Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada...

Area of Science:

• Cardiology
• Medical Informatics
• Data Science

Background:

• Brugada syndrome risk stratification currently relies on combined clinical and electrocardiographic factors.
• Existing methods may not fully capture complex interrelationships between variables.

Purpose of the Study:

• To evaluate if nonnegative matrix factorization (NMF) can improve risk stratification for Brugada syndrome compared to traditional logistic regression.
• To assess the predictive performance of NMF in identifying patients at risk for spontaneous ventricular tachycardia/ventricular fibrillation.

Main Methods:

• Retrospective cohort study of 149 Brugada syndrome patients from Hong Kong (2000-2016).
• Primary outcome: spontaneous ventricular tachycardia/ventricular fibrillation.
• Comparison of logistic regression with NMF incorporating latent variables from clinical and electrocardiographic data.
• External validation on a cohort of 227 patients from 3 countries.

Main Results:

• Patients experiencing ventricular events had higher rates of syncope, atrial fibrillation, and longer QTc intervals.
• Logistic regression identified syncope, atrial fibrillation, QRS duration, and QTc interval as significant predictors.
• NMF improved the area under the curve (AUC) for predicting arrhythmic events from 0.71 to 0.80 in the primary cohort.
• NMF enhanced the AUC from 0.64 to 0.71 in the external validation cohort.

Conclusions:

• Nonnegative matrix factorization offers improved predictive performance for arrhythmic outcomes in Brugada syndrome.
• NMF effectively extracts latent features between variables, enhancing risk stratification accuracy.
• This data-driven approach holds promise for refining clinical decision-making in Brugada syndrome management.