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Aims Dilated cardiomyopathy (DCM) is a major cause of heart failure impairing patient wellbeing and imposing a substantial economic burden on society, but respective data are missing. This study aims to measure the quality of life (QoL) and societal costs of DCM patients. Met hods a nd results A cross-sectional evaluation of QoL and societal costs of DCM patients was performed through the 5-level EuroQol and the Medical Consumption Questionnaire and Productivity Cost Questionnaire, respectively. QoL was translated into numerical values (i.e. utilities). Costs were measured from a Dutch societal perspective. Final costs were extrapolated to 1 year, reported in 2022 Euros, and compared between DCM severity according to NYHA classes. A total of 550 DCM patients from the Maastricht cardiomyopathy registry were included. Mean age was 61 years, and 34% were women. Overall utility was slightly lower for DCM patients than the population mean (0.840 vs. 0.869, P = 0.225). Among EQ-5D dimensions, DCM patients scored lowest in 'usual activities'. Total societal DCM costs were €14 843 per patient per year. Cost drivers were productivity losses ( €7037) and medical costs ( €4621). Patients with more symptomatic DCM (i.e. NYHA class III or IV) had significantly higher average DCM costs per year compared to less symptomatic DCM ( €31 099 vs. €11 446, P < 0.001) and significantly lower utilities (0.631 vs. 0.883, P < 0.001). Conclusion DCM is associated with high societal costs and reduced QoL, in particular with high DCM severity.

Aim: Cardiac diseases remain a leading cause of cardiovascular disease (CVD) related hospitalisation and mortality. That is why research to improve our understanding of pathophysiological processes underlying cardiac diseases is of great importance. There is a strong need for healthy and diseased human cardiac tissue and related clinical data to accomplish this, since currently used animal and in vitro disease models do not fully grasp the pathophysiological processes observed in humans. This design paper describes the initiative of the Netherlands Heart Tissue Bank (NHTB) that aims to boost CVD-related research by providing an open-access biobank. Methods: The NHTB, founded in June 2020, is a non-profit biobank that collects and stores biomaterial (including but not limited to myocardial tissue and blood samples) and clinical data of individuals with and without previously known cardiac diseases. All individuals aged ≥ 18 years living in the Netherlands are eligible for inclusion as a potential future donor. The stored samples and clinical data will be available upon request for cardiovascular researchers. Conclusion: To improve the availability of cardiac tissue for cardiovascular research, the NHTB will include extensive (cardiac) biosamples, medical images, and clinical data of donors with and without a previously known cardiac disease. As such, the NHTB will function as a translational bridge to boost a wide range of cardiac disease-related fundamental and translational studies.

AIM: Cardiac diseases remain a leading cause of cardiovascular disease (CVD) related hospitalisation and mortality. That is why research to improve our understanding of pathophysiological processes underlying cardiac diseases is of great importance. There is a strong need for healthy and diseased human cardiac tissue and related clinical data to accomplish this, since currently used animal and in vitro disease models do not fully grasp the pathophysiological processes observed in humans. This design paper describes the initiative of the Netherlands Heart Tissue Bank (NHTB) that aims to boost CVD-related research by providing an open-access biobank.METHODS: The NHTB, founded in June 2020, is a non-profit biobank that collects and stores biomaterial (including but not limited to myocardial tissue and blood samples) and clinical data of individuals with and without previously known cardiac diseases. All individuals aged ≥ 18 years living in the Netherlands are eligible for inclusion as a potential future donor. The stored samples and clinical data will be available upon request for cardiovascular researchers.CONCLUSION: To improve the availability of cardiac tissue for cardiovascular research, the NHTB will include extensive (cardiac) biosamples, medical images, and clinical data of donors with and without a previously known cardiac disease. As such, the NHTB will function as a translational bridge to boost a wide range of cardiac disease-related fundamental and translational studies.

