Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary
Jesse P A Demandt1, Thomas P Mast2, Konrad A J van Beek2
1Department of Cardiology, Catharina Hospital, Eindhoven, Netherlands jesse.demandt@catharinaziekenhuis.nl.
Insights
A new artificial intelligence (AI) model using convolutional neural networks (CNNs) shows promise in identifying low-risk patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) in emergency medical services (EMS). While AI improves ECG interpretation, clinical risk scores remain superior but can be enhanced by AI integration.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Chest pain is a common emergency medical services (EMS) presentation, often suspected as non-ST-elevation acute coronary syndrome (NSTE-ACS).
- Differentiating NSTE-ACS from non-cardiac causes using prehospital ECG alone is challenging.
- Accurate risk stratification is crucial for appropriate patient triage, reducing emergency department overcrowding and healthcare costs.
Purpose of the Study:
- To develop and validate a CNN-based model (ECG-AI) for NSTE-ACS risk stratification in the prehospital setting.
- To compare the diagnostic performance of ECG-AI against existing prehospital tools.
- To assess the potential of integrating AI into clinical risk scores for improved diagnostic accuracy.
Main Methods:
- A CNN model (ECG-AI) was trained and validated on internal and external cohorts of suspected NSTE-ACS patients.
- Diagnostic performance was compared between ECG-AI, paramedic ECG interpretation (ECG-EMS), point-of-care troponin, and the preHEART clinical risk score.
- The study included 5645 patients suspected of NSTE-ACS, with an external validation cohort of 754 patients.
Main Results:
- ECG-AI demonstrated superior diagnostic performance for NSTE-ACS compared to paramedic ECG interpretation (AUROC 0.70 vs 0.65).
- The preHEART clinical risk score showed higher diagnostic accuracy (AUROC 0.78) than ECG-AI alone.
- Integrating ECG-AI into the preHEART score significantly improved diagnostic performance (AUROC 0.83).
Conclusions:
- AI integration in prehospital ECG interpretation enhances the identification of low-risk NSTE-ACS patients.
- Current clinical risk scores offer the best diagnostic performance but can be further improved with AI.
- These findings support further research into AI's role in prehospital risk stratification for acute coronary syndromes.
Objectives:
Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the ECG is challenging. The aim of this study is to develop and validate a convolutional neural network (CNN)-based model for risk stratification of suspected NSTE-ACS patients and to compare its performance with currently available prehospital diagnostic tools.
Methods:
For this study, an internal training cohort and an external validation cohort were used, both consisting of suspected NSTE-ACS patients. A CNN (ECG interpretation by CNN (ECG-AI)) was trained and validated to detect NSTE-ACS. The diagnostic value of ECG-AI in detecting NSTE-ACS was compared with on-site ECG analyses by an EMS paramedic (ECG-EMS), point-of-care troponin assessment and a validated prehospital clinical risk score (prehospital History, ECG, Age, Risk factors and POC-troponin (preHEART)).
Results:
A total of 5645 patients suspected of NSTE-ACS were included. In the external validation cohort (n=754), 27% were diagnosed with NSTE-ACS. ECG-AI had a better diagnostic performance than ECG-EMS (area under the curve (AUROC) 0.70 (0.66 to 0.74) vs AUROC 0.65 (0.61 to 0.70), p=0.045) for diagnosing NSTE-ACS. The overall diagnostic accuracy of preHEART was AUROC 0.78 (0.74 to 0.82) and superior compared with ECG-AI (p=0.001). Incorporating ECG-AI into preHEART led to a significant improvement in diagnostic performance (AUROC 0.83 (0.79 to 0.86), p<0.001).
Discussion:
Correctly identifying patients who are at low risk for having NSTE-ACS is crucial for optimal triage in the prehospital setting. Recent studies have shown that these low-risk patients could potentially be left at home or transferred to a general practitioner, leading to less emergency department overcrowding and lower healthcare costs. Other studies demonstrated better overall diagnostic performance compared with our artificial intelligence (AI) model. However, these studies were aimed at a study population with a high prevalence of occlusive myocardial infarction, which could explain the differing levels of diagnostic performance.
Conclusion:
Integrating AI in prehospital ECG interpretation improves the identification of patients at low risk for having NSTE-ACS. Nonetheless, clinical risk scores currently yield the best diagnostic performance and their accuracy could be further enhanced through AI. Our results pave the way for new studies focused on exploring the role of AI in prehospital risk-stratification efforts.
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