Pulmonary artery pressure monitoring in chronic heart failure: effects across clinically relevant subgroups in the MONITOR-HF trial.

BACKGROUND AND AIMS: In patients with chronic heart failure (HF), the MONITOR-HF trial demonstrated the efficacy of pulmonary artery (PA)-guided HF therapy over standard of care in improving quality of life and reducing HF hospitalizations and mean PA pressure. This study aimed to evaluate the consistency of these benefits in relation to clinically relevant subgroups. METHODS: The effect of PA-guided HF therapy was evaluated in the MONITOR-HF trial among predefined subgroups based on age, sex, atrial fibrillation, diabetes mellitus, left ventricular ejection fraction, HF aetiology, cardiac resynchronisation therapy, and implantable cardioverter defibrillator. Outcome measures were based upon significance in the main trial and included quality of life, clinical, and PA pressure endpoints, and were assessed for each subgroup. Differential effects in relation to the subgroups were assessed with interaction terms. Both unadjusted and multiple testing adjusted interaction terms were presented. RESULTS: The effects of PA monitoring on quality of life, clinical events, and PA pressure were consistent in the predefined subgroups, without any clinically relevant heterogeneity within or across all endpoint categories (all adjusted interaction P-values were nonsignificant). In the unadjusted analysis of the primary endpoint quality-of-life change, weak trends towards a less pronounced effect in older patients (Pinteraction = 0.03; adjusted Pinteraction = 0.33) and diabetics (Pinteraction = 0.01; adjusted Pinteraction = 0.06) were observed. However, these interaction effects did not persist after adjusting for multiple testing. CONCLUSIONS: This subgroup analysis confirmed the consistent benefits of PA-guided HF therapy observed in the MONITOR-HF trial across clinically relevant subgroups, highlighting its efficacy in improving quality of life, clinical, and PA pressure endpoints in chronic HF patients.

The plasma proteome is linked with left ventricular and left atrial function parameters in patients with chronic heart failure.

BACKGROUND: Examining the systemic biological processes in the heterogeneous syndrome of heart failure with reduced ejection fraction (HFrEF), as reflected by circulating proteins, in relation to echocardiographic characteristics, may provide insights into HF pathophysiology. OBJECTIVE: We investigated the link of 4210 repeatedly measured circulating proteins with repeatedly measured echocardiographic parameters, as well as with elevated left atrial pressure (LAP), in HFrEF patients, to provide insights into underlying mechanisms. METHODS: In 173 HFrEF patients, we performed six-monthly echocardiography and trimonthly blood sampling during a median follow-up of 2.7(IQR:2.5-2.8) years. We investigated circulating proteins in relation to echocardiographic parameters of left ventricular (left ventricular ejection fraction[LVEF], global longitudinal strain[GLS]), and left atrial function (left atrial reservoir strain[LASr]) and elevated LAP(E/e' ratio >15), and used gene enrichment analyses to identify underlying pathophysiological processes. RESULTS: We found 723, 249, 792 and 427 repeatedly measured proteins, with significant associations with LVEF, GLS, LASr and E/e' ratio, respectively. Proteins associated with LASr reflected pathophysiological mechanisms mostly related to the extracellular matrix (ECM). Proteins associated with GLS reflected cardiovascular biological processes and diseases, whereas those associated with LVEF reflected processes involved in the sympathetic nervous system. Moreover, 49 proteins were associated with elevated LAP; after correction for LVEF, three proteins remained: Cystatin-D, Fibulin-5 and HSP40. CONCLUSION: Circulating proteins show varying associations with different echocardiographic parameters in HFrEF patients. These findings suggest that pathways involved in atrial and ventricular dysfunction, as reflected by the plasma proteome, are distinct.

Identifying and ranking novel independent features for cardiovascular disease prediction in people with type 2 diabetes.

