Sacubitril/valsartan for the treatment of non-obstructive hypertrophic cardiomyopathy: An open label randomized controlled trial (SILICOFCM).

AIM: Sacubitril/valsartan treatment reduces mortality and hospitalizations in heart failure with reduced ejection fraction but has limited application in hypertrophic cardiomyopathy (HCM). The aim of this study was to evaluate the effect of sacubitril/valsartan on peak oxygen consumption (VO2) in patients with non-obstructive HCM. METHODS AND RESULTS: This is a phase II, randomized, open-label multicentre study that enrolled adult patients with symptomatic non-obstructive HCM (New York Heart Association class I-III) who were randomly assigned (2:1) to receive sacubitril/valsartan (target dose 97/103 mg) or control for 16 weeks. The primary endpoint was a change in peak VO2. Secondary endpoints included echocardiographic measures of cardiac structure and function, natriuretic peptides and other cardiac biomarkers, and Minnesota Living with Heart Failure quality of life. Between May 2018 and October 2021, 354 patients were screened for eligibility, 115 patients (mean age 58 years, 37% female) met the study inclusion criteria and were randomly assigned to sacubitril/valsartan (n = 79) or control (n = 36). At 16 weeks, there was no significant change in peak VO2 from baseline in the sacubitril/valsartan (15.3 [4.3] vs. 15.9 [4.3] ml/kg/min, p = 0.13) or control group (p = 0.47). No clinically significant changes were found in blood pressure, cardiac structure and function, plasma biomarkers, or quality of life. CONCLUSION: In patients with HCM, a 16-week treatment with sacubitril/valsartan was well tolerated but had no effect on exercise capacity, cardiac structure, or function.

Elevated Troponins after COVID-19 Hospitalization and Long-Term COVID-19 Symptoms: Incidence, Prognosis, and Clinical Outcomes-Results from a Multi-Center International Prospective Registry (HOPE-2).

Background: Acute cardiac injury (ACI) after COVID-19 has been linked with unfavorable clinical outcomes, but data on the clinical impact of elevated cardiac troponin on discharge during follow-up are scarce. Our objective is to elucidate the clinical outcome of patients with elevated troponin on discharge after surviving a COVID-19 hospitalization. Methods: We conducted an analysis in the prospective registry HOPE-2 (NCT04778020). Only patients discharged alive were selected for analysis, and all-cause death on follow-up was considered as the primary endpoint. As a secondary endpoint, we established any long-term COVID-19 symptoms. HOPE-2 stopped enrolling patients on 31 December 2021, with 9299 patients hospitalized with COVID-19, of which 1805 were deceased during the acute phase. Finally, 2382 patients alive on discharge underwent propensity score matching by relevant baseline variables in a 1:3 fashion, from 56 centers in 8 countries. Results: Patients with elevated troponin experienced significantly higher all-cause death during follow-up (log-rank = 27.23, p < 0.001), and had a higher chance of experiencing long-term COVID-19 cardiovascular symptoms. Specifically, fatigue and dyspnea (57.7% and 62.8%, with p-values of 0.009 and <0.001, respectively) are among the most common. Conclusions: After surviving the acute phase, patients with elevated troponin on discharge present increased mortality and long-term COVID-19 symptoms over time, which is clinically relevant in follow-up visits.

Direct electrochemical reduction of graphene oxide thin film for aptamer-based selective and highly sensitive detection of matrix metalloproteinase 2.

Simple and low-cost biosensing solutions are suitable for point-of-care applications aiming to overcome the gap between scientific concepts and technological production. To compete with sensitivity and selectivity of golden standards, such as liquid chromatography, the functionalization of biosensors is continuously optimized to enhance the signal and improve their performance, often leading to complex chemical assay development. In this research, the efforts are made on optimizing the methodology for electrochemical reduction of graphene oxide to produce thin film-modified gold electrodes. Under the employed specific conditions, 20 cycles of cyclic voltammetry (CV) are shown to be optimal for superior electrical activation of graphene oxide into electrochemically reduced graphene oxide (ERGO). This platform is further used to develop a matrix metalloproteinase 2 (MMP-2) biosensor, where specific anti-MMP2 aptamers are utilized as a biorecognition element. MMP-2 is a protein which is typically overexpressed in tumor tissues, with important roles in tumor invasion, metastasis as well as in tumor angiogenesis. Based on impedimetric measurements, we were able to detect as low as 3.32 pg mL-1 of MMP-2 in PBS with a dynamic range of 10 pg mL-1 - 10 ng mL-1. Further experiments with real blood samples revealed a promising potential of the developed sensor for direct measurement of MMP-2 in complex media. High specificity of detection is demonstrated - even to the closely related enzyme MMP-9. Finally, the potential of reuse was demonstrated by signal restoration after experimental detection of MMP-2.

