Key result
An artificial intelligence model using 12-lead ECGs predicted clinical risk classification (low-risk vs. severe pulmonary embolism) with an AUC of 0.83 and an F1 score of 0.78.
Why the study?
Current risk stratification algorithms for acute pulmonary embolism involve a multistep process requiring physical exam, imaging, and laboratory results, prompting investigation into whether ECG alone with artificial intelligence can rapidly identify elevated clinical risk.
Does an AI-driven ECG analysis model accurately predict clinical risk categorization in patients with acute pulmonary embolism?
Observational (n=1,376)
Does an AI-driven ECG analysis model accurately predict clinical risk categorization in patients with acute pulmonary embolism?
Effect estimate: AUC 0.83
An AI-based analysis of 12-lead ECGs can accurately predict clinical risk stratification in patients with acute pulmonary embolism, potentially accelerating triage and treatment.
ECG-AI may enable rapid PE risk stratification; leaves open prospective validation before clinical use.
Aims: Among patients with acute pulmonary embolism (PE), rapid identification of those with highest clinical risk can help guide life-saving treatment. However, current risk stratification algorithms involve a multistep process requiring physical exam, imaging, and laboratory results. We investigated the utility of electrocardiogram (ECG) alone to rapidly identify patients at elevated clinical risk by developing and validating a feature-based artificial intelligence (AI) model to predict clinical risk. Methods and results: Patients who were diagnosed with PE over a 9-year period, had an ECG within 1 day of presentation, and were evaluated by our PE response team (PERT) were included. A feature-based random forest model was trained to predict the PERT team's risk stratification from the ECG alone. The ability of the model to predict the clinical risk categorization and the accuracy of both risk stratification approaches in predicting mortality were examined on a holdout test set. Of the overall cohort of 1376 patients, 55% had a submassive (intermediate risk) or massive (high risk) PE, which were grouped together as 'severe PE'. The AI-ECG model was able to predict the clinical classification (low-risk vs. severe PE) with an AUC of 0.83 and F1 score of 0.78 in a holdout test set. A 30-day mortality and in-hospital mortality were significantly different between patients classified by the model as low vs. elevated risk. Conclusion: AI-based analysis of 12-lead ECGs may provide a useful tool in the risk stratification of PE, allowing for rapid identification and treatment of those at highest risk of adverse outcomes.
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Gokhale et al. (2025) conducted an observational in Acute pulmonary embolism (n=1,376). Artificial intelligence-driven ECG analysis vs. Clinical risk stratification was evaluated on Prediction of clinical risk categorization (low-risk vs. severe PE) (AUC 0.83). An artificial intelligence model using 12-lead ECGs predicted clinical risk classification (low-risk vs. severe pulmonary embolism) with an AUC of 0.83 and an F1 score of 0.78.
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