Clinical presentation, disease course, and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease: a cohort study across 18 countries

Outcomes of Covid-19 among patients with ischemic heart disease: A propensity matched analysis

BACKGROUND

Prior research has linked cardiovascular diseases with higher COVID-19 mortality and worse hospital outcomes, particularly in severe heart failure. Large population-based data regarding the impact of pre-existing ischemic heart disease (IHD) on COVID-19 outcomes is not well established.

OBJECTIVE

To study the impact of COVID-19 infection on IHD hospital mortality and other outcomes.

METHODS

The study included a patient cohort from the 2020 and 2021 National Inpatient Sample (NIS) database. Propensity score matching was used to match the study cohort (COVID-19 with IHD) to controls (COVID-19 without IHD) using a 1:1 matching ratio. The outcomes analyzed were in-hospital mortality, rates of acute kidney injury (AKI), acute myocardial injury (AMI), cardiogenic shock, cardiac arrest, mechanical ventilation, tracheal intubation, pulmonary embolism (PE), ventricular tachycardia (VT), ventricular fibrillation (VF), length of stay (LOS), and total hospitalization charges.

RESULTS

A total of 2,532,652 patients met the inclusion criteria (1,199,008 females [47.3 %), predominantly Caucasian 1,456,203 (57.5 %); mean [SD] age 63, (5.4), including 29,315 (1.1 %) patients with a history of IHD. Following propensity matching, 4,772 COVID-19 patients with and without IHD were matched. IHD patients had higher rates of AMI (adjusted odds ratio (aOR) 3.75, 95 % CI 3.27-4.31, p < 0.001), cardiogenic shock (aOR 2.89, 95 % CI 1.60-5.19, p < 0.001), VT (aOR 3.26, 95 % CI 2.48-4.29, p < 0.001), and VF (aOR 2.23, 95 % CI 1.25-3.99, p < 0.001). The odds ratios of in-hospital mortality, AKI, PE, mechanical ventilation, tracheal intubation, and resource use were not significantly different.

CONCLUSION

A history of IHD does not impact COVID-19 mortality but increases the risk of in-hospital cardiac complications.

Burden of cardiovascular disease on coronavirus disease 2019 hospitalizations in the USA

BACKGROUND

Patients with cardiovascular disease (CVD) and risk factors have increased rates of adverse events and mortality after hospitalization for coronavirus disease 2019 (COVID-19). In this study, we attempted to identify and assess the effects of CVD on COVID-19 hospitalizations in the USA using a large national database.

METHODS

The current study was a retrospective analysis of data from the US National (Nationwide) Inpatient Sample from 2020. All adult patients 18 years of age and older who were admitted with the primary diagnosis of COVID-19 were included. The primary outcome was in-hospital mortality, while secondary outcomes included prolonged hospital length of stay, mechanical ventilation, and disposition other than home. Prolonged hospital length of stay was defined as a length of stay greater than the 75 th percentile for the full sample. The diagnoses were identified using the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes.

RESULTS

A total of 1 050 040 patients were included in the study, of which 454 650 (43.3%) had prior CVD. Patients with CVD had higher mortality during COVID-19 hospitalization (19.3 vs. 5.0%, P  < 0.001). Similarly, these patients had a higher rate of prolonged hospital length of stay (34.5 vs. 21.0%, P  < 0.001), required mechanical ventilation (15.4 vs. 5.6%, P  < 0.001), and were more likely to be discharged to a disposition other than home (62.5 vs. 32.3%, P  < 0.001). Mean hospitalization cost was also higher in patients with CVD during hospitalization ($24 023 vs. $15 320, P  < 0.001). Conditional logistic regression analysis showed that the odds of in-hospital mortality [odds ratio (OR), 3.23; 95% confidence interval (CI), 2.91-3.45] were significantly higher for COVID-19 hospitalizations with CVD, compared with those without CVD. Similarly, prolonged hospital length (OR, 1.82; 95% CI, 1.43-2.23), mechanical ventilation (OR, 3.31; 95% CI, 3.06-3.67), and disposition other than home (OR, 2.01; 95% CI, 1.87-2.21) were also significantly higher for COVID-19 hospitalizations with coronary artery disease.

