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Paediatric cardiomyopathies form a heterogeneous group of disorders characterized by structural and electrical abnormalities of the heart muscle, commonly due to a gene variant of the myocardial cell structure. Mostly inherited as a dominant or occasionally recessive trait, they might be part of a syndromic disorder of underlying metabolic or neuromuscular defects or combine early developing extracardiac abnormalities (i.e., Naxos disease). The annual incidence of 1 per 100,000 children appears higher during the first two years of life. Dilated and hypertrophic cardiomyopathy phenotypes share an incidence of 60% and 25%, respectively. Arrhythmogenic right ventricular cardiomyopathy (ARVC), restrictive cardiomyopathy, and left ventricular noncompaction are less commonly diagnosed. Adverse events such as severe heart failure, heart transplantation, or death usually appear early after the initial presentation. In ARVC patients, high-intensity aerobic exercise has been associated with worse clinical outcomes and increased penetrance in at-risk genotype-positive relatives. Acute myocarditis in children has an incidence of 1.4–2.1 cases/per 100,000 children per year, with a 6–14% mortality rate during the acute phase. A genetic defect is considered responsible for the progression to dilated cardiomyopathy phenotype. Similarly, a dilated or arrhythmogenic cardiomyopathy phenotype might emerge with an episode of acute myocarditis in childhood or adolescence. This review provides an overview of childhood cardiomyopathies focusing on clinical presentation, outcome, and pathology.

Hypertrophic cardiomyopathy (HCM) is a heart muscle disease associated with an increased risk for sudden cardiac death (SCD). Cytokeratin 18-based proteins, such as M30 and M65 antigens, are known cell-death biomarkers. M30 antigen is released from cells during apoptosis, and M65 antigen is released during cell death from any cause, such as apoptosis or necrosis. We aimed to study the expression of M30 and M65 antigens in peripheral blood obtained by 46 HCM patients and compare with 27 age- and sex-matched patients without HCM. We also investigated the CK18 expression in myocardium from postmortem HCM hearts. M30 and M65 antigens were significantly increased in the HCM vs. non-HCM group (Μ30: 338 ± 197 U/uL vs. 206 ± 166 U/uL, p = 0.003; M65: 428 ± 224 U/uL vs. 246 ± 214 U/uL, p = 0.001), and HCM patients with a higher expression of these markers (M30: 417 ± 208 vs. 271 ± 162 U/uL, p = 0.011; M65: 518 ± 242 vs. 351 ± 178 U/uL, p = 0.011) had a higher risk for SCD. In HCM, both apoptosis and necrosis are increased, but particularly necrosis (M30/M65 ratio: 0.75 ± 0.09 vs. 0.85 ± 0.02, p < 0.001). CK18 is expressed in the HCM myocardium (1.767 ± 0.412 vs. 0.537 ± 0.383, % of area, p = 0.0058). Therefore, M30 and M65 antigens may be novel biomarkers in HCM.

Stress echocardiography (SE) is a well established and valid technique, widely used for the diagnostic evaluation of patients with ischemic and nonischemic cardiac diseases. This statement of the Echocardiography Working Group of the Hellenic Society of Cardiology summarizes the consensus of the writing group regarding the applications of SE, based on the expertise of their members and on a critical review of present medical literature. The main objectives of the consensus document include a comprehensive review of SE methodology and training—which focus on the preparation, the protocols used, the analysis of the SE images, and updated, evidence-based knowledge about SE applications on ischemic and nonischemic heart diseases, such as in cardiomyopathies, heart failure, and valvular heart disease.

Introduction Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients.Methods and analysis CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques.Ethics and dissemination This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted.Trial registration number NCT05176769.