Background: CVD prediction models do not perform well in people with diabetes. We therefore aimed to identify novel predictors for six facets of CVD, (including coronary heart disease (CHD), Ischemic stroke, heart failure (HF), and atrial fibrillation (AF)) in people with T2DM. Methods: Analyses were conducted using the UK biobank and were stratified on history of CVD and of T2DM: 459,142 participants without diabetes or a history of CVD, 14,610 with diabetes but without CVD, and 4,432 with diabetes and a history of CVD. Replication was performed using a 20% hold-out set, ranking features on their permuted c-statistic. Results: Out of the 600+ candidate features, we identified a subset of replicated features, ranging between 32 for CHD in people with diabetes to 184 for CVD+HF+AF in people without diabetes. Classical CVD risk factors (e.g. parental or maternal history of heart disease, or blood pressure) were relatively highly ranked for people without diabetes. The top predictors in the people with diabetes without a CVD history included: cystatin C, self-reported health satisfaction, biochemical measures of ill health (e.g. plasma albumin). For people with diabetes and a history of CVD top features were: self-reported ill health, and blood cell counts measurements (e.g. red cell distribution width). We additionally identified risk factors unique to people with diabetes, consisting of information on dietary patterns, mental health and biochemistry measures. Consideration of these novel features improved risk classification, for example per 1000 people with diabetes 133 CVD and 165 HF cases appropriately received a higher risk. Conclusion: Through data-driven feature selection we identified a substantial number of features relevant for prediction of cardiovascular risk in people with diabetes, the majority of which related to non-classical risk factors such as mental health, general illness markers, and kidney disease.

DCM-PROGRESS: predicting end-stage heart failure in non-ischemic dilated cardiomyopathy patients.

Aims: Patients with non-ischemic dilated cardiomyopathy (DCM) are at considerable risk for end-stage heart failure (HF), requiring close monitoring to identify early signs of disease. We aimed to develop a model to predict the 5-years risk of end-stage HF, allowing for tailored patient monitoring and management. Methods and results: Derivation data were available from a Dutch cohort of 293 DCM patients, with external validation available from a Czech Republic cohort of 235 DCM patients. Candidate predictors spanned patient and family histories, ECG and echocardiogram measurements, and biochemistry. End-stage HF was defined as a composite of death, heart transplantation, or implantation of a ventricular assist device. Lasso and sigmoid kernel support vector machine (SVM) algorithms were trained using cross-validation. During follow-up 65 (22%) of Dutch DCM patients developed end-stage HF, with 27 (11%) cases in the Czech cohort. Out of the two considered models, the lasso model (retaining NYHA class, heart rate, systolic blood pressure, height, R-axis, and TAPSE as predictors) reached the highest discriminative performance (testing c-statistic of 0.85, 95%CI 0.58; 0.94), which was confirmed in the external validation cohort (c-statistic of 0.75, 95%CI 0.61; 0.82), compared to a c-statistic of 0.69 for the MAGGIC score. Both the MAGGIC score and the DCM-PROGRESS model slightly over-estimated the true risk, but were otherwise appropriately calibrated. Conclusion: We developed a highly discriminative risk-prediction model for end-stage HF in DCM patients. The model was validated in two countries, suggesting the model can meaningfully improve clinical decision-making.

Yield of family screening in dilated cardiomyopathy within low-income setting: Tanzanian experience.

BACKGROUND: Dilated cardiomyopathy (DCM) is often familial and screening of relatives is recommended. However, studies on the yield of screening are scarce in developing countries. AIM: The aim of the study was to identify and characterise First-degree relatives of patients with DCM in Tanzania. METHODS: We recruited first-degree relatives of 57 DCM patients. DCM in the relatives was diagnosed using the 2016 revised definition by the European Society of Cardiology working group on myocardial and pericardial diseases. RESULTS: We screened 120 first-degree relatives. All were asymptomatic (100%) with a median age of 39.0 years (29.5-49.0), slightly over a half (53.3%) were females and 17 (14.1%) were found to have previously unknown DCM. The mean (± SD) indexed left ventricular end-diastolic volume was significantly higher in relatives with DCM (71 ± 11.5 ml) compared to relatives without DCM (50 ± 11.5) (p = 0.001). CONCLUSION: First-degree relatives of patients with DCM are at risk of developing asymptomatic DCM at a young age.

Circadian rhythms in pump parameters of patients on contemporary left ventricular assist device support.