CONCLUSION

Our study showed that the presence of CVD has a significant negative impact on the prognosis of patients hospitalized for COVID-19. There was an associated increase in mortality, length of stay, ventilator use, and adverse discharge dispositions among COVID-19 patients with CVD. Adjustment in treatment for CVD should be considered when providing care to patients hospitalized for COVID-19 to mitigate some of the adverse hospital outcomes.

The 123 COVID SCORE: A simple and reliable diagnostic tool to predict in-hospital death in COVID-19 patients on hospital admission

BACKGROUND

Patients hospitalized due to Coronavirus disease 2019 (COVID-19) are still burdened with high risk of death. The aim of this study was to create a risk score predicting in-hospital mortality in COVID-19 patients on hospital admission.

METHODS

Independent mortality predictors identified in multivariate logistic regression analysis were used to build the 123 COVID SCORE. Diagnostic performance of the score was evaluated using the area under the receiver-operating characteristic curve (AUROC).

RESULTS

Data from 673 COVID-19 patients with median age of 70 years were used to build the score. In-hospital death occurred in 124 study participants (18.4%). The final score is composed of 3 variables that were found predictive of mortality in multivariate logistic regression analysis: (1) age, (2) oxygen saturation on hospital admission without oxygen supplementation and (3) percentage of lung involvement in chest computed tomography (CT). Four point ranges have been identified: 0-5, 6-8, 9-11 and 12-17, respectively corresponding to low (1.5%), moderate (13.4%), high (28.4%) and very high (57.3%) risk of in-hospital death. The 123 COVID SCORE accuracy measured with the AUROC was 0.797 (95% CI 0.757-0.838; p<0.0001) in the study population and 0.774 (95% CI 0.728-0.821; p<0.0001) in an external validation cohort consisting of 558 COVID-19 patients.

CONCLUSIONS

The 123 COVID SCORE containing merely 3 variables: age, oxygen saturation, and percentage of lung involvement assessed with chest CT is a simple and reliable tool to predict in-hospital death in COVID-19 patients upon hospital admission.

Long COVID and cardiovascular disease: a prospective cohort study

BACKGROUND

Pre-existing cardiovascular disease (CVD) or cardiovascular risk factors have been associated with an increased risk of complications following hospitalisation with COVID-19, but their impact on the rate of recovery following discharge is not known.

OBJECTIVES

To determine whether the rate of patient-perceived recovery following hospitalisation with COVID-19 was affected by the presence of CVD or cardiovascular risk factors.

METHODS

In a multicentre prospective cohort study, patients were recruited following discharge from the hospital with COVID-19 undertaking two comprehensive assessments at 5 months and 12 months. Patients were stratified by the presence of either CVD or cardiovascular risk factors prior to hospitalisation with COVID-19 and compared with controls with neither. Full recovery was determined by the response to a patient-perceived evaluation of full recovery from COVID-19 in the context of physical, physiological and cognitive determinants of health.

RESULTS

From a total population of 2545 patients (38.8% women), 472 (18.5%) and 1355 (53.2%) had CVD or cardiovascular risk factors, respectively. Compared with controls (n=718), patients with CVD and cardiovascular risk factors were older and more likely to have had severe COVID-19. Full recovery was significantly lower at 12 months in patients with CVD (adjusted OR (aOR) 0.62, 95% CI 0.43 to 0.89) and cardiovascular risk factors (aOR 0.66, 95% CI 0.50 to 0.86).

CONCLUSION

Patients with CVD or cardiovascular risk factors had a delayed recovery at 12 months following hospitalisation with COVID-19. Targeted interventions to reduce the impact of COVID-19 in patients with cardiovascular disease remain an unmet need.

TRAIL REGISTRATION NUMBER

ISRCTN10980107.

Epidemiological features of SARS-CoV-2 Omicron infection under new control strategy: a cross-sectional study of the outbreak since December 2022 in Sichuan, China

BACKGROUND

A major shift in the "dynamic zero-COVID" policy was announced by China's National Health Commission on December 7, 2022, and the subsequent immediate large-scale outbreak of SARS-CoV-2 infections in the entire country has caused worldwide concern. This observational cross-sectional study aimed to describe the epidemiological characteristics of this outbreak in Sichuan, China.

