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

Ju, Chengsheng; Lai, Rachel Wing Chuen; Li, Ka Hou Christien; Hung, Joshua Kai Fung; Lai, Jenny Chi Ling; Ho, Jeffery; Liu, Yingzhi; Tsoi, Man Fung; Liu, Tong; Cheung, Bernard Man Yung; Wong, Ian Chi Kei; Tam, Lai Shan; Tse, Gary

doi:10.1093/rheumatology/kez576

Cited by 66 publications

(56 citation statements)

References 43 publications

(56 reference statements)

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“…This has previously been used by local teams for conducting research studies. [15][16][17] The following clinical details were obtained: age, gender, smoking status, diabetes mellitus, hypercholesterolaemia, coronary artery disease, hypertension, baseline atrial fibrillation (AF), and anti-hypertensive and anti-coagulation medications. Complete blood count was performed using automated haematology analysers.…”

Section: Study Population Baseline Characteristics and Laboratory Imentioning

confidence: 99%

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

Tse

Zhou

Woo³

et al. 2020

ESC Heart Failure

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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.

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Section: Study Population Baseline Characteristics and Laboratory Imentioning

confidence: 99%

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

Tse

Zhou

Woo³

et al. 2020

ESC Heart Failure

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“…Some of the subsequent studies however have shown variable outcomes. A recent observational study comparing febuxostat and allopurinol with controls showed a similar incidence of major adverse CV events in both groups, while patients receiving febuxostat had a lower incidence of HF hospitalization [50]. Further information from the LEAF-CHF (Effect of urate LowEring Agent Febuxostat in Chronic Heart Failure patients with hyperuricemia) trial, in which patients with HF (left ventricular ejection fraction < 40%) and hyperuricemia (7.0-10.0 mg/day) are randomized to febuxostat or placebo, with a primary endpoint of difference in plasma B-type natriuretic peptide (BNP) from baseline to 24 weeks, may help elucidate the effect of febuxostat in HF [51].…”

Section: Febuxostatmentioning

confidence: 94%

Gout Pharmacotherapy in Cardiovascular Diseases: A Review of Utility and Outcomes

Kaul

Gupta

Bandyopadhyay

et al. 2020

Am J Cardiovasc Drugs

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Hyperuricemia and gout have been linked to an increased risk for cardiovascular (CV) disease, stroke, hypertension, heart failure, and chronic kidney disease, possibly through a proinflammatory milieu. However, not all the drugs used in gout treatment improve CV outcomes; colchicine has shown improved CV outcomes in patients with recent myocardial infarction and stable coronary artery disease independent of lipid-lowering effects. There is resurging interest in colchicine following publication of the COLCOT, LoDoCo, LoDoCo2, LoDoCo-MI trials, and COLCORONA trial which will shed light on its utility in COVID-19. Our aim is to review the CV use of colchicine beyond pericardial diseases, as well as CV outcomes of the available gout therapies, including allopurinol and febuxostat. The CARES trial and its surrounding controversies, which lead to the US FDA ‘black box’ warning on febuxostat, in addition to the recent FAST trial which contradicts this and finds febuxostat to be non-inferior, are discussed in this paper.

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“…This is a retrospective cohort study nested within the territory-wide Clinical Data Analysis and Reporting System (CDARS), an electronic health record system managed by the Hong Kong Hospital Authority since 1995. The database included over 7 million Hong Kong residents and has been used for producing high-quality clinical studies (26, 27). Patient information was de-identified with pseudo-identity numbers.…”

Section: Methodsmentioning

confidence: 99%

Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

Zhou

Lee

et al. 2021

Preprint

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ObjectiveFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.MethodsThis was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo’s Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Comparisons were made with decision tree and multivariate logistic regression.ResultsA total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality).ConclusionsThe electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

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

Cited by 66 publications

References 43 publications

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

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

Gout Pharmacotherapy in Cardiovascular Diseases: A Review of Utility and Outcomes

Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

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