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.
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.
Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): The Netherlands Organisation for Health Research and Development (ZonMw) University of Amsterdam Research Priority Area Medical Integromics OnBehalf CAPACITY-COVID19 Registry Background The electrocardiogram (ECG) is an easy to assess, widely available and inexpensive tool that is frequently used during the work-up of hospitalized COVID-19 patients. So far, no study has been conducted to evaluate if ECG-based machine learning models are able to predict all-cause in-hospital mortality in COVID-19 patients. Purpose With this study, we aim to evaluate the value of using the ECG to predict in-hospital all-cause mortality of COVID-19 patients by analyzing the ECG at hospital admission, comparing a logistic regression based approach and a DNN based approach. Secondly, we aim to identify specific ECG features associated with mortality in patients diagnosed with COVID-19. Methods and results We studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw-format 12-lead ECGs recorded after admission (<72 hours) were collected, manually assessed, and annotated using pre-defined ECG features. Using data from five out of seven centers (n = 634), two mortality prediction models were developed: (a) a logistic regression model using manually annotated ECG features, and (b) a pre-trained deep neural network (DNN) using the raw ECG waveforms. Data from two other centers (n = 248) were used for external validation. Performance of both prediction models was similar, with a mean area under the receiver operating curve of 0.69 [95%CI 0.55–0.82] for the logistic regression model and 0.71 [95%CI 0.59–0.81] for the DNN in the external validation cohort. After adjustment for age and sex, ventricular rate (OR 1.13 [95% CI 1.01–1.27] per 10 ms increase), right bundle branch block (3.26 [95% CI 1.15–9.50]), ST-depression (2.78 [95% CI 1.03–7.70]) and low QRS voltages (3.09 [95% CI 1.02-9.38]) remained as significant predictors for mortality. Conclusion This study shows that ECG-based prediction models at admission may be a valuable addition to the initial risk stratification in admitted COVID-19 patients. The DNN model showed similar performance to the logistic regression that needs time-consuming manual annotation. Several ECG features associated with mortality were identified. Figure 1: Overview of methods, using and example case: (left) logistic regression and (right) deep learning. This specific case had a high probability of in-hospital mortality (above the threshold of 30%). Follow-up of this case showed that the patient had died during admission. Abstract Figure. Overview of ML methods used
Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon2020 Introduction Wearable devices are gaining interest in the clinical assessment of physical behavior as a marker of disease severity. With the increased use, patient willingness and adherence will be increasingly important. As part of the SafeHeart study, examining the potential of physical behavior as an identifier of clinical deterioration in patients with an implantable cardioverter defibrillator (ICD), we present preliminary results on adherence to a wrist-worn wearable used for physical behavior assessment. Purpose Define the willingness to participate and long-term adherence to wearables in an ICD population. Methods This is a preliminary analysis of the ongoing multicenter, prospective, observational SafeHeart study. SafeHeart is aimed to construct a personalized prediction engine for ICD therapy using wearable-assessed physical behavior, remote ICD monitoring, electronic health records, and patient-reported data. The study will enroll 400 participants with an ICD with or without cardiac resynchronization therapy (CRT-D). In this preliminary analysis, wearable data was analyzed for the first 50 participants, where inclusion required a minimum of 1 month of follow up data. No data from the wearables were provided to the participants. The wrist-worn wearables were used continuously (day and night) for up to 12 months of follow-up. Adherence to the wearable was measured through patient-reported (subjective) adherence and wearable-measured (objective) adherence. Data were extracted from the wearables and non-wear time was detected via open source algorithms. A valid day was set to 22 hours of available wear time with 24-hour periods assessed from 3pm to 3pm for sleep metric capture. The willingness to participate and dropout rates were calculated for the same first 50 patients of the study. Results A total of 50 ICD participants were included in this study. The mean age was 65.1 years, 82 % male, with a mean follow up of 7 weeks, generating 326 patient weeks of data. Regarding patient-reported adherence, participants reported 81.4% full adherence and 18.6 % of participants reported very brief non-wear due to e.g. sauna or surgery. Of those reporting non-wear, 62.5% described one episode only of non-wear lasting 15-75 minutes. Regarding objectively measured adherence from wearable data, full adherence was shown in 91.7% of days. The mean number of valid days per participant was 41.3. Recruitment rates showed a willingness to participate of 50% (50/100) out of eligible subjects invited. No participants were lost to follow Conclusion Results show high adherence and reasonable willingness to participate without wearable adherence dropping over time. Comparison of objectively measured and patient-reported adherence showed similar values.
