Left ventricular hypertrophy (LVH) refers to a complex rebuilding of the left ventricle that can gradually lead to serious complications—heart failure and life-threatening ventricular arrhythmias. LVH is defined as an increase in the size of the left ventricle (i.e., anatomically), therefore the basic diagnosis detecting the increase in the LV size is the domain of imaging methods such as echocardiography and cardiac magnetic resonance. However, to evaluate the functional status indicating the gradual deterioration of the left ventricular myocardium, additional methods are available approaching the complex process of hypertrophic remodeling. The novel molecular and genetic biomarkers provide insights on the underlying processes, representing a potential basis for targeted therapy. This review summarizes the spectrum of the main biomarkers employed in the LVH valuation.

Aims: Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality rates approaching 50%. The ability to identify high-risk patients prior to the development of CS may allow for pre-emptive measures to prevent the development of CS. The objective was to derive and externally validate a simple, machine learning (ML)-based scoring system using variables readily available at first medical contact to predict the risk of developing CS during hospitalization in patients with ACS. Methods and results: Observational multicentre study on ACS patients hospitalized at intensive care units. Derivation cohort included over 40 000 patients from Beth Israel Deaconess Medical Center, Boston, USA. Validation cohort included 5123 patients from the Sheba Medical Center, Ramat Gan, Israel. The final derivation cohort consisted of 3228 and the final validation cohort of 4904 ACS patients without CS at hospital admission. Development of CS was adjudicated manually based on the patients’ reports. From nine ML models based on 13 variables (heart rate, respiratory rate, oxygen saturation, blood glucose level, systolic blood pressure, age, sex, shock index, heart rhythm, type of ACS, history of hypertension, congestive heart failure, and hypercholesterolaemia), logistic regression with elastic net regularization had the highest externally validated predictive performance (c-statistics: 0.844, 95% CI, 0.841–0.847). Conclusion: STOP SHOCK score is a simple ML-based tool available at first medical contact showing high performance for prediction of developing CS during hospitalization in ACS patients. The web application is available at https://stopshock.org/#calculator.

Aims The aim of this pilot study was to compare selected three‐dimensional speckle tracking echocardiography (3D STE) parameters in patients with ischaemic and non‐ischaemic aetiology of heart failure (HF) and to identify indices that can differentiate the two pathologies. Methods and results Forty patients with left ventricular ejection fraction (LVEF) ≤ 40% were included to the study: 20 patients (age 63 ± 9.0 years, LVEF 29.0 ± 11.3%) with ischaemic cardiomyopathy and 20 patients (age 64.0 ± 11.0 years, LVEF 27.3 ± 7.5%) with non‐ischaemic cardiomyopathy. All patients underwent two‐dimensional (2D) and three‐dimensional (3D) transthoracic echocardiography. Standard echocardiographic parameters, global longitudinal strain, and rotational parameters of left ventricle (LV) were assessed using 3D speckle tracking (3D STE). There were no differences in standard and STE parameters between the two groups. Among rotational parameters, the LV apical rotation (4.9 ± 3.5° vs. 2.3 ± 2.4°, P = 0.0022) was significantly higher in patients with ischaemic HF. Among all echocardiographic parameters, a cut‐off value of 3.28° (area under the curve 0.78; 95% confidence interval, 0.62 to 0.93) was able to distinguish the ischaemic and non‐ischaemic aetiology of HF with a sensitivity of 80% and specificity of 75%. Conclusions This is the first study that compares 3D STE parameters between patients with ischaemic and non‐ischaemic cardiomyopathy. It was proved that the apical rotation was significantly higher in patients with ischaemic cardiomyopathy. Our findings suggest that 3D STE might be useful in non‐invasive differentiation between ischaemic and non‐ischaemic aetiology of HF.

Micro ribonucleic acids (miRNAs) are short non-coding RNA molecules responsible for regulation of gene expression. They are involved in many pathophysiological processes of a wide spectrum of diseases. Recent studies showed their involvement in atrial fibrillation. They seem to become potential screening biomarkers for atrial fibrillation and even treatment targets for this arrhythmia. The aim of this review article was to summarize the latest knowledge about miRNA and their molecular relation to the pathophysiology, diagnosis and treatment of atrial fibrillation.

Background: Atrial fibrillation (AF) is associated with high risk of stroke preventable by timely initiation of anticoagulation. Currently available screening tools based on ECG are not optimal due to inconvenience and high costs. Aim of this study was to study the diagnostic value of apelin for AF in patients with high risk of stroke.Methods: We designed a multicenter, matched-cohort study. The population consisted of three study groups: a healthy control group (34 patients) and two matched groups of 60 patients with high risk of stroke (AF and non-AF group). Apelin levels were examined from peripheral blood.Results: Apelin was significantly lower in AF group compared to non-AF group (0.694 ± 0.148 vs. 0.975 ± 0.458 ng/ml, p = 0.001) and control group (0.982 ± 0.060 ng/ml, p < 0.001), respectively. Receiver operating characteristic (ROC) analysis of apelin as a predictor of AF scored area under the curve (AUC) of 0.658. Apelin's concentration of 0.969 [ng/ml] had sensitivity = 0.966 and specificity = 0.467. Logistic regression based on manual feature selection showed that only apelin and NT-proBNP were independent predictors of AF. Logistic regression based on selection from bivariate analysis showed that only apelin was an independent predictor of AF. A logistic regression model using repeated stratified K-Fold cross-validation strategy scored an AUC of 0.725 ± 0.131.Conclusions: Our results suggest that apelin might be used to rule out AF in patients with high risk of stroke.