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.

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Aims Frailty 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. Methods and resultsThis was a retrospective observational study that 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 co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A 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. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

Background and purpose:The aim was to determine trends and patterns of symptomatic medication used against dementia in 66 countries and regions. Methods: This was a cross-sectional study that used the wholesale data from the IQVIA Multinational Integrated Data Analysis System database. Sale data for symptomatic medication against dementia from 66 countries and regions from 2008 to 2018 were analysed and stratified by income level (low/middle-income countries [LMICs], n = 27; high-income countries [HICs], n = 37; regions, n = 2). The medication use volume was estimated by defined daily dose (DDD) per 1000 inhabitants per day (World Health Organization DDD harmonized the size, strength and form of each pack and reflects average dosing). Changes in medication use over time were quantified as percentage changes in compound annual growth rates (CAGRs).Results: Total symptomatic medication against dementia sales increased from 0.85 to 1.33 DDD per 1000 inhabitants per day between 2008 and 2018 (LMICs 0.094-0.396; HICs 3.88-5.04), which is an increase of CAGR of 4.53% per year. The increase was mainly driven by the LMICs (CAGR = 15.42%) in comparison to the HICs (CAGR = 2.65%).The overall medication use from 2008 to 2018 increased for all four agents: memantine (CAGR = 8.51%), rivastigmine (CAGR = 6.91%), donepezil (CAGR = 2.72%) and galantamine (CAGR = 0.695%). In 2018, the most commonly used medication globally was donepezil, contributing to 49.8% of total use volume, followed by memantine (32.7%), rivastigmine (11.24%) and galantamine (6.36%). Conclusion:There was an increasing trend in the use of symptomatic medications against dementia globally, but the use remained low in LMICs. Interventions may be needed to support the medication use in some countries.

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