AIM: Cardiac diseases remain a leading cause of cardiovascular disease (CVD) related hospitalisation and mortality. That is why research to improve our understanding of pathophysiological processes underlying cardiac diseases is of great importance. There is a strong need for healthy and diseased human cardiac tissue and related clinical data to accomplish this, since currently used animal and in vitro disease models do not fully grasp the pathophysiological processes observed in humans. This design paper describes the initiative of the Netherlands Heart Tissue Bank (NHTB) that aims to boost CVD-related research by providing an open-access biobank.METHODS: The NHTB, founded in June 2020, is a non-profit biobank that collects and stores biomaterial (including but not limited to myocardial tissue and blood samples) and clinical data of individuals with and without previously known cardiac diseases. All individuals aged ≥ 18 years living in the Netherlands are eligible for inclusion as a potential future donor. The stored samples and clinical data will be available upon request for cardiovascular researchers.CONCLUSION: To improve the availability of cardiac tissue for cardiovascular research, the NHTB will include extensive (cardiac) biosamples, medical images, and clinical data of donors with and without a previously known cardiac disease. As such, the NHTB will function as a translational bridge to boost a wide range of cardiac disease-related fundamental and translational studies.

Contains fulltext : 284808.pdf (Publisher’s version ) (Open Access), BACKGROUND: Although genotyping allows family screening and influences risk-stratification in patients with nonischemic dilated cardiomyopathy (DCM) or isolated left ventricular systolic dysfunction (LVSD), its result is negative in a significant number of patients, limiting its widespread adoption. OBJECTIVES: This study sought to develop and externally validate a score that predicts the probability for a positive genetic test result (G+) in DCM/LVSD. METHODS: Clinical, electrocardiogram, and echocardiographic variables were collected in 1,015 genotyped patients from Spain with DCM/LVSD. Multivariable logistic regression analysis was used to identify variables independently predicting G+, which were summed to create the Madrid Genotype Score. The external validation sample comprised 1,097 genotyped patients from the Maastricht and Trieste registries. RESULTS: A G+ result was found in 377 (37%) and 289 (26%) patients from the derivation and validation cohorts, respectively. Independent predictors of a G+ result in the derivation cohort were: family history of DCM (OR: 2.29; 95% CI: 1.73-3.04; P < 0.001), low electrocardiogram voltage in peripheral leads (OR: 3.61; 95% CI: 2.38-5.49; P < 0.001), skeletal myopathy (OR: 3.42; 95% CI: 1.60-7.31; P = 0.001), absence of hypertension (OR: 2.28; 95% CI: 1.67-3.13; P < 0.001), and absence of left bundle branch block (OR: 3.58; 95% CI: 2.57-5.01; P < 0.001). A score containing these factors predicted a G+ result, ranging from 3% when all predictors were absent to 79% when ≥4 predictors were present. Internal validation provided a C-statistic of 0.74 (95% CI: 0.71-0.77) and a calibration slope of 0.94 (95% CI: 0.80-1.10). The C-statistic in the external validation cohort was 0.74 (95% CI: 0.71-0.78). CONCLUSIONS: The Madrid Genotype Score is an accurate tool to predict a G+ result in DCM/LVSD.