METHODS

All data were self-reported online by volunteers. We described the epidemic by characterizing the infection, symptoms, clinical duration, severity, spatiotemporal clustering, and dynamic features of the disease. Prevalence ratio (PR), Odds ratios (ORs) and adjusted ORs were calculated to analyze the associations between risk factors and infection and the associations of risk factors with clinical severity using log-binomial and multivariable logistic regression models; 95% confidence intervals (CIs) and Wald test results were reported. The prevalence rates and clinical severity among different subgroups were compared using the Chi-square and trend Chi-square tests.

RESULTS

Between January 6 and 12, 2023, 138,073 volunteers were enrolled in this survey, and 102,645 were infected with COVID-19, holding a prevalence rate of 74.34%; the proportion of asymptomatic infections was 1.58%. Log-binomial regression revealed that the risk of infection increased among those living in urban areas. Multivariable logistic regression analysis showed that female sex, chronic diseases, older age and the fewer doses of vaccine received were associated with an increased risk of severe clinical outcomes after infection. We estimated the mean reproduction number during this pandemic was 1.83. The highest time-dependent reproduction number was 4.15; this number decreased below 1 after 11 days from December 7, 2022. Temporal trends revealed a single peak curve with a plateau pattern of incidence during the outbreak, whereas spatiotemporal clustering analysis showed that the onset in 21 cities in the Sichuan province had four-wave peaks.

CONCLUSIONS

The peak of the first wave of Omicron infection in Sichuan Province had passed and could be considered a snapshot of China under the new control strategy. There were significant increases in the risk of severe clinical outcomes after infection among females, with chronic diseases, and the elderly. The vaccines have been effective in reducing poor clinical outcomes.

Recommendations for the care of patients with cardiovascular disease in health emergency situations: a call to action

COVID-19 has had a negative impact on the health care of patients with cardiovascular disease and patients at high risk of cardiovascular disease. The restrictions affecting access to the health care system have conditioned the care received, resulting in poorer control and a higher risk of events. Taking action to improve the care provided during health emergencies is mandatory. It is important to promote the development of telemedicine and patient empowerment by fostering health literacy and a higher degree of self-care. In addition, primary care and coordination between health care levels should be improved. Moreover, the simplification and optimization of treatment, for example, using the cardiovascular polypill, have led to an improvement in adherence, better control of vascular risk factors, and a reduced risk of events. The present document provides specific recommendations for improving the care provided to patients under a health emergency.

Age Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study

OBJECTIVES

A high body mass index (BMI) is associated with an unfavorable disease course in COVID-19, but not among those who require admission to the ICU. This has not been examined across different age groups. We examined whether age modifies the association between BMI and mortality among critically ill COVID-19 patients.

DESIGN

An observational cohort study.

SETTING

A nationwide registry analysis of critically ill patients with COVID-19 registered in the National Intensive Care Evaluation registry.

PATIENTS

We included 15,701 critically ill patients with COVID-19 (10,768 males [68.6%] with median [interquartile range] age 64 yr [55-71 yr]), of whom 1,402 (8.9%) patients were less than 45 years.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

In the total sample and after adjustment for age, gender, Acute Physiology and Chronic Health Evaluation IV, mechanical ventilation, and use of vasoactive drugs, we found that a BMI greater than or equal to 30 kg/m 2 does not affect hospital mortality (adjusted odds ratio [OR adj ] = 0.98; 95% CI, 0.90-1.06; p = 0.62). For patients less than 45 years old, but not for those greater than or equal to 45 years old, a BMI greater than or equal to 30 kg/m 2 was associated with a lower hospital mortality (OR adj = 0.59; 95% CI, 0.36-0.96; p = 0.03).

CONCLUSIONS

A higher BMI may be favorably associated with a lower mortality among those less than 45 years old. This is in line with the so-called "obesity paradox" that was established for other groups of critically ill patients in broad age ranges. Further research is needed to understand this favorable association in young critically ill patients with COVID-19.

Sex differences in cardiovascular complications and mortality in hospital patients with covid-19: registry based observational study

Objective

To assess whether the risk of cardiovascular complications of covid-19 differ between the sexes and to determine whether any sex differences in risk are reduced in individuals with pre-existing cardiovascular disease.

