Hearts

Background: Congenital heart disease (CHD) is associated with an increased risk of anxiety and depression in adults. However, little is known about the mental health of children and adolescents with CHD. The aim of this study was to assess differences in anxiety and depression symptoms between children and adolescents with CHD and healthy controls. Methods: A total of 232 children and adolescents (age 7–18 years; mean age 13.5 ± 2.7 years, 50.9% female) were enrolled, consisting of 116 patients with CHD and 116 age- and sex-matched healthy controls. Participants were recruited during routine medical examinations at the German Heart Center and Munich schools, respectively. The Beck Anxiety Inventory (BAI) and the Depression Inventory for Youth (BDI-Y) were used to assess anxiety and depression symptoms. Results: The CHD cohort included patients with right heart obstruction (11.2%), left heart obstruction (19.8%), isolated shunts (15.5%), transposition of the great arteries (14.7%), univentricular heart (14.7%), and other defects (24.1%). According to published cut-off values, at least a mild form of anxiety was present in 46.5% CHD patients. However, no significant differences were observed between the CHD group and healthy controls in either the BDI-Y score (CHD: 7.9 ± 7.7 vs. controls: 8.6 ± 8.5; p = 0.569) or the BAI score (CHD: 9.3 ± 8.6 vs. controls: 9.3 ± 10.3; p = 0.429). The complexity of the heart defect was not associated with BAI scores (simple: 5.9 ± 5.7; moderate: 11.1 ± 8.1; complex: 9.3 ± 9.0; p = 0.073) or BDI-Y scores (simple: 7.4 ± 7.5; moderate: 9.0 ± 7.1; complex: 7.0 ± 7.7; p = 0.453). No significant differences in BAI (p = 0.141) or BDI-Y (p = 0.326) scores were found by type of heart defect. Conclusions: Children and adolescents with CHD did not exhibit significantly higher levels of depression or anxiety symptoms compared to healthy controls. Nevertheless, given the increased psychological risk observed in adults with CHD, ongoing mental health monitoring remains important to enable early identification and timely intervention. Further research, particularly through longitudinal studies, is needed to monitor mental health trajectories over time and to identify early predictors of psychological vulnerability in this population.

Background/Objectives: Heart failure (HF) often develops from a prolonged asymptomatic phase where early detection could prevent progression. Pre-heart failure (pre-HF) populations—those with risk factors (Stage A) or subclinical myocardial changes (Stage B)—are critical for intervention. Cardiac magnetic resonance (CMR) with T1 and extracellular volume (ECV) mapping offers a non-invasive approach to detect early myocardial changes in these groups. This systematic review evaluates the role of T1 and ECV mapping in pre-HF populations, focusing on their diagnostic and prognostic utility. Methods: A systematic search of PubMed, EMBASE, and Cochrane was conducted up to April 2025, identifying 17 studies that met inclusion criteria. Data was extracted directly into Excel, and methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) for cohort and cross-sectional studies and AMSTAR-2 for systematic reviews and meta-analyses. A meta-analysis was performed using Review Manager (RevMan) to compare T1 and ECV values between pre-HF and control groups. Results: Studies consistently reported elevated T1 (989.6–1415.41 milliseconds) and ECV (25.7–42.81%) in pre-HF groups compared to controls (T1: 967–1310.63 ms, ECV: 23.5–29.9%). Meta-analysis showed a significant increase in T1 (MD: 27.62 ms, 95% CI: 8.04–47.19, p < 0.006) and ECV (MD: 2.97%, 95% CI: 1.88–4.06, p < 0.00001) in pre-HF groups. RQS scores ranged from 17.2% to 77.8% (mean: 37.9%), and NOS scores ranged from 5 to 8 (mean: 6.2), reflecting variability in study quality. The AMSTAR-2 rating for the systematic review was moderate. Conclusions: T1 and ECV mapping enhance CMR-based detection of early myocardial changes in pre-HF, offering a promising non-invasive approach to predict HF risk. However, variability in study quality, small sample sizes, and methodological inconsistencies limit generalisability. Future research should focus on standardised protocols, prospective designs, and multi-center studies to integrate these techniques into clinical practice, potentially guiding preventive therapies such as SGLT2is and tafamidis.

Background/Objectives: Invasive coronary angiography (ICA) is the gold standard for the diagnosis of coronary artery disease (CAD), with various non-invasive imaging modalities also available. Machine learning (ML) methods are increasingly applied to overcome the limitations of diagnostic imaging by improving accuracy and observer independent performance. Methods: This meta-analysis (PRISMA method) summarizes the evidence for ML-based analyses of coronary imaging data from ICA, coronary computed tomography angiography (CT), and nuclear stress perfusion imaging (SPECT) to predict clinical outcomes and performance for precise diagnosis. We searched for studies from Jan 2012–March 2023. Study-reported c index values and 95% confidence intervals were used. Subgroup analyses separated models by outcome. Combined effect sizes using a random-effects model, test for heterogeneity, and Egger’s test to assess publication bias were considered. Results: In total, 46 studies were included (total subjects = 192,561; events = 31,353), of which 27 had sufficient data. Imaging modalities used were CT (n = 34), ICA (n = 7) and SPECT (n = 5). The most frequent study outcome was detection of stenosis (n = 11). Classic deep neural networks (n = 12) and convolutional neural networks (n = 7) were the most used ML models. Studies aiming to diagnose CAD performed best (0.85; 95% CI: 82, 89); models aiming to predict clinical outcomes performed slightly lower (0.81; 95% CI: 78, 84). The combined c-index was 0.84 (95% CI: 0.81–0.86). Test of heterogeneity showed a high variation among studies (I² = 97.2%). Egger’s test did not indicate publication bias (p = 0.485). Conclusions: The application of ML methods to diagnose CAD and predict clinical outcomes appears promising, although there is lack of standardization across studies.