Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that its accessibility in rural areas may be limited, leading to delayed management decisions and potential missed diagnoses. Artificial intelligence-guided (AI)-TTE offers a solution by permitting non-expert image acquisition. The impact of AI-TTE on the timing of diagnosis and early initiation of cardioprotection is undefined. AGILE-Echo (use of Artificial intelligence-Guided echocardiography to assIst cardiovascuLar patient managEment) is a randomized-controlled trial conducted in 5 rural and remote areas around Australia. Adults with CV risk factors and exercise intolerance, or concerns regarding HVD are randomized into AI-TTE or usual care (UC). AI-TTE participants may have a cardiovascular problem excluded, identified (leading to AI-guided interventions) or unresolved (leading to conventional TTE). UC participants undergo usual management, including referral for standard TTE. The primary endpoint is a composite of HVD or HF diagnosis at 12-months. Subgroup analysis, stratified based on age range and sex, will be conducted. All statistical analyses will be conducted using R. Of the first 157 participants, 78 have been randomized into AI-TTE (median age 68 [IQR 17]) and 79 to UC (median age 65 [IQR 17], P = .034). HVD was the primary concern in 37 participants (23.6%) while 84.7% (n = 133) experienced exercise intolerance. The overall 10-year HF incidence risk was 13.4% and 20.0% (P = .089) for UC and AI-TTE arm respectively. Atrial remodeling, left ventricular remodeling and valvular regurgitation were the most common findings. Thirty-three patients (42.3%) showed no abnormalities. This randomized-controlled trial of AI-TTE will provide proof-of-concept for the role of AI-TTE in identifying pre-symptomatic HF or HVD when access to TTE is limited. Additionally, this could promote the usage of AI-TTE in rural or remote areas, ultimately improving health and quality of life of community dwelling adults with risks, signs or symptoms of cardiac dysfunction.

BackgroundThe impact of prosthesis–patient mismatch (PPM) after transcatheter aortic valve replacement (TAVR) is uncertain. This study was performed to investigate the risk of all-cause mortality, heart failure hospitalization, and aortic valve reintervention in patients with and without predicted PPM after TAVR. MethodsThis nationwide, population-based cohort study included all patients who underwent transfemoral primary TAVR in Sweden from 2008 to 2022 in the SWEDEHEART register. PPM was defined according to published effective orifice areas for each valve model and size. The patients were divided into those with and without PPM. Additional baseline characteristics and outcome data were obtained from other national health data registers. Regression standardization was used to adjust for intergroup differences. ResultsOf 8485 patients, 7879 (93%) had no PPM and 606 (7%) had PPM. The crude cumulative incidence of all-cause mortality at 1, 5, and 10 years in patients with versus without PPM was 7% versus 9%, 40% versus 44%, and 80% versus 85%, respectively. After regression standardization, there was no between-group difference in long-term mortality, and the absolute difference at 10 years was 1.5% (95% confidence interval, −2.9%–6.0%). The mean follow-up was 3.0 years (maximum, 14 years). There was no difference in the risk of heart failure hospitalization or aortic valve reintervention. ConclusionsThe risk of all-cause mortality, heart failure hospitalization, or aortic valve reintervention was not higher in patients with than without predicted PPM following TAVR. Furthermore, PPM was present in only 7% of patients, and severe PPM was almost nonexistent.