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Abstract Aims Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data‐driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. Methods and results Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN‐HF (N = 4868) and BIOSTAT‐CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity‐based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08–0.43] and European registry populations (median 0.17, IQR 0.06–0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985–2005 to 0.19 in 2016–2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. Conclusions Based on 14 trial criteria, only one‐fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.

Abstract The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiography (echo). There is a worldwide shortage of trained sonographers and long waiting times for expert echo. We hypothesised that artificial intelligence (AI)-enhanced point-of-care echo can enable HF screening by novices. The primary endpoint was the accuracy of AI-enhanced novice pathway in detecting reduced LV ejection fraction (LVEF)

Abstract Background Increasing patient loads, healthcare inflation and ageing population have put pressure on the healthcare system. Artificial intelligence and machine learning innovations can aid in task shifting to help healthcare systems remain efficient and cost effective. To gain an understanding of patients’ acceptance toward such task shifting with the aid of AI, this study adapted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), looking at performance and effort expectancy, facilitating conditions, social influence, hedonic motivation and behavioural intention. Methods This was a cross-sectional study which took place between September 2021 to June 2022 at the National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years with at least one heart failure symptom (pedal oedema, New York Heart Association II-III effort limitation, orthopnoea, breathlessness), who presented to the cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers and the experience of echocardiogram by a novice guided by AI technologies. They were then given a survey which looked at the above-mentioned constructs using the UTAUT2 framework. Results Significant, direct, and positive effects of all constructs on the behavioral intention of accepting the AI-novice combination were found. Facilitating conditions, hedonic motivation and performance expectancy were the top 3 constructs. The analysis of the moderating variables, age, gender and education levels, found no impact on behavioral intention. Conclusions These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.

Abstract Aims The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a widely used patient‐reported outcome measure in heart failure (HF). The KCCQ was validated in patients with HF with reduced ejection fraction (HFrEF), leaving knowledge gaps regarding its applicability in HF with preserved ejection fraction (HFpEF). This study addresses the psychometric properties of internal consistency and reliability, construct, and known‐group validity of KCCQ in both HFrEF and HFpEF. We aimed to evaluate the psychometric properties of the KCCQ and their prognostic significance in HFpEF and HFrEF, within a large prospective multinational HF cohort. Methods and results We examined the 23‐item KCCQ in the prospective multinational ASIAN‐HF study [4470 HFrEF (ejection fraction

Abstract Background Air pollution has a significant health impact. Most data originate from temperate regions. We aim to study the health impact of air pollution, particularly among the elderly, in a tropical region. Methods A daily time-series analysis was performed to estimate excess risk (ER) of various air pollutants on daily death counts amongst the general population in Singapore from 2001 to 2013. Air pollutants included particulate matters smaller than 10 μm, and 2.5 μm (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2). The studied outcomes were non-accidental and cardiovascular mortality. Single-day lag and distributed lag models were studied and adjusted for confounders. Results In single-day lag models, a 10 μg/m3 increase in particulate matter was associated with significant increases in non-accidental (PM10 ER: 0.627%; 95% confidence interval (CI): 0.260–0.995% and PM2.5 ER: 0.660%; 95% CI: 0.204–1.118%) and cardiovascular mortality (PM10 ER: 0.897; 95% CI: 0.283–1.516 and PM2.5 ER: 0.883%; 95% CI: 0.121–1.621%). This was significant in the elderly ≥ 65 years but not in those

Background: Few studies have comprehensively evaluated the relationship between vascular disease and cognition of older adults without cardiac disease.Objective: We explored the associations of structural atherosclerosis, vascular stiffness, and reactivity with global, memory, attention, language, visuospatial ability, and executive function in community-dwelling, non-demented older Asians without cardiac diseases.Methods: Cognition was assessed by Mini-Mental State Examination (MMSE) (n = 308) and detailed neuropsychological tests (n = 155). Vascular measures included carotid intima-media thickness; aortic stiffness [carotid-femoral pulse wave velocity (CFPWV), aortic augmentation index (AI), and aortic pulse pressure (PP)]; carotid stiffness [elasticity modulus (Ep), beta index (β), arterial compliance (AC), carotid AI]; and endothelial function [reactive hyperemia index (RHI)]. Multivariable analyses controlled for potential confounding by demographics, apolipoprotein E genotype and cardiovascular risk factors.Results: The participants' mean age was 63.0 ± 6.1 years. Inverse associations with MMSE were found for AC (β= 0.128, p = 0.019), Ep (β= -0.151, p = 0.008), β index (β= -0.122, p = 0.029), carotid stiffness z-score (β= -0.154, p = 0.007); with executive function for CFPWV (β= -0.209, p = 0.026), AC (β= 0.214, p = 0.005), Ep (β= -0.160, p = 0.050), β index (β= -0.165, p = 0.041), and both aortic (β= -0.229, p = 0.010) and carotid (β= -0.208, p = 0.010) stiffness z-scores; with verbal memory for AI (β= -0.229, p = 0.004) and aortic (β= -0.263, p = 0.004) stiffness z-score; with language for AI (β= -0.155, p = 0.025), aortic stiffness z-score (β= -0.196, p = 0.011). RHI positively correlated with visuospatial ability (β= 0.195, p = 0.013) and executive function (β= 0.151, p = 0.045).Conclusion: The results support a link between systemic vascular health and neurocognitive function in older Asian adults. Subclinical noninvasive measures of arterial stiffness and reactivity may identify individuals vulnerable to cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: The role of sex, and its joint effect with age and diabetes mellitus, on mortality subsequent to surviving an acute myocardial infarction (AMI) beyond 30 days are unclear. The high prevalence of diabetes mellitus in an ethnically diverse Asian population motivates this study. Methods: The study population comprised of a nationwide cohort of Asian patients with AMI, hospitalized between 2000 to 2005, who survived the first 30 days post-admission and were followed prospectively until death or 12 years. Results: Among the 13,389 survivors, there were fewer women (25.5%) who were older than men (median 70 vs. 58 years) and a larger proportion had diabetes mellitus at admission (51.4% vs. 31.4%). During follow-up 4,707 deaths (women 13.2%; men 22.0%) occurred, with women experiencing higher mortality than men with an averaged hazard ratio (HR): 2.08; 95% confidence interval : 1.96-2.20. However the actual adverse outcome, although always greater, reduced over time with an estimated HR: 2.23 (2.04-2.45) at 30 days to HR: 1.75; (1.47-2.09) 12 years later. The difference in mortality also declined with increasing age: HR 1.80 (1.52-2.13) for those aged 22-59, 1.26 (1.11-1.42) for 60-69, 1.06 (0.96-1.17) and 0.96 (0.85-1.09) for those 70-79 and 80-101 years. Significant two-factor interactions were observed between sex, age and diabetes (P < 0.001). Diabetic women <60 years of age had greater mortality than diabetic men of the same age (adjusted HR: 1.44; 1.14-1.84; P = 0.003), while diabetic women and men ⩾60 years of age had a less pronounced mortality difference (adjusted HR: 1.12; 0.99-1.26). Conclusions: One in two women hospitalized for AMI in this Asian cohort had diabetes and the sex disparity in post-MI mortality was most pronounced among these who were <60 years of age. This underscores the need for better secondary prevention in this high-risk group. [ABSTRACT FROM AUTHOR]