What is the clinical evidence from this study?

Study design: Cohort. Population: Brugada syndrome (n=149). Intervention: Nonnegative matrix factorization vs. Logistic regression. Primary outcome: Spontaneous ventricular tachycardia/ventricular fibrillation.

November 10, 2020Open Access

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

Key Result

Nonnegative matrix factorization improved the area under the curve for predicting spontaneous ventricular tachycardia or fibrillation from 0.71 to 0.80 compared with logistic regression.

Study Design

Type

Cohort (n=149)

Multicenter

Yes

Structured PICO

Does incorporating latent variables using nonnegative matrix factorization improve risk stratification for spontaneous ventricular tachycardia/ventricular fibrillation in patients with Brugada syndrome compared to logistic regression?

Population

149 patients with Brugada syndrome (84% males, median age 50 [38-61] years) from Hong Kong, China, and an external validation cohort of 227 patients from 3 countries.

Intervention

Risk stratification model incorporating latent variables using nonnegative matrix factorization

Comparator

Standard logistic regression model

Outcome

Spontaneous ventricular tachycardia/ventricular fibrillationhard clinical

Incorporating latent variables using nonnegative matrix factorization significantly improves the prediction of spontaneous ventricular tachycardia or fibrillation in patients with Brugada syndrome compared to standard logistic regression.

Main Result

Absolute Event Rate: 0.8% vs 0.71%

Abstract

Background A combination of clinical and electrocardiographic risk factors is used for risk stratification in Brugada syndrome. In this study, we tested the hypothesis that the incorporation of latent variables between variables using nonnegative matrix factorization can improve risk stratification compared with logistic regression. Methods and Results This was a retrospective cohort study of patients presented with Brugada electrocardiographic patterns between 2000 and 2016 from Hong Kong, China. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation. The external validation cohort included patients from 3 countries. A total of 149 patients with Brugada syndrome (84% males, median age of presentation 50 38–61 years) were included. Compared with the nonarrhythmic group (n=117, 79%), the spontaneous ventricular tachycardia/ ventricular fibrillation group (n=32, 21%) were more likely to suffer from syncope (69% versus 37%, P =0.001) and atrial fibrillation (16% versus 4%, P =0.023) as well as displayed longer QTc intervals (424 399–449 versus 408 386–425; P =0.020). No difference in QRS interval was observed (108 98–114 versus 102 95–110, P =0.104). Logistic regression found that syncope (odds ratio, 3.79; 95% CI, 1.64–8.74; P =0.002), atrial fibrillation (odds ratio, 4.15; 95% CI, 1.12–15.36; P =0.033), QRS duration (odds ratio, 1.03; 95% CI, 1.002–1.06; P =0.037) and QTc interval (odds ratio, 1.02; 95% CI, 1.01–1.03; P =0.009) were significant predictors of spontaneous ventricular tachycardia/ventricular fibrillation. Increasing the number of latent variables of these electrocardiographic indices incorporated from n=0 (logistic regression) to n=6 by nonnegative matrix factorization improved the area under the curve of the receiving operating characteristics curve from 0.71 to 0.80. The model improves area under the curve of external validation cohort (n=227) from 0.64 to 0.71. Conclusions Nonnegative matrix factorization improves the predictive performance of arrhythmic outcomes by extracting latent features between different variables.