BACKGROUND: Algorithms to monitor pump parameters are needed to further improve outcomes after left ventricular assist device (LVAD) implantation. Previous research showed a restored circadian rhythm in pump parameters in patients on HeartWare (HVAD) support. Circadian patterns in HeartMate3 (HM3) were not studied before, but this is important for the development of LVAD monitoring algorithms. Hence, we aimed to describe circadian patterns in HM3 parameters and their relation to patterns in heart rate (HR). METHODS: 18 HM3 patients were included in this study. HM3 data were retrieved at a high frequency (one sample per 1 or 2 h) for 1-2 weeks. HR was measured using a wearable biosensor. To study overall patterns in HM3 parameters and HR, a heatmap was created. A 24-h cosine was fitted on power and HR separately. The relationship between the amplitude of the fitted cosines of power and HR was calculated using Spearman correlation. RESULTS: A lower between patient variability was found in power compared with flow and PI. 83% of the patients showed a significant circadian rhythmicity in power (p < 0.001-0.04), with a clear morning increase. All patients showed significant circadian rhythmicity in HR (p < 0.001-0.02). The amplitudes of the circadian rhythm in power and HR were not correlated (Spearman correlation of 0.32, p = 0.19). CONCLUSIONS: A circadian rhythm of pump parameters is present in the majority of HM3 patients. Higher frequency pump parameter data should be collected, to enable early detection of complications in the future development of predictive algorithms.

Circulating Acylcarnitines Associated with Hypertrophic Cardiomyopathy Severity: an Exploratory Cross-Sectional Study in MYBPC3 Founder Variant Carriers.

Hypertrophic cardiomyopathy (HCM) is a relatively common genetic heart disease characterised by myocardial hypertrophy. HCM can cause outflow tract obstruction, sudden cardiac death and heart failure, but severity is highly variable. In this exploratory cross-sectional study, circulating acylcarnitines were assessed as potential biomarkers in 124 MYBPC3 founder variant carriers (59 with severe HCM, 26 with mild HCM and 39 phenotype-negative [G + P-]). Elastic net logistic regression identified eight acylcarnitines associated with HCM severity. C3, C4, C6-DC, C8:1, C16, C18 and C18:2 were significantly increased in severe HCM compared to G + P-, and C3, C6-DC, C8:1 and C18 in mild HCM compared to G + P-. In multivariable linear regression, C6-DC and C8:1 correlated to log-transformed maximum wall thickness (coefficient 5.01, p = 0.005 and coefficient 0.803, p = 0.007, respectively), and C6-DC to log-transformed ejection fraction (coefficient -2.50, p = 0.004). Acylcarnitines seem promising biomarkers for HCM severity, however prospective studies are required to determine their prognostic value.

Risk Factors and Prevalence of Dilated Cardiomyopathy in Sub-Saharan Africa: A Systematic Review.

Highlights  Prevalence of DCM varies widely in SSA.Cardiovascular risk factors are important in patients with DCM.The role of genetics in idiopathic DCM is not studied in major part of SSA.

The benefit of vaccination against COVID-19 outweighs the potential risk of myocarditis and pericarditis.

Vaccines against coronavirus 2019 disease (COVID-19) have shown to be greatly effective in preventing viral spread, serious illness and death from this infectious disease and are therefore critical for the management of the COVID-19 pandemic. However, the listing of myocarditis and pericarditis as possible rare side effects of the messenger RNA (mRNA) vaccines against COVID-19 by regulatory agencies has sparked discussion on the vaccines' safety. The most important published cohort studies to date demonstrat that myocarditis is a very rare side effect after COVID-19 mRNA vaccination, with an incidence of approximately 1-4 cases per 100,000 vaccinated persons. Young males (16-29 years) appear to be at highest risk, predominantly after receiving the second dose. The disease course is self-limiting in a vast majority of cases: 95% of patients show a rapid resolution of symptoms and normalisation of cardiac biomarkers, electro- and echocardiographic findings within days. Importantly, the available data suggest that the incidence rate of myocarditis in the context of COVID-19 is much greater than the risk of this side effect following vaccination. We conclude that the benefit of vaccination against COVID-19 outweighs the potential risk of myocarditis and pericarditis in both adolescents and adults. Prospective follow-up of patients who have developed these complications after vaccination is required to assess long-term outcomes.

