Classification of patients with embolic stroke of undetermined source into cardioembolic and non-cardioembolic
Max Christian Martin1, Thorsten Sichtermann2, Kolja Schürmann1
1Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany.
Insights
Machine learning accurately identifies cardioembolic stroke of undetermined source (ESUS) patients. The model found 40% of ESUS patients have a cardioembolic profile, potentially benefiting from anticoagulation.
Area of Science:
- Neurology
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Embolic stroke of undetermined source (ESUS) has diverse hidden causes, with cardioembolism being significant.
- Identifying cardioembolic ESUS is crucial for potential oral anticoagulation therapy.
- Current methods for differentiating cardioembolic ESUS are unclear.
Purpose of the Study:
- To develop and validate a machine learning (ML) model to differentiate cardioembolic from non-cardioembolic ESUS profiles.
- To utilize baseline demographic and laboratory variables for ML model training and classification.
Main Methods:
- An ML model was trained on demographic and laboratory data from ischemic stroke patients with known etiologies.
- The model was validated for accuracy in distinguishing cardioembolic vs. non-cardioembolic stroke.
- The validated model was applied to classify patients with ESUS.
Main Results:
- The ML model achieved excellent accuracy (AUC=0.82) in classifying known stroke etiologies.
- The model identified 40.3% of ESUS patients as having a cardioembolic profile.
- Patients with cardioembolic profiles were older, more often female, had higher CHA2DS2-VASc scores, and more premature atrial complexes.
Conclusions:
- A machine learning model effectively classifies ESUS patients into cardioembolic and non-cardioembolic groups.
- The model predicts a significant proportion (40%) of ESUS patients may have a cardioembolic origin.
- This ML approach aids in identifying ESUS patients who might benefit from anticoagulation.
Background And Purpose:
It is currently thought that embolic stroke of undetermined source (ESUS) has diverse underlying hidden etiologies, of which cardioembolism is one of the most important. The subgroup of patients with this etiology could theoretically benefit from oral anticoagulation, but it remains unclear if these patients can be correctly identified from other ESUS subgroups and which markers should be used. We aimed to determine whether a machine-learning (ML) model could discriminate between ESUS patients with cardioembolic and those with non-cardioembolic profiles using baseline demographic and laboratory variables.
Methods:
Based on a prospective registry of consecutive ischemic stroke patients submitted to acute revascularization therapies, an ML model was trained using the age, sex and 11 selected baseline laboratory parameters of patients with known stroke etiology, with the aim of correctly identifying patients with cardioembolic and non-cardioembolic etiologies. The resulting model was used to classify ESUS patients into those with cardioembolic and those with non-cardioembolic profiles.
Results:
The ML model was able to distinguish patients with known stroke etiology into cardioembolic or non-cardioembolic profile groups with excellent accuracy (area under the curve = 0.82). When applied to ESUS patients, the model classified 40.3% as having cardioembolic profiles. ESUS patients with cardioembolic profiles were older, more frequently female, more frequently had hypertension, less frequently were active smokers, had higher CHA2 DS2 -VASc (Congestive heart failure or left ventricular systolic dysfunction, Hypertension, Age ≥ 75 [doubled], Diabetes, Stroke/transient ischemic attack [doubled], Vascular disease, Age 65-74, and Sex category) scores, and had more premature atrial complexes per hour.
Conclusions:
An ML model based on baseline demographic and laboratory variables was able to classify ESUS patients into cardioembolic or non-cardioembolic profile groups and predicted that 40% of the ESUS patients had a cardioembolic profile.
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