Design

Registry based observational study.

Setting

74 hospitals across 13 countries (eight European) participating in CAPACITY-COVID (Cardiac complicAtions in Patients With SARS Corona vIrus 2 regisTrY), from March 2020 to May 2021.

Participants

All adults (aged ≥18 years), predominantly European, admitted to hospital with highly suspected covid-19 disease or covid-19 disease confirmed by positive laboratory test results (n=11 167 patients).

Main outcome measures

Any cardiovascular complication during admission to hospital. Secondary outcomes were in-hospital mortality and individual cardiovascular complications with ≥20 events for each sex. Logistic regression was used to examine sex differences in the risk of cardiovascular outcomes, overall and grouped by pre-existing cardiovascular disease.

Results

Of 11 167 adults (median age 68 years, 40% female participants) included, 3423 (36% of whom were female participants) had pre-existing cardiovascular disease. In both sexes, the most common cardiovascular complications were supraventricular tachycardias (4% of female participants, 6% of male participants), pulmonary embolism (3% and 5%), and heart failure (decompensated or de novo) (2% in both sexes). After adjusting for age, ethnic group, pre-existing cardiovascular disease, and risk factors for cardiovascular disease, female individuals were less likely than male individuals to have a cardiovascular complication (odds ratio 0.72, 95% confidence interval 0.64 to 0.80) or die (0.65, 0.59 to 0.72). Differences between the sexes were not modified by pre-existing cardiovascular disease; for the primary outcome, the female-to-male ratio of the odds ratio in those without, compared with those with, pre-existing cardiovascular disease was 0.84 (0.67 to 1.07).

Conclusions

In patients admitted to hospital for covid-19, female participants were less likely than male participants to have a cardiovascular complication. The differences between the sexes could not be attributed to the lower prevalence of pre-existing cardiovascular disease in female individuals. The reasons for this advantage in female individuals requires further research.

The Profile and All-Cause In-Hospital Mortality Dynamics of St-Segment Elevation Myocardial Infarction Patients during the Two Years of the COVID-19 Pandemic

During the coronavirus pandemic 2019 (COVID-19), some studies showed differences in the profile of subjects presenting with acute coronary syndromes as well as in overall mortality due to the delay of presentation and other complications. The purpose of this study was to compare the profile and outcomes, with emphasis on all-cause in-hospital mortality, of ST-elevation myocardial infarction (STEMI) subjects presenting to the emergency department during the pandemic period compared with a control group from the previous year, 2019. The study enrolled 2011 STEMI cases, which were divided into two groups-pre-pandemic (2019-2020) and pandemic period (2020-2022). Hospital admissions for a STEMI diagnosis sharply decreased during the COVID-19 period by 30.26% during the first year and 25.4% in the second year. This trend was paralleled by a significant increase in all-cause in-hospital mortality: 11.5% in the pandemic period versus 8.1% in the previous year. There was a significant association between SARS-CoV-2 positivity and all-cause in-hospital mortality, but no correlation was found between COVID-19 diagnosis and the type of revascularization. However, the profile of subjects presenting with STEMI did not change over time during the pandemic; their demographic and comorbid characteristics remained similar.

COVID-19 and the heart

BACKGROUND

There is evidence for a bi-directional relationship between COVID-19 and the cardiovascular (CV) system.

SOURCE OF DATA

Published literature.

AREAS OF AGREEMENT

Pre-existing heart failure (HF) increases the risk of mortality with COVID-19. CV complications are recognized, including increased rates of acute coronary syndromes, HF, arrhythmia and myocarditis. Drugs targeting the angiotensin system are safe and may provide prognostic benefit.

AREAS OF CONTROVERSY

Vaccination as a cause of myocarditis remains a key area of contention.

GROWING POINTS

As the pandemic progresses, we are gaining more data about the long-term effects of COVID-19 on the CV system: long COVID, and medium-to-long-term increases in CV risk.

AREAS TIMELY FOR DEVELOPING RESEARCH

Large-scale longitudinal studies will shed light on long-term CV outcomes with COVID-19. Furthermore, the differential effects of COVID-19 variants on the CV system must be investigated.