Hypertrophic Cardiomyopathy: Genetic Foundations, Outcomes, Interconnections, and Their Modifiers.

Hypertrophic cardiomyopathy (HCM) is the most prevalent heritable cardiomyopathy. HCM is considered to be caused by mutations in cardiac sarcomeric protein genes. Recent research suggests that the genetic foundation of HCM is much more complex than originally postulated. The clinical presentations of HCM are very variable. Some mutation carriers remain asymptomatic, while others develop severe HCM, terminal heart failure, or sudden cardiac death. Heterogeneity regarding both genetic mutations and the clinical course of HCM hinders the establishment of universal genotype-phenotype correlations. However, some trends have been identified. The presence of a mutation in some genes encoding sarcomeric proteins is associated with earlier HCM onset, more severe left ventricular hypertrophy, and worse clinical outcomes. There is a diversity in the mechanisms implicated in the pathogenesis of HCM. They may be classified into groups, but they are interrelated. The lack of known supplementary elements that control the progression of HCM indicates that molecular mechanisms that exist between genotype and clinical presentations may be crucial. Secondary molecular changes in pathways implicated in HCM pathogenesis, post-translational protein modifications, and epigenetic factors affect HCM phenotypes. Cardiac loading conditions, exercise, hypertension, diet, alcohol consumption, microbial infection, obstructive sleep apnea, obesity, and environmental factors are non-molecular aspects that change the HCM phenotype. Many mechanisms are implicated in the course of HCM. They are mostly interconnected and contribute to some extent to final outcomes.

Pulmonary Valve Endocarditis during and beyond Euro ENDO Registry: A Single Center Case Series.

Background: Pulmonary valve infective endocarditis (PVIE) is a rare form of infective endocarditis (IE) and is associated with high mortality and severe complications. Guidelines for treatment of this form of IE are scarce and based on general recommendations. We report a case series of PVE. Detailed Case Description: Case 1-A 36-year-old female with congenital pulmonary artery stenosis, dyspnea and leg edema symptoms for 2 months. Blood cultures yielded Staphylococcus spp. and Corynebacterium sp., and echocardiography revealed multiple floating vegetation at the pulmonic valve and surrounding structures. The clinical course was complicated with sepsis and multi-organ failure. Urgent surgery with pulmonary homograft implantation resulted in successful five-year outcome. Case 2-In a 38-year-old male with previous tetralogy of Fallot correction and symptoms of fatigue, fever, myalgia, and photophobia, echocardiography was suggestive of PVIE. The clinical course was complicated with septic shock, multi-organ failure, ischemic stroke with hemorrhagic transformation and death on the 12th day of hospitalization. Case 3-A 41-year-old male without previous medical history was hospitalized due to prolonged fatigue, fever, dyspnea, and leg edema. He was diagnosed with multi-valve infective endocarditis, affecting the aortic, tricuspid, and pulmonary valve. Acute heart failure and hemodynamic instability indicated urgent surgery with aortic valve replacement and reconstruction of the tricuspid and pulmonary valves. At four-year follow up he was doing well. Conclusion: Symptoms in PVIE may be versatile, and diagnosis is often delayed. High level of suspicion, early recognition, and echocardiography are cornerstones in diagnostics. Despite the standpoint that medical therapy is first-line, the role of surgery needs to be advocated in particular cases.

Clinical Profile and Determinants of Mortality in Patients with Interstitial Lung Disease Admitted for COVID-19.

