Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-11-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.119.012714 |
| _version_ | 1819183224847335424 |
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| author | Gary Tse Jiandong Zhou Sharen Lee Tong Liu George Bazoukis Panagiotis Mililis Ian C. K. Wong Cheng Chen Yunlong Xia Tsukasa Kamakura Takeshi Aiba Kengo Kusano Qingpeng Zhang Konstantinos P. Letsas |
| author_facet | Gary Tse Jiandong Zhou Sharen Lee Tong Liu George Bazoukis Panagiotis Mililis Ian C. K. Wong Cheng Chen Yunlong Xia Tsukasa Kamakura Takeshi Aiba Kengo Kusano Qingpeng Zhang Konstantinos P. Letsas |
| author_sort | Gary Tse |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T22:58:38Z |
| format | Article |
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| last_indexed | 2024-12-22T22:58:38Z |
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| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj.art-5f255401902f4ee69d83b08fbdc6e19f2022-12-21T18:09:44ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802020-11-0192210.1161/JAHA.119.012714Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada SyndromeGary Tse0Jiandong Zhou1Sharen Lee2Tong Liu3George Bazoukis4Panagiotis Mililis5Ian C. K. Wong6Cheng Chen7Yunlong Xia8Tsukasa Kamakura9Takeshi Aiba10Kengo Kusano11Qingpeng Zhang12Konstantinos P. Letsas13Tianjin Key Laboratory of Ionic‐Molecular Function of Cardiovascular disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin P.R. ChinaSchool of Data Science City University of Hong Kong Hong Kong Hong Kong SAR People’s Republic of ChinaLaboratory of Cardiovascular Physiology Chinese University Shenzhen Institute Shenzhen P.R. ChinaTianjin Key Laboratory of Ionic‐Molecular Function of Cardiovascular disease Department of Cardiology Tianjin Institute of Cardiology Second Hospital of Tianjin Medical University Tianjin P.R. ChinaSecond Department of Cardiology Laboratory of Cardiac Electrophysiology Evangelismos General Hospital of Athens Athens GreeceSecond Department of Cardiology Laboratory of Cardiac Electrophysiology Evangelismos General Hospital of Athens Athens GreeceSchool of Pharmacy University College London London UKDepartment of Cardiology The First Affiliated Hospital of Dalian Medical University Dalian ChinaDepartment of Cardiology The First Affiliated Hospital of Dalian Medical University Dalian ChinaNational Cerebral and Cardiovascular Center Osaka JapanNational Cerebral and Cardiovascular Center Osaka JapanNational Cerebral and Cardiovascular Center Osaka JapanSchool of Data Science City University of Hong Kong Hong Kong Hong Kong SAR People’s Republic of ChinaSecond Department of Cardiology Laboratory of Cardiac Electrophysiology Evangelismos General Hospital of Athens Athens GreeceBackground 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.https://www.ahajournals.org/doi/10.1161/JAHA.119.012714Brugada syndromedepolarizationECGlatent variablenonnegative matrix factorizationrepolarization |
| spellingShingle | Gary Tse Jiandong Zhou Sharen Lee Tong Liu George Bazoukis Panagiotis Mililis Ian C. K. Wong Cheng Chen Yunlong Xia Tsukasa Kamakura Takeshi Aiba Kengo Kusano Qingpeng Zhang Konstantinos P. Letsas Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease Brugada syndrome depolarization ECG latent variable nonnegative matrix factorization repolarization |
| title | Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome |
| title_full | Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome |
| title_fullStr | Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome |
| title_full_unstemmed | Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome |
| title_short | Incorporating Latent Variables Using Nonnegative Matrix Factorization Improves Risk Stratification in Brugada Syndrome |
| title_sort | incorporating latent variables using nonnegative matrix factorization improves risk stratification in brugada syndrome |
| topic | Brugada syndrome depolarization ECG latent variable nonnegative matrix factorization repolarization |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.119.012714 |
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