep neural network can help identify regional wall motion abnormalities and reduced LV function from a 12-lead ECG and could potentially be used as a screening tool for triaging patients who need either initial or repeat echocardiographic imaging.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11160848PMC
http://dx.doi.org/10.1101/2024.05.31.24308304DOI Listing

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