BACKGROUND: Concern has risen about the effects of COVID-19 in interstitial lung disease (ILD) patients. The aim of our study was to determine clinical characteristics and prognostic factors of ILD patients admitted for COVID-19. METHODS: Ancillary analysis of an international, multicenter COVID-19 registry (HOPE: Health Outcome Predictive Evaluation) was performed. The subgroup of ILD patients was selected and compared with the rest of the cohort. RESULTS: A total of 114 patients with ILDs were evaluated. Mean ± SD age was 72.4 ± 13.6 years, and 65.8% were men. ILD patients were older, had more comorbidities, received more home oxygen therapy and more frequently had respiratory failure upon admission than non-ILD patients (all p < 0.05). In laboratory findings, ILD patients more frequently had elevated LDH, C-reactive protein, and D-dimer levels (all p < 0.05). A multivariate analysis showed that chronic kidney disease and respiratory insufficiency on admission were predictors of ventilatory support, and that older age, kidney disease and elevated LDH were predictors of death. CONCLUSIONS: Our data show that ILD patients admitted for COVID-19 are older, have more comorbidities, more frequently require ventilatory support and have higher mortality than those without ILDs. Older age, kidney disease and LDH were independent predictors of mortality in this population.

Post-COVID-19 syndrome and diabetes mellitus: a propensity-matched analysis of the International HOPE-II COVID-19 Registry.

Background: Diabetes mellitus (DM) is one of the most frequent comorbidities in patients suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a higher rate of severe course of coronavirus disease (COVID-19). However, data about post-COVID-19 syndrome (PCS) in patients with DM are limited. Methods: This multicenter, propensity score-matched study compared long-term follow-up data about cardiovascular, neuropsychiatric, respiratory, gastrointestinal, and other symptoms in 8,719 patients with DM to those without DM. The 1:1 propensity score matching (PSM) according to age and sex resulted in 1,548 matched pairs. Results: Diabetics and nondiabetics had a mean age of 72.6 ± 12.7 years old. At follow-up, cardiovascular symptoms such as dyspnea and increased resting heart rate occurred less in patients with DM (13.2% vs. 16.4%; p = 0.01) than those without DM (2.8% vs. 5.6%; p = 0.05), respectively. The incidence of newly diagnosed arterial hypertension was slightly lower in DM patients as compared to non-DM patients (0.5% vs. 1.6%; p = 0.18). Abnormal spirometry was observed more in patients with DM than those without DM (18.8% vs. 13; p = 0.24). Paranoia was diagnosed more frequently in patients with DM than in non-DM patients at follow-up time (4% vs. 1.2%; p = 0.009). The incidence of newly diagnosed renal insufficiency was higher in patients suffering from DM as compared to patients without DM (4.8% vs. 2.6%; p = 0.09). The rate of readmission was comparable in patients with and without DM (19.7% vs. 18.3%; p = 0.61). The reinfection rate with COVID-19 was comparable in both groups (2.9% in diabetics vs. 2.3% in nondiabetics; p = 0.55). Long-term mortality was higher in DM patients than in non-DM patients (33.9% vs. 29.1%; p = 0.005). Conclusions: The mortality rate was higher in patients with DM type II as compared to those without DM. Readmission and reinfection rates with COVID-19 were comparable in both groups. The incidence of cardiovascular symptoms was higher in patients without DM.

Machine learning and physical based modeling for cardiac hypertrophy.

Background and objective: Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful. Methods: In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy. Results: Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results. Conclusions: The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.

Sacubitril/valsartan reverses cardiac structure and function in experimental model of hypertension-induced hypertrophic cardiomyopathy.

This study evaluated the effect of sacubtril/valsartan on cardiac remodeling, molecular and cellular adaptations in experimental (rat) model of hypertension-induced hypertrophic cardiomyopathy. Thirty Wistar Kyoto rats, 10 healthy (control) and 20 rats with confirmed hypertension-induced hypertrophic cardiomyopathy (HpCM), were used for this study. The HpCM group was further subdivided into untreated and sacubitril/valsartan-treated groups. Myocardial structure and function were assessed using echocardiography, Langendorff's isolated heart experiment, blood sampling and qualitative polymerase chain reaction. Echocardiographic examinations revealed protective effects of sacubitril/valsartan by improving left ventricular internal diameter in systole and diastole and fractional shortening. Additionally, sacubitril/valsartan treatment decreased systolic and diastolic blood pressures in comparison with untreated hypertensive rats. Moreover, sacubitril/valsartan treatment reduced oxidative stress and apoptosis (reduced expression of Bax and Cas9 genes) compared to untreated rats. There was a regular histomorphology of cardiomyocytes, interstitium, and blood vessels in treated rats compared to untreated HpCM rats which expressed hypertrophic cardiomyocytes, with polymorphic nuclei, prominent nucleoli and moderately dilated interstitium. In experimental model of hypertension-induced hypertrophic cardiomyopathy, sacubitril/valsartan treatment led to improved cardiac structure, haemodynamic performance, and reduced oxidative stress and apoptosis. Sacubitril/valsartan thus presents as a potential therapeutic strategy resulted in hypertension-induced hypertrophic cardiomyopathy.