Age is the main determinant of COVID-19 related in-hospital mortality with minimal impact of pre-existing comorbidities, a retrospective cohort study.

BACKGROUND: Age and comorbidities increase COVID-19 related in-hospital mortality risk, but the extent by which comorbidities mediate the impact of age remains unknown. METHODS: In this multicenter retrospective cohort study with data from 45 Dutch hospitals, 4806 proven COVID-19 patients hospitalized in Dutch hospitals (between February and July 2020) from the CAPACITY-COVID registry were included (age 69[58-77]years, 64% men). The primary outcome was defined as a combination of in-hospital mortality or discharge with palliative care. Logistic regression analysis was performed to analyze the associations between sex, age, and comorbidities with the primary outcome. The effect of comorbidities on the relation of age with the primary outcome was evaluated using mediation analysis. RESULTS: In-hospital COVID-19 related mortality occurred in 1108 (23%) patients, 836 (76%) were aged ≥70 years (70+). Both age 70+ and female sex were univariably associated with outcome (odds ratio [OR]4.68, 95%confidence interval [4.02-5.45], OR0.68[0.59-0.79], respectively;both p<  0.001). All comorbidities were univariably associated with outcome (p<0.001), and all but dyslipidemia remained significant after adjustment for age70+ and sex. The impact of comorbidities was attenuated after age-spline adjustment, only leaving female sex, diabetes mellitus (DM), chronic kidney disease (CKD), and chronic pulmonary obstructive disease (COPD) significantly associated (female OR0.65[0.55-0.75], DM OR1.47[1.26-1.72], CKD OR1.61[1.32-1.97], COPD OR1.30[1.07-1.59]). Pre-existing comorbidities in older patients negligibly (<6% in all comorbidities) mediated the association between higher age and outcome. CONCLUSIONS: Age is the main determinant of COVID-19 related in-hospital mortality, with negligible mediation effect of pre-existing comorbidities. TRIAL REGISTRATION: CAPACITY-COVID ( NCT04325412 ).

Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning.

BACKGROUND AND PURPOSE: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. METHODS: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. RESULTS: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65-0.79), 0.76 (95% CI 0.68-0.82) and 0.77 (95% CI 0.70-0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. CONCLUSION: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features.

Prognostic value of strain by feature-tracking cardiac magnetic resonance in arrhythmogenic right ventricular cardiomyopathy.

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is characterized by ventricular dysfunction and ventricular arrhythmias (VA). Adequate arrhythmic risk assessment is important to prevent sudden cardiac death. We aimed to study the incremental value of strain by feature-tracking cardiac magnetic resonance imaging (FT-CMR) in predicting sustained VA in ARVC patients. METHODS AND RESULTS: CMR images of 132 ARVC patients (43% male, 40.6 ± 16.0 years) without prior VA were analysed for global and regional right and left ventricular (RV, LV) strain. Primary outcome was sustained VA during follow-up. We performed multivariable regression assessing strain, in combination with (i) RV ejection fraction (EF); (ii) LVEF; and (iii) the ARVC risk calculator. False discovery rate adjusted P-values were given to correct for multiple comparisons and c-statistics were calculated for each model. During 4.3 (2.0-7.9) years of follow-up, 19% of patients experienced sustained VA. Compared to patients without VA, those with VA had significantly reduced RV longitudinal (P ≤ 0.03) and LV circumferential (P ≤ 0.04) strain. In addition, patients with VA had significantly reduced biventricular EF (P ≤ 0.02). After correcting for RVEF, LVEF, and the ARVC risk calculator separately in multivariable analysis, both RV and LV strain lost their significance [hazard ratio 1.03-1.18, P > 0.05]. Likewise, while strain improved the c-statistic in combination with RVEF, LVEF, and the ARVC risk calculator separately, this did not reach statistical significance (P ≥ 0.18). CONCLUSION: Both RV longitudinal and LV circumferential strain are reduced in ARVC patients with sustained VA during follow-up. However, strain does not have incremental value over RVEF, LVEF, and the ARVC VA risk calculator.

