Comparative cardiovascular risk in users versus non-users of xanthine oxidase inhibitors and febuxostat versus allopurinol users.

OBJECTIVES: The aim of this study is to determine major adverse cardiovascular events (MACE) and all-cause mortality comparing between xanthine oxidase inhibitors (XOIs) and non-XOI users, and between allopurinol and febuxostat. METHODS: This is a retrospective cohort study of gout patients prescribed anti-hyperuricemic medications between 2013 and 2017 using a territory-wide administrative database. XOI users were matched 1:1 to XOI non-users using propensity scores. Febuxostat users were matched 1:3 to allopurinol users. Subgroup analyses were conducted based on colchicine use. RESULTS: Of the 13 997 eligible participants, 3607 (25.8%) were XOI users and 10 390 (74.2%) were XOI non-users. After propensity score matching, compared with non-users (n = 3607), XOI users (n = 3607) showed similar incidence of MACE (hazard ratio [HR]: 0.997, 95% CI, 0.879, 1.131; P>0.05) and all-cause mortality (HR = 0.972, 95% CI 0.886, 1.065, P=0.539). Febuxostat (n = 276) users showed a similar risk of MACE compared with allopurinol users (n = 828; HR: 0.672, 95% CI, 0.416, 1.085; P=0.104) with a tendency towards a lower risk of heart failure-related hospitalizations (HR = 0.529, 95% CI 0.272, 1.029; P=0.061). Concurrent colchicine use reduced the risk for all-cause mortality amongst XOI users (HR = 0.671, 95% 0.586, 0.768; P<0.001). CONCLUSION: In gout patients, XOI users showed similar risk of MACE and all-cause mortality compared with non-users. Compared with allopurinol users, febuxostat users showed similar MACE and all-cause mortality risks but lower heart failure-related hospitalizations.

Multi-parametric system for risk stratification in mitral regurgitation: A multi-task Gaussian prediction approach.

BACKGROUND: We hypothesized that a multi-parametric approach incorporating medical comorbidity information, electrocardiographic P-wave indices, echocardiographic assessment, neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) calculated from laboratory data can improve risk stratification in mitral regurgitation (MR). METHODS: Patients diagnosed with mitral regurgitation between 1 March 2005 and 30 October 2018 from a single centre were retrospectively analysed. Outcomes analysed were incident atrial fibrillation (AF), transient ischemic attack (TIA)/stroke and mortality. RESULTS: This study cohort included 706 patients, of whom 171 had normal inter-atrial conduction, 257 had inter-atrial block (IAB) and 266 had AF at baseline. Logistic regression analysis showed that age, hypertension and mean P-wave duration (PWD) were significant predictors of new-onset AF. Low left ventricular ejection fraction (LVEF), abnormal P-wave terminal force in V1 (PTFV1) predicted TIA/stroke. Age, smoking, hypertension, diabetes mellitus, hypercholesterolaemia, ischemic heart disease, secondary mitral regurgitation, urea, creatinine, NLR, PNI, left atrial diameter (LAD), left ventricular end-diastolic dimension, LVEF, pulmonary arterial systolic pressure, IAB, baseline AF and heart failure predicted all-cause mortality. A multi-task Gaussian process learning model demonstrated significant improvement in risk stratification compared to logistic regression and a decision tree method. CONCLUSIONS: A multi-parametric approach incorporating multi-modality clinical data improves risk stratification in mitral regurgitation. Multi-task machine learning can significantly improve overall risk stratification performance.

Outcomes in Brugada Syndrome Patients With Implantable Cardioverter-Defibrillators: Insights From the SGLT2 Registry.

BACKGROUND AND OBJECTIVES: Brugada syndrome (BrS) is a cardiac ion channelopathy with characteristic electrocardiographic patterns, predisposing affected individuals to sudden cardiac death (SCD). Implantable cardioverter-defibrillator (ICD) is used for primary or secondary prevention in BrS, but its use remains controversial amongst low-risk asymptomatic patients. The present study aims to examine indicators for ICD implantation amongst BrS patients with different disease manifestations. METHODS: This study included BrS patients who received ICDs between 1997 and 2018. The cohort was divided into three categories based on presentations before ICD implantation: asymptomatic, syncope, ventricular tachycardia/ventricular fibrillation (VT/VF). Univariate and multivariate Cox-regression analysis were performed to identify independent predictors of appropriate and inappropriate shock delivery. RESULTS: A total of 136 consecutive patients were included with a median follow-up of 95 (IQR: 80) months. Appropriate shocks were delivered in 34 patients (25.0%) whereas inappropriate shocks were delivered in 24 patients (17.6%). Complications occurred in 30 patients (22.1%). Type 1 Brugada pattern were found to be an independent predictor of appropriate shock delivery, whilst the presence of other arrhythmia was predictive for both appropriate and inappropriate ICD shock delivery under multivariate Cox regression analysis. CONCLUSION: ICD therapy is effective for primary and secondary prevention of SCD in BrS. Whilst appropriate shocks occur more frequently in BrS patients presenting with VT/VF, they also occur in asymptomatic patients. Further research in risk stratification can improve patient prognosis while avoid unnecessary ICD implantation.

Multi-modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45.

AIMS: Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) will improve risk stratification for adverse outcomes in HF compared to logistic regression. METHODS AND RESULTS: Consecutive Chinese patients referred to our centre for transthoracic echocardiography and subsequently diagnosed with HF, between 1 January 2010 and 31 December 2016, were included in this study. Two machine learning techniques, multilayer perceptron and multi-task learning, were compared with logistic regression for their ability to predict incident atrial fibrillation (AF), transient ischaemic attack (TIA)/stroke, and all-cause mortality. This study included 312 HF patients [mean age: 64 (55-73) years, 75% male]. There were 76 cases of new-onset AF, 62 cases of incident TIA/stroke, and 117 deaths during follow-up. Univariate analysis revealed that age, left atrial reservoir strain (LARS) and contractile strain (LACS) were significant predictors of new-onset AF. Age and smoking predicted incident stroke. Age, hypertension, type 2 diabetes mellitus, chronic kidney disease, mitral or aortic regurgitation, P-wave terminal force in V1, the presence of partial inter-atrial block, left atrial diameter, ejection fraction, global longitudinal strain, serum creatinine and albumin, high NLR, low PNI, and LARS and LACS predicted all-cause mortality. Machine learning techniques achieved better prediction performance than logistic regression. CONCLUSIONS: Multi-modality assessment is important for risk stratification in HF. A machine learning approach provides additional value for improving outcome prediction.