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.

Introduction: Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.Methods: We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)—based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.Results: We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.Conclusion: We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

Plasmatic apelin shows a promising potential as a screening biomarker for atrial fibrillation

Background and Objectives: Surgical revascularisation of patients with atherosclerosis of the ascending aorta remains a challenge. Different surgical strategies have been described in coronary surgical patients to offer alternative revascularisation strategies other than the conventional surgical revascularisation in patients unsuitable for it. The aim of this study is to compare the real-world outcomes between two groups of patients who underwent off-pump surgery (left internal mammary artery graft to the left anterior descending artery) or a hybrid with a percutaneous revascularisation procedure at a later stage. Materials and Methods: This is a single-centre retrospective observational study. Between the years 2010 and 2021, 91/6863 patients (1.33%) were diagnosed with severe atherosclerosis of the ascending aorta. All the patients were treated with off-pump revascularisation (91 patients), and the cardiologist would decide at a later stage whether the rest of the vessels would be treated with percutaneous revascularisation (25 patients). Results: There was no statistical difference in the various preoperative characteristics, except for coronary artery left main disease (30.30% vs. 64%; p = 0.0043). The two groups had no statistical differences in the perioperative characteristics and postoperative complications. The 1-, 5-, and 10-year mortality rates in the two groups were 6.1% vs. 0%, 59% vs. 80%, and 93.9% vs. 100%, respectively (off-pump vs. hybrid with percutaneous revascularisation procedure, p = 0.1958). Conclusions: Both strategies have high long-term comparable mortality. The off-pump surgery and the HCR procedure at a later stage may be solutions for these high-risk patients, but the target treatment should be complete HCR revascularisation during the index hospitalization.