Item does not contain fulltext, BACKGROUND: The left atrium is an early sensor of left ventricular (LV) dysfunction. Still, the prognostic value of left atrial (LA) function (strain) on cardiac magnetic resonance (CMR) in dilated cardiomyopathy (DCM) remains unknown. OBJECTIVES: The goal of this study was to evaluate the prognostic value of CMR-derived LA strain in DCM. METHODS: Patients with DCM from the Maastricht Cardiomyopathy Registry with available CMR imaging were included. The primary endpoint was the combination of sudden or cardiac death, heart failure (HF) hospitalization, or life-threatening arrhythmias. Given the nonlinearity of continuous variables, cubic spline analysis was performed to dichotomize. RESULTS: A total of 488 patients with DCM were included (median age: 54 [IQR: 46-62] years; 61% male). Seventy patients (14%) reached the primary endpoint (median follow-up: 6 [IQR: 4-9] years). Age, New York Heart Association (NYHA) functional class >II, presence of late gadolinium enhancement (LGE), LV ejection fraction (LVEF), LA volume index (LAVI), LV global longitudinal strain (GLS), and LA reservoir and conduit strain were univariably associated with the outcome (all P < 0.02). LA conduit strain was a stronger predictor of outcome compared with reservoir strain. LA conduit strain, NYHA functional class >II, and LGE remained associated in the multivariable model (LA conduit strain HR: 3.65 [95% CI: 2.01-6.64; P < 0.001]; NYHA functional class >II HR: 1.81 [95% CI: 1.05-3.12; P = 0.033]; and LGE HR: 2.33 [95% CI: 1.42-3.85; P < 0.001]), whereas age, N-terminal pro-B-type natriuretic peptide, LVEF, left atrial ejection fraction, LAVI, and LV GLS were not. Adding LA conduit strain to other independent predictors (NYHA functional class and LGE) significantly improved the calibration, accuracy, and reclassification of the prediction model (P < 0.05). CONCLUSIONS: LA conduit strain on CMR is a strong independent prognostic predictor in DCM, superior to LV GLS, LVEF, and LAVI and incremen

Contains fulltext : 284809.pdf (Publisher’s version ) (Closed access)

Aims Patients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality. Methods and results We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable Poisson regression models were fitted to assess the association between different types of pre-existing heart disease and in-hospital mortality. A total of 16 511 patients with COVID-19 were included (21.1% aged 66-75 years; 40.2% female) and 31.5% had a history of heart disease. Patients with heart disease were older, predominantly male, and often had other comorbid conditions when compared with those without. Mortality was higher in patients with cardiac disease (29.7%; n= 1545 vs. 15.9%; n= 1797). However, following multivariable adjustment, this difference was not significant [adjusted risk ratio (aRR) 1.08, 95% confidence interval (CI) 1.02-1.15; P = 0.12 (corrected for multiple testing)]. Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for heart failure (aRR 1.19, 95% CI 1.10-1.30; P <0.018) particularly for severe (New York Heart Association class III/IV) heart failure (aRR 1.41, 95% CI 1.20-1.64; P < 0.018). None of the other heart disease subtypes, including ischaemic heart disease, remained significant after multivariable adjustment. Serious cardiac complications were diagnosed in <1% of patients. Conclusion Considerable heterogeneity exists in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with heart failure are at greatest risk of death when hospitalized with COVID-19. Serious cardiac complications are rare during hospitalization. [GRAPHICS] .

Item does not contain fulltext, PURPOSE: Accurate interpretation of variants detected in dilated cardiomyopathy (DCM) is crucial for patient care but has proven challenging. We applied a set of proposed refined American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria for DCM, reclassified all detected variants in robust genes, and associated these results to patients' phenotype. METHODS: The study included 902 DCM probands from the Maastricht Cardiomyopathy Registry who underwent genetic testing. Two gene panel sizes (extended n = 48; and robust panel n = 14) and two standards of variant classification (standard versus the proposed refined ACMG/AMP criteria) were applied to compare genetic yield. RESULTS: A pathogenic or likely pathogenic (P/LP) variant was found in 17.8% of patients, and a variant of uncertain significance (VUS) was found in 32.8% of patients when using method 1 (extended panel (n = 48) + standard ACMG/AMP), compared to respectively 16.9% and 12.9% when using method 2 (robust panel (n = 14) + standard ACMG/AMP), and respectively 14% and 14.5% using method 3 (robust panel (n = 14) + refined ACMG/AMP). Patients with P/LP variants had significantly lower event-free survival compared to genotype-negative DCM patients. CONCLUSION: Stringent gene selection for DCM genetic testing reduced the number of VUS while retaining ability to detect similar P/LP variants. The number of genes on diagnostic panels should be limited to genes that have the highest signal to noise ratio.