Post-COVID-19 Symptoms and Heart Disease: Incidence, Prognostic Factors, Outcomes and Vaccination: Results from a Multi-Center International Prospective Registry (HOPE 2).

BACKGROUND: Heart disease is linked to worse acute outcomes after coronavirus disease 2019 (COVID-19), although long-term outcomes and prognostic factor data are lacking. We aim to characterize the outcomes and the impact of underlying heart diseases after surviving COVID-19 hospitalization. METHODS: We conducted an analysis of the prospective registry HOPE-2 (Health Outcome Predictive Evaluation for COVID-19-2, NCT04778020). We selected patients discharged alive and considered the primary end-point all-cause mortality during follow-up. As secondary main end-points, we included any readmission or any post-COVID-19 symptom. Clinical features and follow-up events are compared between those with and without cardiovascular disease. Factors with p < 0.05 in the univariate analysis were entered into the multivariate analysis to determine independent prognostic factors. RESULTS: HOPE-2 closed on 31 December 2021, with 9299 patients hospitalized with COVID-19, and 1805 died during this acute phase. Finally, 7014 patients with heart disease data were included in the present analysis, from 56 centers in 8 countries. Heart disease (+) patients were older (73 vs. 58 years old), more frequently male (63 vs. 56%), had more comorbidities than their counterparts, and suffered more frequently from post-COVID-19 complications and higher mortality (OR heart disease: 2.63, 95% CI: 1.81-3.84). Vaccination was found to be an independent protector factor (HR all-cause death: 0.09; 95% CI: 0.04-0.19). CONCLUSIONS: After surviving the acute phase, patients with underlying heart disease continue to present a more complex clinical profile and worse outcomes including increased mortality. The COVID-19 vaccine could benefit survival in patients with heart disease during follow-up.

Antithrombotic Strategy in Secondary Prevention for High-Risk Patients with Previous Acute Coronary Syndrome: Overlap between the PEGASUS Eligibility and the COMPASS Eligibility in a Large Multicenter Registry.

BACKGROUND: Patients with previous acute coronary syndrome (ACS) are at high risk of recurrent adverse cardiovascular events. Recently, prolonged dual antiplatelet therapy (DAPT) and oral anticoagulation therapy (OAT) have been shown to reduce recurrent ischemic events to the expense of an increase in bleeding events. The number of patients potentially eligible for these therapies in real life remains to be determined. METHODS: Among ACS patients from five registries and one randomized controlled trial, we assessed the proportion of patients eligible for the PEGASUS strategy only and the proportion of patients eligible for the COMPASS strategy only, and set out the proportion of patients with an overlap between the strategies. FINDINGS: Among the 10,048 evaluable patients, we found that 5373 (53.4%) were eligible for the PEGASUS strategy and 3841 (38.2%) were eligible for the COMPASS strategy, with a group of 3444 (34.4%) overlapping between the two strategies. The number of patients eligible for the PEGASUS strategy only was 1929 (19.2%) and the number eligible for the COMPASS strategy only was 397 (4.0%); 4278 (42.6%) were eligible for neither a PEGASUS strategy nor a COMPASS strategy. INTERPRETATION: In a large cohort of ACS patients, one in three patients was eligible for either a prolonged DAPT with ticagrelor 60 mg and low-dose aspirin or a dual pathway inhibition approach with rivaroxaban 2.5 mg and low-dose aspirin.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms.

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

Haemodynamic determinants of quality of life in chronic heart failure.

BACKGROUND: Heart failure patients demonstrate reduced functional capacity, hemodynamic function, and quality of life (QOL) which are associated with high mortality and morbidity rate. The aim of the present study was to assess the relationship between functional capacity, hemodynamic response to exercise and QOL in chronic heart failure. METHODS: A single-centre prospective study recruited 42 chronic heart failure patients (11 females, mean age 60 ± 10 years) with reduced left ventricular ejection fraction (LVEF = 23 ± 7%). All participants completed a maximal graded cardiopulmonary exercise test with non-invasive hemodynamic (bioreactance) monitoring. QOL was assessed using Minnesota Living with Heart Failure Questionnaire. RESULTS: The average value of QOL score was 40 ± 23. There was a significant negative relationship between the QOL and peak O2 consumption (r = - 0.50, p ≤ 0.01). No significant relationship between the QOL and selected exercise hemodynamic measures was found, including peak exercise cardiac power output (r = 0.15, p = 0.34), cardiac output (r = 0.22, p = 0.15), and mean arterial blood pressure (r = - 0.08, p = 0.60). CONCLUSION: Peak O2 consumption, but not hemodynamic response to exercise, is a significant determinant of QOL in chronic heart failure patients.

