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ECG-based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease
Ying Wang1, Jiaxin Sun2, Kui Sun3
1Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China.
Insights
The novel cardiodynamicsgram (CDG) method, using electrocardiograms (ECG), shows high accuracy in diagnosing coronary artery disease (CAD). This ECG-based tool may serve as a valuable pre-screening method for suspected CAD patients.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- The cardiodynamicsgram (CDG) is a novel noninvasive technique.
- It utilizes deterministic learning to extract dynamic ST-T segment information from electrocardiograms (ECG).
- CDG aims to identify anomalous functional information indicative of coronary artery disease (CAD).
Purpose of the Study:
- To evaluate the diagnostic performance of the CDG in patients with suspected CAD.
- To assess the accuracy of CDG compared to coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA).
- To explore the utility of CDG as a potential screening tool for CAD.
Main Methods:
- Retrospective enrollment of 456 patients with suspected CAD undergoing CCTA and ECG.
- CAD diagnosis based on CCTA (≥50% stenosis) or CT-derived fractional flow reserve (CT-FFR ≤0.8).
- CDG performance analyzed, including in an ECG-diagnosis-negative subgroup and patients with subsequent ICA.
Main Results:
- The CDG demonstrated an overall accuracy of 79.56% for CAD diagnosis (Sensitivity: 75.60%, Specificity: 82.99%, AUC: 0.836).
- In the ECG-diagnosis-negative subgroup, CDG accuracy was 80.27% with an AUC of 0.842.
- CDG showed a significant correlation with CT-FFR (r = -0.395, p < 0.001).
Conclusions:
- The ECG-based CDG exhibits high specificity and accuracy for diagnosing CAD.
- CDG effectively reflects functional cardiac information.
- It holds potential as a noninvasive screening tool for patients with suspected CAD prior to CCTA.
Background:
The cardiodynamicsgram (CDG), a novel noninvasive method, extracts dynamic ST-T segment information from an electrocardiogram (ECG) through deterministic learning.
Hypothesis:
The CDG can reflect anomalous functional information in coronary artery disease (CAD).
Methods:
We retrospectively enrolled 456 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) from January 2020 to 2022, followed immediately by standard 12-lead ECG acquisition. Positivity for CAD were defined as CCTA ≥ 50% or CT-derived fractional flow reserve (CT-FFR) ≤ 0.8. A CDG value <0 was considered negative; otherwise, it was considered positive. We also evaluated the diagnostic performance of the CDG in the ECG-diagnosis-negative subgroup and in patients who had undergone invasive coronary angiography (ICA) after CCTA.
Results:
Of 362 patients, 168 (46.41%) were positive for CAD, and 178 (49.17%) were men. The median age was 59 (52-66) years. The accuracy of the CDG in the diagnosis of CAD was 79.56%, with a sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) of 75.60%, 82.99%, and 0.836 (95% CI: 0.794-0.878), respectively. Similarly, in the ECG-diagnosis-negative subgroup (n = 223), the accuracy of the CDG was 80.27%, with an AUC of 0.842 (95% CI: 0.790-0.895). Among the 11 patients with CAD confirmed by ICA, 10 were diagnosed positive by the CDG. Furthermore, the CDG values and CT-FFR were correlated (r = -.395; p < .001).
Conclusions:
The ECG-based CDG has relatively high specificity and accuracy for the diagnosis of CAD and reflects functional cardiac information to some extent. It has the potential to be used as a screening tool for suspected CAD patients before CCTA.
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