| Summary: | 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.
|
|---|