Introduction: Ticagrelor has been established as the P2Y12-inhibitor of choice in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI); however, its use has not been adequately studied in the context of thrombolysis. In the present study, we sought to investigate whether the administration of ticagrelor together with thrombolysis could result in a greater degree of left ventricular systolic function recovery compared to clopidogrel, at 90 days post-STEMI, as well as to evaluate post-PCI corrected TIMI Frame Count (CTFC) as a predictive marker of myocardial recovery in thrombolysis-treated patients. Material and methods: In this pre-specified analysis of the MIRTOS trial, the degree of change in left ventricular ejection fraction (ΔLVEF) and left ventricular longitudinal strain (ΔLV-GLS) from baseline to 90 days post-randomization in all patients who underwent conventional and speckle-tracking echocardiography at both timepoints was compared between the ticagrelor and clopidogrel groups. In addition, speckle-tracking echocardiographic measurements were evaluated for any correlations to post-PCI CTFC. Results: No statistically significant differences were detected between the ticagrelor and clopidogrel groups for ΔLVEF (+3.61 ± 5.08 % versus +2.21 ± 4.78 %; P = 0.18) and ΔLV-GLS (−1.53 ± 2.7 % versus −1.21 ± 3.05 %; P = 0.73). A strong negative correlation was found between post-PCI CTFC and the absolute value of LV-GLS at 90 days post-randomization (r = −0.33, P = 0.014). Conclusions: Our work suggests that both P2Y12-inhibitors are accompanied with a similar degree of myocardial recovery in the context of lytic therapy. Importantly, post-PCI microvascular integrity is a predictor of 3-month left ventricular systolic function in STEMI patients initially treated with thrombolysis.

Abstract Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). The authors trained supervised ML models to classify patients with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow‐Lyon voltage, QRS‐T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from CR+LVH combined, with 87% accuracy on an unseen test set, 75% specificity, 97% sensitivity, and area under the receiver operating curve (AUC/ROC) equal to 0.91. The authors also trained our model to classify NG and CR (NG + CR) against those with LVH, with 89% test set accuracy, 93% specificity, 67% sensitivity, and an AUC/ROC value of 0.89, for a 0.4 decision threshold. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, and ML may enable progress in this direction.

Abstract Whereas younger female patients were diagnosed with idiopathic pulmonary arterial hypertension (IPAH) in 1980s, it is now frequently encountered in elderly patients with cardiovascular comorbidities (CVCs) associated with increased risk for left heart disease. We present data until November 2019 regarding specific features and clinical outcomes of IPAH population from the Hellenic Pulmonary Hypertension Registry (HOPE). Patients were divided into two groups based on the presence of ≥ or

Current national and international guidelines, including those of the European Society of Cardiology, recognize that the assessment of prognosis should be a part of the standard management for patients with chronic heart failure (CHF). However, these same guidelines recognize the inherent difficulty of this process. A variety of factors contribute to this difficulty, including the varying etiology, frequent co-morbidity and, perhaps most importantly, huge inter-individual variability in the disease progression and outcome. Although CHF is chronic, it is also a condition in which significant proportions of patients experience apparently ‘sudden’ death, which almost certainly contributes to our difficulty in assessing individual patient prognosis. A useful tool for the risk stratification of heart failure patients is dobutamine stress echocardiography (DSE), which determines the myocardial viability in ischemic cardiomyopathy and myocardial contractile reserve in idiopathic cardiomyopathy.

: Aims We sought to evaluate the utility of speckle tracking echocardiography (STE) for detecting left ventricular (LV) mechanical abnormalities during low-dose (20 µg) dobutamine stress (DSE). : Methods and results Twenty-nine patients (56 ± 12 years) with a history of recent acute coronary events (ACE) underwent STE-DSE. Left ventricular images, sampled at frame rates 70–100 Hz, were analysed off-line (Echopac BT 6.0.0). Velocity, strain, and rotational imaging were performed. Twenty patients had LV ejection fraction (EF) >40% (Group 1) whereas nine patients had LVEF <40% (Group 2). Average heart and frame rates were identical during DSE in the two groups (P = ns). Global circumferential strain (%) was significantly lower in Group 2 compared with Group 1 (10.65 ± 5.30 vs. 16.82 ± 6.61; P < 0.05) at rest and during peak stress (14.72 ± 6.51 vs. 21.13 ± 7.2; P < 0.05). The global peak rotation rate (degree/s) was, however, higher at rest in Group 2 (70 ± 97 vs. 19 ± 67; P < 0.05) and 20 µg stress. Peak systolic velocity increased in three of the four LV walls at 20 µg (in Groups 1 and 2). A global rotational rate increased significantly at 20 µg during systole in both the groups, but was unchanged in Group 2 during diastole. : Conclusions Speckle tracking echocardiography dobutamine stress appears to provide comprehensive information on LV mechanical status in the aftermath of ACE. The modality may help risk stratify such patients. [ABSTRACT FROM AUTHOR]