Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency
Antonius Büscher1,2, Lucas Plagwitz1, Kemal Yildirim1
1Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1/Building A11, Münster 48149, Germany.
Insights
A deep learning model accurately detects electrocardiogram (ECG) patterns for acute coronary syndrome revascularization likelihood. This AI tool shows promise in improving diagnosis and complementing clinical assessment for myocardial infarction (MI).
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Acute coronary syndrome (ACS) diagnosis can be challenging with inconclusive ECG or biomarker results.
- Accurate identification of patients needing coronary revascularization is crucial for timely intervention.
- Current diagnostic methods may lead to diagnostic uncertainty and delays in treatment.
Purpose of the Study:
- To develop and validate a deep learning model for detecting ECG patterns indicative of revascularization likelihood in ACS patients.
- To assess the model's performance against clinician interpretation and cardiac biomarkers.
- To evaluate the model's utility in guiding further assessment and reducing diagnostic uncertainty.
Main Methods:
- A convolutional neural network model was trained on a large US cohort (144,691 ED visits).
- The model was tested on a separate cohort and externally validated on a European cohort (18,673 ED visits).
- Performance was benchmarked against clinician ECG interpretation and cardiac troponin T (TnT) using area under the receiver operating characteristic curve (AUROC).
Main Results:
- The deep learning model achieved an AUROC of 0.91 in the test cohort, outperforming clinician ECG interpretation (0.65) and cardiac TnT (0.71).
- External validation showed AUROCs of 0.81 for revascularization and 0.85 for type 1 MI.
- The model demonstrated competitive performance, with higher specificity but lower sensitivity than high-sensitivity TnT.
Conclusions:
- The developed deep learning model effectively detects ECG patterns associated with revascularization and type 1 MI.
- The model's performance suggests it can serve as a valuable tool to complement existing clinical assessments for ACS.
- This AI-driven approach has the potential to improve diagnostic accuracy and patient management in cardiology.
Background And Aims:
Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusive electrocardiogram (ECG) findings or biomarker results. A deep learning model to detect ECG patterns associated with revascularization likelihood was developed, aiming to guide further assessment and reduce diagnostic uncertainty.
Methods:
A convolutional neural network model was trained on 144 691 ED visits from a US cohort (60 ± 19 years; 53% female; 0.6% revascularization), tested on a separate test cohort (n = 35 995), and benchmarked against clinician ECG interpretation and cardiac troponin T (TnT). External validation was performed for the outcomes revascularization and Type 1 myocardial infarction (MI) on 18 673 ED visits from Europe (55 ± 21 years; 49% female; 1.5% revascularization; 1% Type 1 MI). Primary performance metric was area under the receiver operating characteristic curve (AUROC).
Results:
In the test cohort, the model achieved an AUROC of 0.91 (95% confidence interval [CI] 0.91-0.91), outperforming clinician ECG interpretation (AUROC 0.65, 95% CI 0.54-0.76) and conventional cardiac TnT (AUROC 0.71). In the external validation cohort, ECG model AUROC was 0.81 (95% CI 0.81-0.82) for revascularization, and 0.85 (95% CI 0.84-0.85) for Type 1 MI, compared with 0.70 (95% CI 0.57-0.83) and 0.74 (95% CI 0.56-0.92) for clinician interpretation, and 0.85 and 0.87 for high-sensitivity (hs)-TnT, respectively. The ECG model had higher specificity but lower sensitivity compared with high-sensitivity-troponin T.
Conclusions:
The model was able to detect revascularization and Type 1 MI with competitive performance, suggesting a potential role to complement current clinical assessment.
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