The concepts underlying hypertrophic cardiomyopathy (HCM) pathogenesis have evolved greatly over the last 60 years since the pioneering work of the British pathologist Donald Teare, presenting the autopsy findings of “asymmetric hypertrophy of the heart in young adults”. Advances in human genome analysis and cardiac imaging techniques have enriched our understanding of the complex architecture of the malady and shaped the way we perceive the illness continuum. Presently, HCM is acknowledged as “a disease of the sarcomere”, where the relationship between genotype and phenotype is not straightforward but subject to various genetic and nongenetic influences. The focus of this review is to discuss key aspects related to molecular mechanisms and imaging aspects that have prompted genotype–phenotype correlations, which will hopefully empower patient-tailored health interventions.

Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.

Background: Visual estimation (VE) of coronary stenoses is the first step during invasive coronary angiography. The aim of this study was to evaluate the accuracy of VE together with invasive functional assessment (IFA) in defining the functional significance (FS) of coronary stenoses based on the opinion of multiple operators. Methods: Fourteen independent operators visually evaluated 133 coronary lesions which had a previous FFR measurement, indicating the degree of stenosis (DS), FS and IFA intention. We determined the accuracy of FS prediction using several scenarios combining individual and group decision, considering IFA as deemed necessary by the operator or only in intermediate lesions. Results: The accuracy of VE in predicting FS was largely variable between operators (average 66.1%); it improved significantly when IFA was used either as per operator’s opinion (86.3%; p < 0.0001) or only in intermediate DS (82.9; p < 0.0001). There was no significant difference between using IFA per observer’s opinion or only in intermediate DS lesions (p = 0.166). The poorest accuracy of VE for FS was obtained in intermediate DS lesions (59.1%). Conclusions: There are significant inter-observer differences in reporting the degree of DS, while the accuracy of VE prediction of FS is also largely dependent on the operator, and the worst performance is obtained in the evaluation of intermediate DS.

During the coronavirus pandemic 2019 (COVID-19), some studies showed differences in the profile of subjects presenting with acute coronary syndromes as well as in overall mortality due to the delay of presentation and other complications. The purpose of this study was to compare the profile and outcomes, with emphasis on all-cause in-hospital mortality, of ST-elevation myocardial infarction (STEMI) subjects presenting to the emergency department during the pandemic period compared with a control group from the previous year, 2019. The study enrolled 2011 STEMI cases, which were divided into two groups—pre-pandemic (2019–2020) and pandemic period (2020–2022). Hospital admissions for a STEMI diagnosis sharply decreased during the COVID-19 period by 30.26% during the first year and 25.4% in the second year. This trend was paralleled by a significant increase in all-cause in-hospital mortality: 11.5% in the pandemic period versus 8.1% in the previous year. There was a significant association between SARS-CoV-2 positivity and all-cause in-hospital mortality, but no correlation was found between COVID-19 diagnosis and the type of revascularization. However, the profile of subjects presenting with STEMI did not change over time during the pandemic; their demographic and comorbid characteristics remained similar.

(1) Background: Acute ST-segment elevation myocardial infarction (STEMI) remains one of the main morbidity and mortality contributors worldwide. Its main treatment, primary percutaneous coronary intervention (pPCI), can only be performed with a high anticoagulation regimen, usually with heparin. There is still not enough evidence regarding the timing of heparin administration. (2) Methods: We conducted a multicenter observational study of 614 consecutive STEMI patients treated between 2017 and 2019. We split the population in two groups: one that received heparin at the first medical contact, as early as possible, and the second group that received heparin at the PCI capable center or in the cath lab. (3) Results: There was a significantly higher rate of infarct-related artery (IRA) patency at the time of the coronary angiogram in the pre-transfer heparin group than in the on-site heparin group, 44.7% vs. 37.3%, p = 0.042. Also, the early heparin group received shorter and wider stents. There was no difference in bleeding rates or in the in-hospital and two-year mortality rates. (4) Conclusions: Early administration of heparin leads to a higher rate of reperfusion in the IRA, before pPCI, with significant related benefits, such as better stent implantation parameters, without increased bleeding rates.

Residual inflammatory risk after acute coronary syndromes (ACSs) remains a critical contributor to atherosclerosis progression and plaque destabilization. Inflammatory biomarkers such as interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) may provide additional insights into coronary lesion complexity and vulnerability. The main aim of the study was to evaluate the association of interleukin-1 receptor antagonist (IL-1ra), resistin, and C-reactive protein (CRP) with coronary disease extent; functional significance of non-culprit lesions, assessed by fractional flow reserve (FFR); and plaque vulnerability, assessed by optical coherence tomography (OCT) in patients with acute coronary syndrome (ACS). This prospective study enrolled 93 ACS patients undergoing invasive coronary assessment for an ACS. Inflammatory biomarkers were measured at admission and 6 months post-event. Patients were stratified post hoc into tertiles by biomarker distribution. SYNTAX score, FFR, and OCT-defined thin-cap fibroatheroma (TCFA) were used to characterize lesion burden and morphology. Multivariate logistic regression was performed adjusting for conventional cardiovascular risk factors and ACS type. Higher tertiles of IL-1ra, resistin, and CRP were significantly associated with increased SYNTAX score (p < 0.05), FFR < 0.80 (68% in the highest tertile), and presence of TCFA (62% vs. 20%, p < 0.01). All biomarkers correlated with coronary disease severity. In multivariate logistic models, IL-1ra (OR 1.23 per 100 pg/mL, p = 0.03), resistin (OR 2.35 per 1 ng/mL, p = 0.001), and CRP (OR 1.11 per 0.001 ng/mL, p = 0.006) independently predicted high-risk coronary profiles. IL-1ra, resistin, and CRP are independently associated with lesion complexity, functional significance, and vulnerability in ACS. Inflammatory biomarker profiling may provide complementary anatomical and physiological assessment in future ACS risk stratification strategies.