Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation

Lee³

et al. 2021

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BackgroundArrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterized by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD). The present study aims to investigate the risk of detrimental cardiovascular events in an Asian population of ARVC/D patients, including the incidence of malignant ventricular arrhythmias, new-onset heart failure with reduced ejection fraction (HFrEF), as well as long-term mortality.Methods and ResultsThis was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019 in Hong Kong. This study consisted of 109 ARVC/D patients (median age: 61 [46-71] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariable Cox regression identified prolonged QRS duration as a predictor of VT/VF (p < 0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (P < 0.05. The presence of epsilon waves, in addition to the parameters of prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p<0.05). Clinical scores were developed to predict incident VT/VF, new-onset HFrEF and all-cause mortality, and all were significantly improved by machine learning techniques.ConclusionClinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should in turn be used in tandem to aid risk stratification in the hospital setting.

Section: Discussionmentioning

confidence: 96%

A territory-wide study of arrhythmogenic right ventricular cardiomyopathy patients from Hong Kong

Lakhani¹,

Lee³

et al. 2021

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“…43 RSF-based models have been applied to enhance risk stratification in different clinical settings, including diabetes. [44][45][46][47][48] However, RSF model has been criticized for the bias due to favouring covariates with many split-points. 49 In our study, the CISF model was used for time-toevent survival data analysis in predicting AMI and non-AMI SCD, 11,12 which were shown to shown superior predictive performance compared to RSF and multivariate Cox models.…”

Section: Discussionmentioning

confidence: 99%

“…RSF models, as extensions of classification and regression trees and random forests, have been identified as alternative survival data analysis methods when the proportional hazard assumption is violated 43 . RSF‐based models have been applied to enhance risk stratification in different clinical settings, including diabetes 44‐48 . However, RSF model has been criticized for the bias due to favouring covariates with many split‐points 49 .…”

Section: Discussionmentioning

confidence: 99%

Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

Lee¹,

Endocrino Diabet & Metabol

Guo

et al. 2021

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Introduction:The present study evaluated the application of incorporating non-linear J/U-shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non-AMI-related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus.Methods: This was a territory-wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti-diabetic agents between January 1st, 2009 to December 31st, 2009 at government-funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non-AMI-related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model. Results:This study included 261 308 patients (age = 66.0 ± 11.8 years old, male = 47.6%, follow-up duration = 3552 ± 1201 days, diabetes duration = 4.77 ± 2.29 years). Mean HbA1c and low high-density lipoprotein-cholesterol (HDL-C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL-C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J-shaped relationship with non-AMI-related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models. Conclusion:A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…The application of RSF was able to identify the most important clinical and electrocardiographic predictors, which are in line with those reported by existing registries. Previously, RSF has been reported to enhance the model's performance for predicting sudden arrhythmic deaths in patients with left ventricular ejection fraction <=35% [41], ischaemic heart disease [42] and ventricular tachyarrhythmias in congenital long QT syndrome [43], acquired long QT syndrome [44] and Brugada syndrome [45].…”

Section: Discussionmentioning

confidence: 99%

Arrhythmic Outcomes in Catecholaminergic Polymorphic Ventricular Tachycardia

Lee¹,

Jeevaratnam

et al. 2021

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IntroductionLong QT syndrome (LQTS) and catecholaminergic ventricular tachycardia (CPVT) are less prevalent cardiac ion channelopathies than Brugada syndrome in Asia. The present study compared paediatric/young and adult patients with these conditions.MethodsThis was a territory-wide retrospective cohort study of consecutive patients diagnosed with LQTS and CPVT attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF).ResultsA total of 142 LQTS (mean onset age= 27±23 years old) and 16 CPVT (mean presentation age=11±4 years old) patients were included. For LQTS, arrhythmias other than VT/VF (HR=4.67, 95% confidence interval=[1.53-14.3], p=0.007), initial VT/VF (HR=3.25 [1.29-8.16], p=0.012) and Schwartz score (HR=1.90 [1.11-3.26], p=0.020) were predictive of the primary outcome for the overall cohort, whilst arrhythmias other than VT/VF (HR=5.41 [1.36-21.4], p=0.016) and Schwartz score (HR=4.67 [1.48-14.7], p=0.009) were predictive for the adult subgroup (>25 years old; n=58). All CPVT patients presented before the age of 25 but no significant predictors of VT/VF were identified. A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic, and arrhythmias other than VT/VF as the most important variables for risk prediction in LQTS, and initial VT/VF/sudden cardiac death, palpitations, QTc, initially symptomatic and heart rate in CPVT.ConclusionClinical and ECG presentation vary between the pediatric/young and adult LQTS population. All CPVT patients presented before the age of 25. Machine learning models achieved more accurate VT/VF prediction.