Prevalence, pathophysiology, prediction and health-related quality of life of long COVID: study protocol of the longitudinal multiple cohort CORona Follow Up (CORFU) study

INTRODUCTION

The variety, time patterns and long-term prognosis of persistent COVID-19 symptoms (long COVID-19) in patients who suffered from mild to severe acute COVID-19 are incompletely understood. Cohort studies will be combined to describe the prevalence of long COVID-19 symptoms, and to explore the pathophysiological mechanisms and impact on health-related quality of life. A prediction model for long COVID-19 will be developed and internally validated to guide care in future patients.

METHODS AND ANALYSIS

Data from seven COVID-19 cohorts will be aggregated in the longitudinal multiple cohort CORona Follow Up (CORFU) study. CORFU includes Dutch patients who suffered from COVID-19 at home, were hospitalised without or with intensive care unit treatment, needed inpatient or outpatient rehabilitation and controls who did not suffer from COVID-19. Individual cohort study designs were aligned and follow-up has been synchronised. Cohort participants will be followed up for a maximum of 24 months after acute infection. Next to the clinical characteristics measured in individual cohorts, the CORFU questionnaire on long COVID-19 outcomes and determinants will be administered digitally at 3, 6, 12, 18 and 24 months after the infection. The primary outcome is the prevalence of long COVID-19 symptoms up to 2 years after acute infection. Secondary outcomes are health-related quality of life (eg, EQ-5D), physical functioning, and the prevalence of thromboembolic complications, respiratory complications, cardiovascular diseases and endothelial dysfunction. A prediction model and a patient platform prototype will be developed.

ETHICS AND DISSEMINATION

Approval was obtained from the medical research ethics committee of Maastricht University Medical Center+ and Maastricht University (METC 2021-2990) and local committees of the participating cohorts. The project is supported by ZonMW and EuroQol Research Foundation. Results will be published in open access peer-reviewed scientific journals and presented at (inter)national conferences.

TRIAL REGISTRATION NUMBER

NCT05240742.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

OBJECTIVE

To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19.

DESIGN

Two stage individual participant data meta-analysis.

SETTING

Secondary and tertiary care.

PARTICIPANTS

46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021.

DATA SOURCES

Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge.

MODEL SELECTION AND ELIGIBILITY CRITERIA

Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor.

METHODS

Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters.

MAIN OUTCOME MEASURES

30 day mortality or in-hospital mortality.

RESULTS

Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28).

CONCLUSION

The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Prevalence, Predictors, and Outcomes of Myocardial Injury in Hospitalized COVID-19 Patients—An Observational Retrospective Study

COVID-19 mainly causes pulmonary manifestation; nonetheless, its systemic inflammatory response involves multiple organs, including the heart. We aimed to evaluate the prevalence, predictors, and outcomes of myocardial injury in hospitalized patients with SARS-CoV-2 infection. Methods and Results: We performed an observational retrospective analysis on patients hospitalized with COVID-19 in a moderate-sized community hospital system. Myocardial injury was defined as highly sensitive troponin T levels in the 99th percentile above the normal upper limit for the respective biological sex. Multivariable logistic regression models were fitted to assess the association between the myocardial-injury and the no-myocardial-injury groups for primary and secondary outcomes. A total of 1632 (49.3% male, 41.7% aged 60–79 years) patients with COVID-19 were included, out of which 312 (19.1%) had a myocardial injury. Patients with myocardial injury were older (36.9% > 80 years) and had higher cardiovascular-related comorbidities than those without. The prevalence of cardiovascular risk factors (78.5% vs. 52.0%) and cardiovascular diseases (78.2% vs. 56.1%) was much higher in the myocardial-injury group. Older age (50–64 years vs. <49 years; OR, 3.67 [1.99–6.74]), Angiotensin Receptor Blockers (ARBs) (OR, 1.44 [1.01–2.05]), Beta-blockers (OR, 2.37 [1.80–3.13]), and cardiovascular comorbidities (OR, 1.49 [1.09–2.05]) were strong predictors of cardiac injury after multivariable adjustment. Myocardial injury was strongly associated with ICU admission (adjusted OR, 1.68 [1.29–2.19]) and longer length of hospital stay (median days, 5 (3, 9) vs. 4 (2, 7)). The results do not show a significant difference in the use of mechanical ventilation (OR, 1.29 [0.87–1.89]) or in-hospital mortality (OR, 1.37 [0.98–1.91]) with respect to myocardial injury. Conclusion: This multicenter retrospective study of nearly 1600 patients revealed the following findings: Myocardial injury was observed in 1 out of 5 patients hospitalized with COVID-19 but was more often clinically insignificant. Patients of age > 65 had very high odds of having elevated troponin levels after adjusting for sex and other illnesses. Pre-existing cardiac diseases and risk factors were robust predictors of cardiac injury after adjusting for age and sex. In the adjusted model, myocardial injury was not associated with the requirement of mechanical ventilation or change in in-hospital mortality.

