Artificial intelligence-driven electrocardiogram analysis for risk stratification in pulmonary embolism.

ABSTRACT:

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

SUBMITTER: Gokhale TA 

PROVIDER: S-EPMC12450519 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature