Ventricular and atrial function and deformation is largely preserved after arterial switch operation

Adopting artificial intelligence in cardiovascular medicine: a scoping review

Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.

Right Ventricular Systolic Dysfunction in Adults With Anatomic Repair of d-Transposition of Great Arteries

The purpose of this study was to assess the prevalence of right ventricular (RV) systolic dysfunction in adults with anatomic repair of dextro-transposition of great arteries (d-TGAs), and to determine its relation to clinical outcomes across multiple domains (functional status, peak oxygen consumption, N-terminal pro-brain natriuretic peptide, and heart failure hospitalization). Adults with anatomic repair for d-TGA and with echocardiographic images for strain analysis were divided into 2 groups: (1) d-TGA status after an arterial switch operation (d-TGA-ASO group) and (2) d-TGA status after a Rastelli operation (d-TGA-Rastelli group). RV systolic function was assessed using RV global longitudinal strain (RVGLS), and RV systolic dysfunction was defined as RVGLS >-18%. We identified 151 patients (median age 21 years [19 to 28]; d-TGA-ASO group 89 [59%], and d-TGA-Rastelli group 62 [41%]). The mean RVGLS was -22 ± 4%, and 47 patients (31%) had RV systolic dysfunction. The d-TGA-Rastelli group had lower (less negative) RVGLS than that of the d-TGA-ASO group (-19 ± 3% vs -25 ± 3%, p <0.001) and higher prevalence of RV systolic dysfunction (48% vs 19%, p <0.001). RVGLS (absolute value) was associated with peak oxygen consumption (r = 0.58, p <0.001; adjusted R2 = 0.28), log-N-terminal pro-brain natriuretic peptide (r = -0.41, p = 0.004; adjusted R2 = 0.21), New York Heart Association class III/IV (odds ratio 2.29, 1.56 to 3.19, p = 0.01), and heart failure hospitalization (hazard ratio 0.93, 0.88 to 0.98, p = 0.009). RV systolic dysfunction was common in adults with d-TGA and anatomic repair and was associated with clinical outcomes. Longitudinal studies are required to determine the risk factors for progressive RV systolic dysfunction and to identify strategies for preventing RV systolic dysfunction in this population.