A Comparison of Germany and the United Kingdom Indicates That More SARS-CoV-2 Circulation and Less Restrictions in the Warm Season Might Reduce Overall COVID-19 Burden

(1) Background: Between March 2020 and January 2022 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused five infection waves in Europe. The first and the second wave was caused by wildtype SARS-CoV-2, while the following waves were caused by the variants of concern Alpha, Delta, and Omicron respectively. (2) Methods: In the present analysis, the first four waves were compared in Germany and the UK, in order to examine the COVID-19 epidemiology and its modulation by non-pharmaceutical interventions (NPI). (3) Results: The number of COVID-19 patients on intensive care units and the case fatality rate were used to estimate disease burden, the excess mortality to assess the net effect of NPI and other measures on the population. The UK was more severely affected by the first and the third wave while Germany was more affected by the second wave. The UK had a higher excess mortality during the first wave, afterwards the excess mortality in both countries was nearly identical. While most NPI were lifted in the UK in July 2021, the measures were kept and even aggravated in Germany. Nevertheless, in autumn 2021 Germany was much more affected, nearly resulting in a balanced sum of infections and deaths compared to the UK. Within the whole observation period, in Germany the number of COVID-19 patients on ICUs was up to four times higher than in the UK. Our results show that NPI have a limited effect on COVID-19 burden, seasonality plays a crucial role, and a higher virus circulation in a pre-wave situation could be beneficial. (4) Conclusions: Although Germany put much more effort and resources to fight the pandemic, the net balance of both countries was nearly identical, questioning the benefit of excessive ICU treatments and of the implementation of NPI, especially during the warm season.

Cardiac Registries During the COVID-19 Pandemic: Lessons Learned

PURPOSE OF THIS REVIEW

We discuss the role of observational studies and cardiac registries during the COVID-19 pandemic. We focus on published cardiac registries and highlight contributions to the field that have had clinical implications.

RECENT FINDINGS

We included observational studies of COVID-19 patients published in peer-reviewed medical journals with defined inclusion and exclusion criteria, defined study design, and primary outcomes. A PubMed and MEDLINE literature review results in 437 articles, of which 52 include patients with COVID-19 with cardiac endpoints. From July 2020 to December 2021, the average time from last data collected to publication was 8.9 ± 4.1 months, with an increasing trend over time (R = 0.9444, p < 0.0001). Of the 52 articles that met our inclusion criteria, we summarize main findings of 4 manuscripts on stroke, 14 on acute coronary syndrome, 4 on cardiac arrest, 7 on heart failure, 7 on venous thromboembolism, 5 on dysrhythmia, and 11 on different populations at risk for cardiovascular. Registries are cost effective, not disruptive to essential health services, and can be rapidly disseminated with short intervals between last data point collected and publication. In less than 2 years, cardiac registries have filled important gaps in knowledge and informed the care of COVID-19 patients with cardiovascular conditions.

Acute Coronary Syndrome in the COVID-19 Era-Differences and Dilemmas Compared to the Pre-COVID-19 Era

The COVID-19 pandemic has led to numerous negative implications for all aspects of society. Although COVID-19 is a predominant lung disease, in 10-30% of cases, it is associated with cardiovascular disease (CVD). The presence of myocardial injury in COVID-19 patients occurs with a frequency between 7-36%. There is growing evidence of the incidence of acute coronary syndrome (ACS) in COVID-19, both due to coronary artery thrombosis and insufficient oxygen supply to the myocardium in conditions of an increased need. The diagnosis and treatment of patients with COVID-19 and acute myocardial infarction (AMI) is a major challenge for physicians. Often the presence of mixed symptoms, due to the combined presence of COVID-19 and ACS, as well as possible other diseases, nonspecific changes in the electrocardiogram (ECG), and often elevated serum troponin (cTn), create dilemmas in diagnosing ACS in COVID-19. Given the often-high ischemic risk, as well as the risk of bleeding, in these patients and analyzing the benefit/risk ratio, the treatment of patients with AMI and COVID-19 is often associated with dilemmas and difficult decisions. Due to delays in the application of the therapeutic regimen, complications of AMI are more common, and the mortality rate is higher.

