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)
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.
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 of 45 Dutch hospitals, 4,806 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 in-hospital mortality. The effect of comorbidities on the relation of age with in-hospital mortality was evaluated using mediation analysis.Results: In-hospital COVID-19 related mortality occurred in 1,108 (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).
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. Method and results We used data from the CAPACITY-COVID registry and LEOSS study. Multivariable modified Poisson regression models were fitted to assess the association between different types of pre-existent heart disease and in-hospital mortality. 10,481 patients with COVID-19 were included (22.4% aged 66-75 years; 38.7% female) of which 30.5% had a history of cardiac disease. Patients with heart disease were older, predominantly male and more likely to have other comorbid conditions when compared to those without. COVID-19 symptoms at presentation did not differ between these groups. Mortality was higher in patients with cardiac disease (30.3%; n=968 versus 15.7%; n=1143). However, following multivariable adjustment this difference was not significant (adjusted risk ratio (aRR) 1.06 [95% CI 0.98-1.15, p-value 0.13]). Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for NYHA III/IV heart failure (aRR 1.43 [95% CI 1.22-1.68, p-value <0.001]) and atrial fibrillation (aRR 1.14 [95% CI 1.04-1.24, p-value 0.01]). None of the other heart disease subtypes, including ischemic heart disease, remained significant after multivariable adjustment. Conclusion There is considerable heterogeneity in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with severe heart failure are at greatest risk of death when hospitalized with COVID-19.
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.
Introduction The direction of blood flow in the left ventricle (LV) is determined by intraventricular pressure gradients (IVPGs) between apex and base, which are altered when cardiac function declines. New cardiac magnetic resonance (CMR) post-processing software enables estimating LV-IVPGs. To date, the prognostic value of CMR derived IVPGs in patients with dilated cardiomyopathy (DCM) remains unknown. Methods DCM patients from the Maastricht Cardiomyopathy Registry, who underwent a CMR, were included. The software estimates the LV-IVPGs (between apex and base) by using the myocardial movement and velocity of a reconstructed 3D-LV model (derived from feature-tracking strain analysis of 2-, 3- and 4-chamber cine images). The primary outcome was a combined endpoint of heart failure (HF) hospitalisations, life-threatening arrhythmias and (sudden) cardiac death. Results In total, 447 DCM patients were included (age 55 interquartile range [46–63] years; 60% male). During a median follow-up of 6 [4–9] years, 66 patients (15%) reached the primary endpoint. In 168 patients (38%), a temporary pressure reversal from base-apex to apex-base during the systolic-diastolic transition was observed (figure). After correction for covariates that were univariably associated with outcome (p<0.100, age, NYHA-class≥3, and left atrial (LA) conduit strain), flow reversal from base-apex to apex-base in the diastole was independently associated with outcome in the total cohort (HR 2.91, 95%-Confidence interval (95%-CI) [1.16–7.32], p=0.023; Table). In patients without pressure reversal (N=279) in the systolic-diastolic transition, IVPG during the total cardiac cycle (HR 0.88 [0.81–0.96], p=0.003), the systolic ejection force (HR 0.92 [0.87–0.97], p=0.003), and the E-wave decelerative force “C” (passive diastolic filling, HR 0.85 [0.74–0.97], p=0.013) were predictors of outcome, independent of other covariates (age, sex, NYHA class ≥3, LV ejection fraction, late gadolinium enhancement, LV longitudinal strain, LA volume index and LA conduit strain, table). Conclusion CMR-derived LV-IVPG analysis showed pressure reversal in the systolic-diastolic transition in one-third of DCM patients, and flow reversal was an independent predictor of worse outcome in these patients. In patients without this pressure reversal, LV-IVPG during the total cardiac cyle, the systolic ejection force, and the E-wave decelerative force were predictors of outcome, independent of all evauluated clinical and imaging parameters. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Netherlands Cardiovascular Research Initiative (initiative with support of the Dutch Heart Foundation) and CVON (She-PREDICTS, grant 2017-21 & CVON-DCVA Double Dosis 2021)
Background/Objectives Titin truncating variants (TTNtv) are the most prevalent genetic cause of dilated cardiomyopathy (DCM), resulting in upregulation of cardiac transcripts of oxidative phosphorylation (1,2). However, the underlying molecular mechanism(s) and cellular consequences of these findings remain unknown. Methods and results To gain insight into the metabolic changes and cellular consequences of a TTNtv, metabolic, mitochondrial, and survival pathways were studied in human TTNtv DCM hearts and isolated cardiomyocytes of TTNtv mice. TTNtv resulted in a significant increase of cardiac transcripts of glycolysis, citric acid cycle, mitochondrial fission, autophagy, and apoptosis when comparing RNAseq in 24 TTNtv and 27 mutation-negative DCM cardiac biopsies. Furthermore, a decrease in the area of myofibrils in human TTNtv hearts (TTNtv vs. mutation-negative DCM: 46%, and 62%, P=0.001), and an increase of mitochondrial (49% and 31%, P=0,001) and autophagosome areas (4% and 2%, P=0.002) was observed using transmission electron microscopy (TEM). Similar patterns of cardiomyocyte disorganization and stress could be seen in TTNtv hearts of mice even without a phenotype. Additionally, observed swollen mitochondria by TEM and decreased quantity of OXPHOS proteins by immunoblotting in murine TTNtv hearts indicate mitochondrial stress. Mitochondrial oxygen consumption at baseline and the maximum respiration in TTNtv cardiomyocytes of mice increased by a factor of 1.8 and 1.5 respectively (both P≤0.05), compared to WT. Furthermore, palmitate oxidation in TTNtv cardiomyocytes increased by 1.3 fold (P=0.005) compared to WT mice, suggestive of increased energy demand in TTNtv. Conclusion Myofibrillar insufficiency in human TTNtv DCM augments the cardiac oxygen and energy consumption, leading to pronounced morphological and functional mitochondrial decompensation. Swelling, damage and fission of mitochondria is further characterized by autophagosome formation and increased apoptosis pathways in TTNtv hearts. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Double-Dose consortium by Dutch Cardiovascular Alliance (DCVA)
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