The Netherlands Heart Tissue Bank : Strengthening the cardiovascular research infrastructure with an open access Cardiac Tissue Repository.

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.

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.