More slices, less truth: effects of different test-set design strategies for magnetic resonance image classification.

AIM: To assess the effects of different test-set design strategies for magnetic resonance (MR) image classification using deep learning. METHODS: Error rates in 10 experimental settings were assessed. The performance of pretrained models and data augmentation were examined as possible contributing factors. RESULTS: Error rates in experimental settings using MR images of different patients for training and test sets were ten times higher than those in experimental settings using MR images of the same patients (four disease groups with whole-chest images, 46.80% vs 2.06%; four disease groups without whole-chest images, 49.09% vs 1.29%; sex classification with whole-chest images, 16.02% vs 0.96%; and sex classification without whole-chest images, 23.56% vs 0.30%). Error rates were higher when data augmentation was applied to settings that used MR images of different patients for training and test sets. CONCLUSION: When deep learning is applied to MR image classification, training and test sets should consist of MR images of different patients. Models built on training and test sets consisting of images of the same patients yield optimistic error rates and lead to wrong conclusions. MR images of neighboring slices are so similar that they cause data leakage effect.

Gender Related Differences in the Clinical Presentation of Hypertrophic Cardiomyopathy-An Analysis from the SILICOFCM Database.

Background and Objectives: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease that affects approximately 1 in 500 people. Due to an incomplete disease penetrance associated with numerous factors, HCM is not manifested in all carriers of genetic mutation. Although about two-thirds of patients are male, it seems that female gender is associated with more severe disease phenotype and worse prognosis. The objective of this study was to evaluate the gender related differences in HCM presentation. Materials and Methods: This study was conducted as a part of the international multidisciplinary SILICOFCM project. Clinical information, laboratory analyses, electrocardiography, echocardiography, and genetic testing data were collected for 362 HCM patients from four clinical centers (Florence, Newcastle, Novi Sad, and Regensburg). There were 33% female patients, and 67% male patients. Results: Female patients were older than males (64.5 vs. 53.5 years, p < 0.0005). The male predominance was present across all age groups until the age of 70, when gender distribution became comparable. Females had higher number of symptomatic individuals then males (69% vs. 52%, p = 0.003), most frequently complaining of dyspnea (50% vs. 30%), followed by chest pain (30% vs. 17%), fatigue (26% vs. 13%), palpitations (22% vs. 13%), and syncope (13% vs. 8%). The most common rhythm disorder was atrial fibrillation which was present in a similar number of females and males (19% vs. 13%, p = 0.218). Levels of N-terminal pro-brain natriuretic peptide were comparable between the genders (571 vs. 794 ng/L, p = 0.244). Echocardiography showed similar thickness of interventricular septum (18 vs. 16 mm, p = 0.121) and posterolateral wall (13 vs. 12 mm, p = 0.656), however, females had a lower number of systolic anterior motion (8% vs. 16%, p = 0.020) and other mitral valve abnormalities. Conclusions: Female patients are underrepresented but seem to have a more pronounced clinical presentation of HCM. Therefore, establishing gender specific diagnostic criteria for HCM should be considered.

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning.

BACKGROUND: Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). OBJECTIVE: Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. METHODS: The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. RESULTS: The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R2 from 0.3 to 0.6. CONCLUSIONS: By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

Unusual non-valvular left ventricular endocarditis presenting as multiple brain embolism.