Rationale and design of the PHOspholamban RElated CArdiomyopathy intervention STudy (i-PHORECAST).

BACKGROUND: The p.Arg14del (c.40_42delAGA) phospholamban (PLN) pathogenic variant is a founder mutation that causes dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM). Carriers are at increased risk of malignant ventricular arrhythmias and heart failure, which has been ascribed to cardiac fibrosis. Importantly, cardiac fibrosis appears to be an early feature of the disease, occurring in many presymptomatic carriers before the onset of overt disease. As with most monogenic cardiomyopathies, no evidence-based treatment is available for presymptomatic carriers. AIMS: The PHOspholamban RElated CArdiomyopathy intervention STudy (iPHORECAST) is designed to demonstrate that pre-emptive treatment of presymptomatic PLN p.Arg14del carriers using eplerenone, a mineralocorticoid receptor antagonist with established antifibrotic effects, can reduce disease progression and postpone the onset of overt disease. METHODS: iPHORECAST has a multicentre, prospective, randomised, open-label, blinded endpoint (PROBE) design. Presymptomatic PLN p.Arg14del carriers are randomised to receive either 50 mg eplerenone once daily or no treatment. The primary endpoint of the study is a multiparametric assessment of disease progression including cardiac magnetic resonance parameters (left and right ventricular volumes, systolic function and fibrosis), electrocardiographic parameters (QRS voltage, ventricular ectopy), signs and/or symptoms related to DCM and ACM, and cardiovascular death. The follow-up duration is set at 3 years. BASELINE RESULTS: A total of 84 presymptomatic PLN p.Arg14del carriers (n = 42 per group) were included. By design, at baseline, all participants were in New York Heart Association (NHYA) class I and had a left ventricular ejection fraction > 45% and < 2500 ventricular premature contractions during 24-hour Holter monitoring. There were no statistically significant differences between the two groups in any of the baseline characteristics. The study is currently well underway, with the last participants expected to finish in 2021. CONCLUSION: iPHORECAST is a multicentre, prospective randomised controlled trial designed to address whether pre-emptive treatment of PLN p.Arg14del carriers with eplerenone can prevent or delay the onset of cardiomyopathy. iPHORECAST has been registered in the clinicaltrials.gov-register (number: NCT01857856).

Residual cardiovascular risk reduction guided by lifetime benefit estimation in patients with symptomatic atherosclerotic disease: effectiveness and cost-effectiveness.

AIMS: To determine the (cost)-effectiveness of blood pressure lowering, lipid-lowering, and antithrombotic therapy guided by predicted lifetime benefit compared to risk factor levels in patients with symptomatic atherosclerotic disease. METHODS AND RESULTS: For all patients with symptomatic atherosclerotic disease in the UCC-SMART cohort (1996-2018; n = 7697) two treatment strategies were compared. The lifetime benefit-guided strategy was based on individual estimation of gain in cardiovascular disease (CVD)-free life with the SMART-REACH model. In the risk factor-based strategy, all patients were treated the following: low-density lipoprotein cholesterol (LDL-c) < 1.8 mmol/L, systolic blood pressure <140 mmHg, and antithrombotic medication. Outcomes were evaluated for the total cohort using a microsimulation model. Effectiveness was evaluated as total gain in CVD-free life and events avoided, cost-effectiveness as incremental cost-effectivity ratio (ICER). In comparison to baseline treatment, treatment according to lifetime benefit would lead to an increase of 24 243 CVD-free life years [95% confidence interval (CI) 19 980-29 909] and would avoid 940 (95% CI 742-1140) events in the next 10 years. For risk-factor based treatment, this would be an increase of 18 564 CVD-free life years (95% CI 14 225-20 456) and decrease of 857 (95% CI 661-1057) events. The ICER of lifetime benefit-based treatment with a treatment threshold of ≥1 year additional CVD-free life per therapy was €15 092/QALY gained and of risk factor-based treatment €9933/QALY gained. In a direct comparison, lifetime benefit-based treatment compared to risk factor-based treatment results in 1871 additional QALYs for the price of €36 538/QALY gained. CONCLUSION: Residual risk reduction guided by lifetime benefit estimation results in more CVD-free life years and more CVD events avoided compared to the conventional risk factor-based strategy. Lifetime benefit-based treatment is an effective and potentially cost-effective strategy for reducing residual CVD risk in patients with clinical manifest vascular disease.

Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review.

OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data. STUDY DESIGN AND SETTING: We systematically searched the literature on published papers between 2018 and 2019 about primary studies developing and/or validating clinical prediction models using any supervised ML methodology across medical fields. From the retrieved studies information about the amount and nature (e.g. missing completely at random, potential reasons for missingness) of missing data and the way they were handled were extracted. RESULTS: We identified 152 machine learning-based clinical prediction model studies. A substantial amount of these 152 papers did not report anything on missing data (n = 56/152). A majority (n = 96/152) reported details on the handling of missing data (e.g., methods used), though many of these (n = 46/96) did not report the amount of the missingness in the data. In these 96 papers the authors only sometimes reported possible reasons for missingness (n = 7/96) and information about missing data mechanisms (n = 8/96). The most common approach for handling missing data was deletion (n = 65/96), mostly via complete-case analysis (CCA) (n = 43/96). Very few studies used multiple imputation (n = 8/96) or built-in mechanisms such as surrogate splits (n = 7/96) that directly address missing data during the development, validation, or implementation of the prediction model. CONCLUSION: Though missing values are highly common in any type of medical research and certainly in the research based on routine healthcare data, a majority of the prediction model studies using machine learning does not report sufficient information on the presence and handling of missing data. Strategies in which patient data are simply omitted are unfortunately the most often used methods, even though it is generally advised against and well known that it likely causes bias and loss of analytical power in prediction model development and in the predictive accuracy estimates. Prediction model researchers should be much more aware of alternative methodologies to address missing data.

Impact of cardiovascular disease and cardiovascular risk factors in hospitalised COVID-19 patients.

BACKGROUND: Hospitalised COVID-19 patients with underlying cardiovascular disease (CVD) and cardiovascular risk factors appear to be at risk of poor outcome. It is unknown if these patients should be considered a vulnerable group in healthcare delivery and healthcare recommendations during the COVID-19 pandemic. METHODS: A systematic literature search was performed to answer the following question: In which hospitalised patients with proven COVID-19 and with underlying CVD and cardiovascular risk factors should doctors be alert to a poor outcome? Relevant outcome measures were mortality and intensive care unit admission. Medline and Embase databases were searched using relevant search terms until 9 June 2020. After systematic analysis, 8 studies were included. RESULTS: Based on the literature search, there was insufficient evidence that CVD and cardiovascular risk factors are significant predictors of mortality and poor outcome in hospitalised patients with COVID-19. Due to differences in methodology, the level of evidence of all studies was graded 'very low' according to the Grading Recommendations Assessment, Development and Evaluation methodology. It is expected that in the near future, two multinational and multicentre European registries (CAPACITY-COVID and LEOSS) will offer more insight into outcome in COVID-19 patients. CONCLUSION: This literature review demonstrated there was insufficient evidence to identify CVD and cardiovascular risk factors as important predictors of poor outcome in hospitalised COVID-19 patients. However, patients with CVD and cardiovascular risk factors remain vulnerable to infectious disease outbreaks. As such, governmental and public health COVID-19 recommendations for vulnerable groups apply to these patients.