Contains fulltext : 231362.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 237914.pdf (Publisher’s version ) (Open Access), AIMS: To determine the prognostic value of multilevel assessment of fibrosis in dilated cardiomyopathy (DCM) patients. METHODS AND RESULTS: We quantified fibrosis in 209 DCM patients at three levels: (i) non-invasive late gadolinium enhancement (LGE) at cardiac magnetic resonance (CMR); (ii) blood biomarkers [amino-terminal propeptide of procollagen type III (PIIINP) and carboxy-terminal propeptide of procollagen type I (PICP)], (iii) invasive endomyocardial biopsy (EMB) (collagen volume fraction, CVF). Both LGE and elevated blood PICP levels, but neither PIIINP nor CVF predicted a worse outcome defined as death, heart transplantation, heart failure hospitalization, or life-threatening arrhythmias, after adjusting for known clinical predictors [adjusted hazard ratios: LGE 3.54, 95% confidence interval (CI) 1.90-6.60; P < 0.001 and PICP 1.02, 95% CI 1.01-1.03; P = 0.001]. The combination of LGE and PICP provided the highest prognostic benefit in prediction (likelihood ratio test P = 0.007) and reclassification (net reclassification index: 0.28, P = 0.02; and integrated discrimination improvement index: 0.139, P = 0.01) when added to the clinical prediction model. Moreover, patients with a combination of LGE and elevated PICP (LGE+/PICP+) had the worst prognosis (log-rank P < 0.001). RNA-sequencing and gene enrichment analysis of EMB showed an increased expression of pro-fibrotic and pro-inflammatory pathways in patients with high levels of fibrosis (LGE+/PICP+) compared to patients with low levels of fibrosis (LGE-/PICP-). This would suggest the validity of myocardial fibrosis detection by LGE and PICP, as the subsequent generated fibrotic risk profiles are associated with distinct cardiac transcriptomic profiles. CONCLUSION: The combination of myocardial fibrosis at CMR and circulating PICP levels provides additive prognostic value accompanied by a pro-fibrotic and pro-inflammatory transcriptomic profile in DCM patients with LGE and elevated PICP.

Contains fulltext : 227365.pdf (Publisher’s version ) (Closed access)

Contains fulltext : 218291.pdf (Publisher’s version ) (Open Access), BACKGROUND: Metabolomic profiling may have diagnostic and prognostic value in heart failure. This study investigated whether targeted blood and urine metabolomics reflects disease severity in patients with nonischemic dilated cardiomyopathy (DCM) and compared its incremental value on top of N-terminal prohormone of brain natriuretic peptide (NT-proBNP). METHODS AND RESULTS: A total of 149 metabolites were measured in plasma and urine samples of 273 patients with DCM and with varying stages of disease (patients with DCM and normal left ventricular reverse remodeling, n=70; asymptomatic DCM, n=72; and symptomatic DCM, n=131). Acylcarnitines, sialic acid and glutamic acid are the most distinctive metabolites associated with disease severity, as repeatedly revealed by unibiomarker linear regression, sparse partial least squares discriminant analysis, random forest, and conditional random forest analyses. However, the absolute difference in the metabolic profile among groups was marginal. A decision-tree model based on the top metabolites did not surpass NT-proBNP in classifying stages. However, a combination of NT-proBNP and the top metabolites improved the decision tree to distinguish patients with DCM and left ventricular reverse remodeling from symptomatic DCM (area under the curve 0.813 +/- 0.138 vs 0.739 +/- 0.114; P=0.02). CONCLUSION: Functional cardiac recovery is reflected in metabolomics. These alterations reveal potential alternative treatment targets in advanced symptomatic DCM. The metabolic profile can complement NT-proBNP in determining disease severity in nonischemic DCM.