January 18, 2026Open Access

Automated ECG Report as a Factor in the Clinical Decision Pathway for Acute Chest Pain in the Emergency Department

Structured PICO

Does a hybrid model combining the Glasgow algorithm and GPT-4 accurately classify automated ECG reports in patients presenting with acute chest pain?

Population

860 ECGs from patients presenting with acute chest pain in the emergency department at Rashid Hospital, Dubai (June 2022 to August 2022).

Intervention

Hybrid model combining the established Glasgow algorithm with a large language model (GPT-4) using few-shot prompting to classify unstructured automated ECG reports.

Outcome

Predictive accuracy (F1 score, AUC, and overall accuracy) for classifying ECGs into four classes: STEMI, NSTEMI, normal ECG, and new arrhythmia.

Integrating large language models like GPT-4 with traditional ECG algorithms can accurately classify normal ECGs and STEMI, potentially accelerating emergency department triage for acute chest pain.

Abstract

Background Electrocardiographic analysis algorithms have consistently evolved, becoming essential tools for physicians in diverse settings, particularly in assessing patients with acute chest pain. Moving forward, it is crucial to classify unstructured automated ECG reports into clinically relevant outcomes using advanced large language models. This approach holds significant potential to enhance an accelerated clinical decision pathway in clinical settings. Objective This study aims to integrate automated electrocardiogram algorithms with advanced machine learning techniques, enhancing the classification of ECG reports within emergency department settings. Specifically, it investigates how natural language processing can augment traditional methods to accelerate the electrocardiographic-directed management of acute chest pain. Methods Employing a retrospective observational dataset from Rashid Hospital, Dubai, spanning from June 2022 to August 2022, we analyzed 860 ECGs from patients presenting with acute chest pain. The ECGs were categorized into four classes, namely, STEMI, NSTEMI, normal ECG, and new arrhythmia using a hybrid model that combines the established Glasgow algorithm with a large language model, GPT-4. The Glasgow algorithm produced structured text inputs, which were then classified by GPT-4 using few-shot prompting (temperature = 0. 2, topₚ=1. 0). Results The model demonstrates high predictive accuracy for normal ECGs, achieving an F1 score of 0. 93, followed by STEMI with an F1 score of 0. 80. New arrhythmias, however, present more challenges, reflected by the lowest F1 score of 0. 45. Notably, the model excels in discriminating between STEMI and normal ECGs (AUC=0. 92) and between STEMI and new arrhythmias (AUC=0. 91). Overall accuracy was 85. 9% (95% CI: 0. 816-0. 895) Conclusion The findings suggest that leveraging deep learning alongside traditional algorithms can significantly improve the rapid classification of ECGs, supporting accelerated decision-making pathways in clinical practice.

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