Multi-omics integration identifies key upstream regulators of pathomechanisms in hypertrophic cardiomyopathy due to truncating MYBPC3 mutations.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways and key regulators causing the disease. Therefore, we employed a multi-omics approach to study the pathomechanisms underlying HCM comparing patient hearts harboring MYBPC3 mutations to control hearts. RESULTS: Using H3K27ac ChIP-seq and RNA-seq we obtained 9310 differentially acetylated regions and 2033 differentially expressed genes, respectively, between 13 HCM and 10 control hearts. We obtained 441 differentially expressed proteins between 11 HCM and 8 control hearts using proteomics. By integrating multi-omics datasets, we identified a set of DNA regions and genes that differentiate HCM from control hearts and 53 protein-coding genes as the major contributors. This comprehensive analysis consistently points toward altered extracellular matrix formation, muscle contraction, and metabolism. Therefore, we studied enriched transcription factor (TF) binding motifs and identified 9 motif-encoded TFs, including KLF15, ETV4, AR, CLOCK, ETS2, GATA5, MEIS1, RXRA, and ZFX. Selected candidates were examined in stem cell-derived cardiomyocytes with and without mutated MYBPC3. Furthermore, we observed an abundance of acetylation signals and transcripts derived from cardiomyocytes compared to non-myocyte populations. CONCLUSIONS: By integrating histone acetylome, transcriptome, and proteome profiles, we identified major effector genes and protein networks that drive the pathological changes in HCM with mutated MYBPC3. Our work identifies 38 highly affected protein-coding genes as potential plasma HCM biomarkers and 9 TFs as potential upstream regulators of these pathomechanisms that may serve as possible therapeutic targets.

BIO FOr CARE: biomarkers of hypertrophic cardiomyopathy development and progression in carriers of Dutch founder truncating MYBPC3 variants-design and status.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease, commonly caused by truncating variants in the MYBPC3 gene. HCM is an important cause of sudden cardiac death; however, overall prognosis is good and penetrance in genotype-positive individuals is incomplete. The underlying mechanisms are poorly understood and risk stratification remains limited. AIM: To create a nationwide cohort of carriers of truncating MYBPC3 variants for identification of predictive biomarkers for HCM development and progression. METHODS: In the multicentre, observational BIO FOr CARe (Identification of BIOmarkers of hypertrophic cardiomyopathy development and progression in Dutch MYBPC3 FOunder variant CARriers) cohort, carriers of the c.2373dupG, c.2827C > T, c.2864_2865delCT and c.3776delA MYBPC3 variants are included and prospectively undergo longitudinal blood collection. Clinical data are collected from first presentation onwards. The primary outcome constitutes a composite endpoint of HCM progression (maximum wall thickness ≥ 20 mm, septal reduction therapy, heart failure occurrence, sustained ventricular arrhythmia and sudden cardiac death). RESULTS: So far, 250 subjects (median age 54.9 years (interquartile range 43.3, 66.6), 54.8% male) have been included. HCM was diagnosed in 169 subjects and dilated cardiomyopathy in 4. The primary outcome was met in 115 subjects. Blood samples were collected from 131 subjects. CONCLUSION: BIO FOr CARe is a genetically homogeneous, phenotypically heterogeneous cohort incorporating a clinical data registry and longitudinal blood collection. This provides a unique opportunity to study biomarkers for HCM development and prognosis. The established infrastructure can be extended to study other genetic variants. Other centres are invited to join our consortium.

Risk stratification and subclinical phenotyping of dilated and/or arrhythmogenic cardiomyopathy mutation-positive relatives: CVON eDETECT consortium.

In relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy, early detection of disease onset is essential to prevent sudden cardiac death and facilitate early treatment of heart failure. However, the optimal screening interval and combination of diagnostic techniques are unknown. The clinical course of disease in index patients and their relatives is variable due to incomplete and age-dependent penetrance. Several biomarkers, electrocardiographic and imaging (echocardiographic deformation imaging and cardiac magnetic resonance imaging) techniques are promising non-invasive methods for detection of subclinical cardiomyopathy. However, these techniques need optimisation and integration into clinical practice. Furthermore, determining the optimal interval and intensity of cascade screening may require a personalised approach. To address this, the CVON-eDETECT (early detection of disease in cardiomyopathy mutation carriers) consortium aims to integrate electronic health record data from long-term follow-up, diagnostic data sets, tissue and plasma samples in a multidisciplinary biobank environment to provide personalised risk stratification for heart failure and sudden cardiac death. Adequate risk stratification may lead to personalised screening, treatment and optimal timing of implantable cardioverter defibrillator implantation. In this article, we describe non-invasive diagnostic techniques used for detection of subclinical disease in relatives of index patients with dilated cardiomyopathy and arrhythmogenic cardiomyopathy.