INTRODUCTION: Masses inside the heart can cause serious and life-threatening effects to the cardiovascular system, mainly because of hemodynamic obstruction of the blood flow, either in the heart cavities themselves or remotely due to embolization. In this paper, we report a case of left ventricular tumor mass which presented with neurological symptoms due to multiple brain embolism. CASE REPORT: A 35-year-old female patient presented with right hemiparesis and dysarthria. Seven days prior to admission she had elevated body temperature and started taking antibiotics. Inflammatory markers were not elevated, and blood cultures were negative. Computerized tomography (CT) and magnetic resonance imaging (MRI) of the brain revealed multiple acute ischemic lesions. Echocardiography showed the presence of a lobular mass inside the left ventricle, which was attached to the basal segment of the lateral left ventricular wall. Based on the laboratory results and additional heart imaging (CT and MRI) the mass was primarily suspected to be a tumor. It was surgically removed. Microscopic analysis of the removed tissue revealed a non-specific endocardial inflammation with formed fresh fibrin thrombi on the surface. During the postoperative recovery intense physical rehabilitation was being performed, so the initial neurological deficit was completely withdrawn. CONCLUSION: Intracardiac masses can cause serious and potentially fatal complications that often present with dramatic clinical symptoms. Despite the comprehensive clinical, laboratory, and imaging investigations, intracardiac masses can be hard to distinguish until the definite microscopic analysis. However, with the right approach and multidisciplinary collaboration, they can be successfully managed.

Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis: The AVATAR Trial.

BACKGROUND: Surgical aortic valve replacement (SAVR) represents a class I indication in symptomatic patients with severe aortic stenosis (AS). However, indications for early SAVR in asymptomatic patients with severe AS and normal left ventricular function remain debated. METHODS: The AVATAR trial (Aortic Valve Replacement Versus Conservative Treatment in Asymptomatic Severe Aortic Stenosis) is an investigator-initiated international prospective randomized controlled trial that evaluated the safety and efficacy of early SAVR in the treatment of asymptomatic patients with severe AS, according to common criteria (valve area ≤1 cm2 with aortic jet velocity >4 m/s or a mean transaortic gradient ≥40 mm Hg), and with normal left ventricular function. Negative exercise testing was mandatory for inclusion. The primary hypothesis was that early SAVR would reduce the primary composite end point of all-cause death, acute myocardial infarction, stroke, or unplanned hospitalization for heart failure compared with a conservative strategy according to guidelines. The trial was designed as event-driven to reach a minimum of 35 prespecified events. The study was performed in 9 centers in 7 European countries. RESULTS: Between June 2015 and September 2020, 157 patients (mean age, 67 years; 57% men) were randomly allocated to early surgery (n=78) or conservative treatment (n=79). Follow-up was completed in May 2021. Overall median follow-up was 32 months: 28 months in the early surgery group and 35 months in the conservative treatment group. There was a total of 39 events, 13 in early surgery and 26 in the conservative treatment group. In the early surgery group, 72 patients (92.3%) underwent SAVR with operative mortality of 1.4%. In an intention-to-treat analysis, patients randomized to early surgery had a significantly lower incidence of primary composite end point than those in the conservative arm (hazard ratio, 0.46 [95% CI, 0.23-0.90]; P=0.02). There was no statistical difference in secondary end points, including all-cause mortality, first heart failure hospitalizations, major bleeding, or thromboembolic complications, but trends were consistent with the primary outcome. CONCLUSIONS: In asymptomatic patients with severe AS, early surgery reduced a primary composite of all-cause death, acute myocardial infarction, stroke, or unplanned hospitalization for heart failure compared with conservative treatment. This randomized trial provides preliminary support for early SAVR once AS becomes severe, regardless of symptoms. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02436655.

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation.

BACKGROUND: Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype-phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms' models and examined the performance of different machine approaches for that task. RESULTS: We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models' differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems' performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. CONCLUSIONS: Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

ACUTE KIDNEY INJURY AFTER OPEN-HEART SURGERY PROCEDURES.

Cardiac surgery-associated acute kidney injury (CS-AKI) is a major complication associated with increased morbidity and mortality. There are multiple diagnostic criteria for CS-AKI. Despite many new investigations available for improved AKI diagnostics, creatinine and urea remain the cornerstone of diagnostics in everyday clinical practice. There are three major pathophysiological mechanisms that contribute to kidney injury, i.e. renal hypoperfusion, inflammation with oxidative stress, and use of nephrotoxic agents. Some risk factors have been identified that can be modified during the course of treatment (use of nephrotoxic agents, duration of cardiopulmonary bypass, type of extracorporeal circulation, postoperative low cardiac output or hypotension). The aim of AKI prevention should always be to prevent aggravation of renal failure and, if possible, to avoid progression to renal replacement therapy, which in turn brings worse long-term outcomes.