Sex- and age specific association of new-onset atrial fibrillation with in-hospital mortality in hospitalised COVID-19 patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a systemic disease with cardiovascular involvement, including cardiac arrhythmias. Notably, new-onset atrial fibrillation (AF) and atrial flutter (AFL) during hospitalisation in COVID-19 patients has been associated with increased mortality. However, how this risk is impacted by age and sex is still poorly understood.

METHODS

For this multicentre cohort study, we extracted demographics, medical history, occurrence of electrical disorders and in-hospital mortality from the large international patient registry CAPACITY-COVID. For each electrical disorder, prevalence during hospitalisation was calculated. Subsequently, we analysed the incremental prognostic effect of developing AF/AFL on in-hospital mortality, using multivariable logistic regression analyses, stratified for sex and age.

RESULTS

In total, 5782 patients (64% male; median age 67) were included. Of all patients 11.0% (95% CI 10.2-11.8) experienced AF and 1.6% (95% CI 1.3-1.9) experienced AFL during hospitalisation. Ventricular arrhythmias were rare (<0.8% (95% CI 0.6-1.0)) and a conduction disorder was observed in 6.3% (95% CI 5.7-7.0). An event of AF/AFL appeared to occur more often in patients with pre-existing heart failure. After multivariable adjustment for age and sex, new-onset AF/AFL was significantly associated with a poorer prognosis, exemplified by a two- to three-fold increased risk of in-hospital mortality in males aged 60-72 years, whereas this effect was largely attenuated in older male patients and not observed in female patients.

CONCLUSION

In this large COVID-19 cohort, new-onset AF/AFL was associated with increased in-hospital mortality, yet this increased risk was restricted to males aged 60-72 years.

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.

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.

Supplementary Information

The online version of this article (10.1007/s12471-022-01670-2) contains supplementary material, which is available to authorized users.

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 ).

Using the NYHA Classification as Forecasting Tool for Hospital Readmission and Mortality in Heart Failure Patients with COVID-19

During the COVID-19 pandemic, it was observed that patients with heart disease are more likely to be hospitalized and develop severe COVID-19. Cardiac disease takes the top position among patient comorbidities, heart failure (HF) prevalence reaching almost 5% in the general population older than 35 years in Romania. This retrospective study aimed to determine the potential use of the NYHA classification for HF in hospitalized patients with COVID-19 as prognostic tool for in-hospital mortality, length of hospitalization, and probability of rehospitalization for HF decompensation. We observed that patients with advanced HF had a history of significantly more comorbid conditions that are associated with worse disease outcomes than the rest of patients classified as NYHA I and II. However, regardless of existing diseases, NYHA III, and, especially, NYHA IV, patients were at greatest risk for mortality following SARS-CoV-2 infection. They required significantly longer durations of hospitalization, ICU admission for mechanical ventilation, and developed multiple severe complications. NYHA IV patients required a median duration of 20 days of hospitalization, and their in-hospital mortality was as high as 47.8%. Cardiac biomarkers were significantly altered in patients with SARS-CoV-2 and advanced HF. Although the study sample was small, all patients with NYHA IV who recovered from COVID-19 required a rehospitalization in the following month, and 65.2% of the patients at initial presentation died during the next six months. The most significant risk factor for mortality was the development of severe in-hospital complications (OR = 4.38), while ICU admission was the strongest predictor for rehospitalization (OR = 5.19). Our result highlights that HF patients continue to be vulnerable post SARS-CoV-2 infection. Physicians and policymakers should consider this population’s high likelihood of hospital readmissions when making discharge, hospital capacity planning, and post-discharge patient monitoring choices.