Applying the UTAUT2 framework to patients' attitudes toward healthcare task shifting with artificial intelligence.

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.

Automated quantitative assessment of cardiovascular magnetic resonance-derived atrioventricular junction velocities.

The assessment of atrioventricular junction (AVJ) deformation plays an important role in evaluating left ventricular systolic and diastolic function in clinical practice. This study aims to demonstrate the effectiveness and consistency of cardiovascular magnetic resonance (CMR) for quantitative assessment of AVJ velocity compared with tissue Doppler echocardiography (TDE). A group of 145 human subjects comprising 21 healthy volunteers, 8 patients with heart failure, 17 patients with hypertrophic cardiomyopathy, 52 patients with myocardial infarction, and 47 patients with repaired Tetralogy of Fallot were prospectively enrolled and underwent TDE and CMR scan. Six AVJ points were tracked with three CMR views. The peak systolic velocity (Sm1), diastolic velocity during early diastolic filling (Em), and late diastolic velocity during atrial contraction (Am) were extracted and analyzed. All CMR-derived septal and lateral AVJ velocities correlated well with TDE measurements (Sm1: r = 0.736; Em: r = 0.835; Am: r = 0.701; Em/Am: r = 0.691; all p < 0.001) and demonstrated excellent reproducibility [intrastudy: r = 0.921-0.991, intraclass correlation coefficient (ICC): 0.918-0.991; interstudy: r = 0.900-0.970, ICC: 0.887-0.957; all p < 0.001]. The evaluation of three-dimensional AVJ motion incorporating measurements from all views better differentiated normal and diseased states [area under the curve (AUC) = 0.918] and provided further insights into mechanical dyssynchrony diagnosis in HF patients (AUC = 0.987). These findings suggest that the CMR-based method is feasible, accurate, and consistent in quantifying the AVJ deformation, and subsequently in diagnosing systolic and diastolic cardiac dysfunction.

Endothelial function is associated with myocardial diastolic function in women with systemic lupus erythematosus.

Endothelial dysfunction is associated with traditional and systemic lupus erythematosus (SLE)-specific risk factors, and early data suggest reversibility of endothelial dysfunction with therapy. The clinical relevance of endothelial function assessment has been limited by the lack of studies, demonstrating its prognostic significance and impact on early myocardial function. Therefore, we aimed to determine the association between endothelial and myocardial diastolic function in SLE women. Women with SLE and no coronary artery disease were prospectively recruited and underwent radionuclide myocardial perfusion imaging (MPI) (Jetstream, Philips, the Netherlands) to exclude subclinical myocardial ischemia. Cardiac and vascular functions were assessed in all patients (Alpha 10, Aloka, Tokyo). Diastolic function was assessed using pulse wave early (E) and late mitral blood inflow and myocardial tissue Doppler (mean of medial and lateral annulus e') velocities. Endothelial function was measured using brachial artery flow-mediated vasodilatation (FMD%). Univariate and multivariate linear regressions were used to assess the association between FMD% and myocardial diastolic function, adjusting for potential confounders. Thirty-eight patients without detectable myocardial ischemia on MPI were studied (mean age 44 ± 10 years; mean disease duration 14 ± 6 years). About 61 % of patients had normal diastolic function (E/e' ≤ 8), and 5 % of patients had definite diastolic dysfunction with E/e' > 13 (mean 7.1 ± 2.9). FMD% was associated with E/e' (regression coefficient ß = -0.35; 95 % CI -0.62 to -0.08; p = 0.01) independent of systolic blood pressure, age, and SLICC/ACR Damage Index.

Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).

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) < 50%. Symptomatic patients with suspected HF (N = 100, mean age 61 ± 15 years, 56% men) were prospectively recruited. Novices with no prior echo experience underwent 2-weeks' training to acquire echo images with AI guidance using the EchoNous Kosmos handheld echo, with AI-automated reporting by Us2.ai (AI-enhanced novice pathway). All patients also had standard echo by trained sonographers interpreted by cardiologists (reference standard). LVEF < 50% by reference standard was present in 27 patients. AI-enhanced novice pathway yielded interpretable results in 96 patients and took a mean of 12 min 51 s per study. The area under the curve (AUC) of the AI novice pathway was 0.880 (95% CI 0.802, 0.958). The sensitivity, specificity, positive predictive and negative predictive values of the AI-enhanced novice pathway in detecting LVEF < 50% were 84.6%, 91.4%, 78.5% and 94.1% respectively. The median absolute deviation of the AI-novice pathway LVEF from the reference standard LVEF was 6.03%. AI-enhanced novice pathway holds potential to task shift echo beyond tertiary centres and improve the HF diagnostic workflow.

Potential usefulness and clinical relevance of a novel left atrial filling index to estimate left ventricular filling pressures in patients with preserved left ventricular ejection fraction.

AIMS: The aim of this study was to examine the potential usefulness and clinical relevance of a novel left atrial (LA) filling index using 2D speckle-tracking transthoracic echocardiography to estimate left ventricular (LV) filling pressures in patients with preserved LV ejection fraction (LVEF). METHODS AND RESULTS: The LA filling index was calculated as the ratio of the mitral early-diastolic inflow peak velocity (E) over LA reservoir strain (i.e. E/LA strain ratio). This index showed a good diagnostic performance to determine elevated LV filling pressures in a test-cohort (n = 31) using invasive measurements of LV end-diastolic pressure (area under the curve 0.82, cut-off > 3.27 = sensitivity 83.3%, specificity 78.9%), which was confirmed in a validation-cohort (patients with cardiovascular risk factors; n = 486) using the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging criteria (cut-off > 3.27 = sensitivity 88.1%, specificity 77.6%) and in a specificity-validation cohort (patients free of cardiovascular risk factors, n = 120; cut-off > 3.27 = specificity 98.3%). Regarding the clinical relevance of the LA filling index, an elevated E/LA strain ratio (>3.27) was significantly associated with the risk of heart failure hospitalization at 2 years (odds ratio 4.3, 95% confidence interval 1.8-10.5), even adjusting this analysis by age, sex, renal failure, LV hypertrophy, or abnormal LV global longitudinal systolic strain. CONCLUSION: The findings from this study suggest that a novel LA filling index using 2D speckle-tracking echocardiography could be of potential usefulness and clinical relevance in estimating LV filling pressures in patients with preserved LVEF.