Funding Acknowledgements Type of funding sources: None. Background Remote monitoring (RM) for implantable cardioverter defibrillators (ICDs) enables early detection of clinical events and reduces inappropriate shocks. However, although RM has been associated with improved clinical outcomes at short to medium-term follow-up, data on long-term survival benefit of RM is currently lacking. Purpose To investigate long-term survival of patients using RM and compare this to a propensity score matched non-RM group. Methods This is a retrospective, observational single-center analysis of patients ≥18 years old implanted with an ICD (single- and dual chamber, biventricular or subcutaneous ICDs) between 1995 and 2021. Data was extracted from the electronic health records. Patients were included in the RM group only if they were started on RM at some point during follow-up. To adjust for differences in baseline characteristics between RM and non-RM patients, propensity score matching was performed in a 1:1 fashion (caliper 0.20) using greedy matching. Estimation of the propensity score was done using logistic regression with RM as the outcome variable, adjusting for 17 covariates (age, gender, year of device implant, type of device, clinical variables and medication at baseline). Time-to-event analysis was performed using Kaplan Meier survival analysis, with significance indicated using a log rank P value. Hazard ratios and 95% confidence intervals (CI) were calculated using a Cox proportional-hazards model. Results A total of 3199 ICD patients were included in this analysis. After propensity score matching for the probability of RM use, 1160 RM patients and 1160 non-RM patients with similar baseline characteristics were selected (mean age 59.1 ±14.8 years, 72.8% male). During a mean follow-up duration of 8.1 ±5.2 years, 363 (31.3%) patients died in the non-RM group and 111 (9.6%) patients died in the RM group (log rank p<0.001, Figure 1). The hazard ratio of RM for mortality was 0.261 (95 CI% 0.211 – 0.323, p=0.001). Conclusion Long-term retrospective analysis indicates a significant survival benefit in ICD patients using RM.
Funding Acknowledgements Type of funding sources: None. Background Remote monitoring (RM) for implantable cardioverter defibrillators (ICDs) is recommended as the standard of care in clinical guidelines. Presumably, the restrictions on face-to-face visits that were imposed during the coronavirus (COVID-19) pandemic have further accelerated the adoption of RM. However, quantitative real-world data on the uptake of RM during the COVID-19 pandemic is lacking. Purpose To assess the uptake of RM during the COVID-19 pandemic to a pre-COVID-19 period, and compare the arrhythmic burden between the two groups. Methods This is a substudy of the retrospective, observational single-center DISTANT-study. For this substudy, data from patients who were enrolled in the RM program after de novo ICD implantation (single- and dual chamber, biventricular or subcutaneous ICDs) were analysed. The time until RM was initiated per patient was calculated for patient implanted during the COVID-19 pandemic (March 2020-January 2021) and compared to a similar 10-month period pre-COVID-19 (May 2019-March 2020). ICD therapy (shock and/or anti-tachycardia pacing), non-sustained ventricular tachycardia (NSVT), supraventricular tachycardia (SVT) and mortality were registered for each patient. Patients <18 years old at implantation and patients with a follow-up of <6 months were excluded from this analysis. Results A total of 134 patients (72.4 % male, mean age 57.3 ±14.9 years) were eligible for this substudy, of which 61 patients in the COVID-19 group and 73 patients in the pre-COVID-19 group. In both groups there was a similar percentage of primary prevention ICD implantations (COVID-19: 43%, pre-COVID: 44%; p=0.888). During COVID-19, RM was initiated more promptly following ICD implantation compared to pre-COVID-19 (respectively 63 days vs. 131 days; p=0.007). Second, in the COVID-19 group 60.7% patients were enrolled in RM within 30 days following implantation compared to 39.7% in the pre-COVID-19 group (p=0.016). In terms of arrhythmic burden, no differences in the occurrence of ICD therapy (p=0.759), NSVT (p=0.267) and SVT (p=0.454) were observed. Conclusion During the COVID-19 pandemic RM was initiated more promptly following ICD implantation compared to before the pandemic, however, no differences in arrhythmic burden between groups were observed.
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