RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses
Introduction: Congenital long QT syndrome (LQTS) is a cardiac ion channelopathy that predisposes affected individuals to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). The main aims of the study were to: (1) provide a description of the local epidemiology of...
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Frontiers Media S.A.
2021-02-01
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| author | Gary Tse Gary Tse Sharen Lee Jiandong Zhou Tong Liu Ian Chi Kei Wong Ian Chi Kei Wong Chloe Mak Ngai Shing Mok Kamalan Jeevaratnam Qingpeng Zhang Shuk Han Cheng Wing Tak Wong |
| author_facet | Gary Tse Gary Tse Sharen Lee Jiandong Zhou Tong Liu Ian Chi Kei Wong Ian Chi Kei Wong Chloe Mak Ngai Shing Mok Kamalan Jeevaratnam Qingpeng Zhang Shuk Han Cheng Wing Tak Wong |
| author_sort | Gary Tse |
| collection | DOAJ |
| description | Introduction: Congenital long QT syndrome (LQTS) is a cardiac ion channelopathy that predisposes affected individuals to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). The main aims of the study were to: (1) provide a description of the local epidemiology of LQTS, (2) identify significant risk factors of ventricular arrhythmias in this cohort, and (3) compare the performance of traditional Cox regression with that of random survival forests.Methods: This was a territory-wide retrospective cohort study of patients diagnosed with congenital LQTS between 1997 and 2019. The primary outcome was spontaneous VT/VF.Results: This study included 121 patients [median age of initial presentation: 20 (interquartile range: 8–44) years, 62% female] with a median follow-up of 88 (51–143) months. Genetic analysis identified novel mutations in KCNQ1, KCNH2, SCN5A, ANK2, CACNA1C, CAV3, and AKAP9. During follow-up, 23 patients developed VT/VF. Univariate Cox regression analysis revealed that age [hazard ratio (HR): 1.02 (1.01–1.04), P = 0.007; optimum cut-off: 19 years], presentation with syncope [HR: 3.86 (1.43–10.42), P = 0.008] or VT/VF [HR: 3.68 (1.62–8.37), P = 0.002] and the presence of PVCs [HR: 2.89 (1.22–6.83), P = 0.015] were significant predictors of spontaneous VT/VF. Only initial presentation with syncope remained significant after multivariate adjustment [HR: 3.58 (1.32–9.71), P = 0.011]. Random survival forest (RSF) model provided significant improvement in prediction performance over Cox regression (precision: 0.80 vs. 0.69; recall: 0.79 vs. 0.68; AUC: 0.77 vs. 0.68; c-statistic: 0.79 vs. 0.67). Decision rules were generated by RSF model to predict VT/VF post-diagnosis.Conclusions: Effective risk stratification in congenital LQTS can be achieved by clinical history, electrocardiographic indices, and different investigation results, irrespective of underlying genetic defects. A machine learning approach using RSF can improve risk prediction over traditional Cox regression models. |
| format | Article |
| id | doaj-art-3ee884a754f443d7ba8815f7623d8322 |
| institution | Kabale University |
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| language | English |
| publishDate | 2021-02-01 |
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| spelling | doaj-art-3ee884a754f443d7ba8815f7623d83222024-11-28T13:05:23ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2021-02-01810.3389/fcvm.2021.608592608592RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox AnalysesGary Tse0Gary Tse1Sharen Lee2Jiandong Zhou3Tong Liu4Ian Chi Kei Wong5Ian Chi Kei Wong6Chloe Mak7Ngai Shing Mok8Kamalan Jeevaratnam9Qingpeng Zhang10Shuk Han Cheng11Wing Tak Wong12Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, ChinaFaculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomLaboratory of Cardiovascular Physiology, Li Ka Shing Institute of Health Sciences, Hong Kong, ChinaSchool of Data Science, City University of Hong Kong, Hong Kong, ChinaFaculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomCentre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, ChinaSchool of Pharmacy, University College London, London, United KingdomDepartment of Pathology, Hong Kong Children's Hospital, Hong Kong, ChinaDepartment of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong, ChinaFaculty of Health and Medical Sciences, University of Surrey, Guildford, United KingdomSchool of Data Science, City University of Hong Kong, Hong Kong, ChinaDepartment of Biomedical Sciences, City University of Hong Kong, Hong Kong, China0State Key Laboratory of Agrobiotechnology, School of Life Sciences, Chinese University of Hong Kong, Hong Kong, ChinaIntroduction: Congenital long QT syndrome (LQTS) is a cardiac ion channelopathy that predisposes affected individuals to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). The main aims of the study were to: (1) provide a description of the local epidemiology of LQTS, (2) identify significant risk factors of ventricular arrhythmias in this cohort, and (3) compare the performance of traditional Cox regression with that of random survival forests.Methods: This was a territory-wide retrospective cohort study of patients diagnosed with congenital LQTS between 1997 and 2019. The primary outcome was spontaneous VT/VF.Results: This study included 121 patients [median age of initial presentation: 20 (interquartile range: 8–44) years, 62% female] with a median follow-up of 88 (51–143) months. Genetic analysis identified novel mutations in KCNQ1, KCNH2, SCN5A, ANK2, CACNA1C, CAV3, and AKAP9. During follow-up, 23 patients developed VT/VF. Univariate Cox regression analysis revealed that age [hazard ratio (HR): 1.02 (1.01–1.04), P = 0.007; optimum cut-off: 19 years], presentation with syncope [HR: 3.86 (1.43–10.42), P = 0.008] or VT/VF [HR: 3.68 (1.62–8.37), P = 0.002] and the presence of PVCs [HR: 2.89 (1.22–6.83), P = 0.015] were significant predictors of spontaneous VT/VF. Only initial presentation with syncope remained significant after multivariate adjustment [HR: 3.58 (1.32–9.71), P = 0.011]. Random survival forest (RSF) model provided significant improvement in prediction performance over Cox regression (precision: 0.80 vs. 0.69; recall: 0.79 vs. 0.68; AUC: 0.77 vs. 0.68; c-statistic: 0.79 vs. 0.67). Decision rules were generated by RSF model to predict VT/VF post-diagnosis.Conclusions: Effective risk stratification in congenital LQTS can be achieved by clinical history, electrocardiographic indices, and different investigation results, irrespective of underlying genetic defects. A machine learning approach using RSF can improve risk prediction over traditional Cox regression models.https://www.frontiersin.org/articles/10.3389/fcvm.2021.608592/fulllong QT syndromerisk stratificationgenetic variantsmachine learningrandom survival forest |
| spellingShingle | Gary Tse Gary Tse Sharen Lee Jiandong Zhou Tong Liu Ian Chi Kei Wong Ian Chi Kei Wong Chloe Mak Ngai Shing Mok Kamalan Jeevaratnam Qingpeng Zhang Shuk Han Cheng Wing Tak Wong RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses Frontiers in Cardiovascular Medicine long QT syndrome risk stratification genetic variants machine learning random survival forest |
| title | RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses |
| title_full | RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses |
| title_fullStr | RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses |
| title_full_unstemmed | RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses |
| title_short | RETRACTED: Territory-Wide Chinese Cohort of Long QT Syndrome: Random Survival Forest and Cox Analyses |
| title_sort | retracted territory wide chinese cohort of long qt syndrome random survival forest and cox analyses |
| topic | long QT syndrome risk stratification genetic variants machine learning random survival forest |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2021.608592/full |
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