Cardiopulmonary long-term effects in patients after hospitalization due to COVID-19 infection

Background: Multiple studies have described acute effects of the Covid-19 infection on the heart, but little is known about the long-term cardiac and pulmonary effects and complications after recovery. The aim of this analysis was to deliver a comprehensive report of symptoms and possible long-term impairments after hospitalization because of Covid-19 infection as well as to try to identify predictors for Long-Covid. Method(s): This was a prospective, multicenter registry study. Patients with verified Covid-19 infection, who were treated as in-patients at our dedicated Covid hospital (Clinic Favoriten), have been included in this study. In all patients, testing was performed approximately 6 months post discharge. During the study visit the following tests and investigations were performed: Detailed patient history and clinical examination, transthoracic echocardiography, electrocardiography, cardiac magnetic resonance imaging (MRI), chest computed tomography (CT) scan, lung function test and a comprehensive list of laboratory parameters including cardiac bio markers. Result(s): Between July 2020 and October 2021, 150 patients were recruited. Sixty patients (40%) were female and the average age was 53.5+/-14.5 years. Of all patients, 92% had been admitted to our general ward and 8% had a severe course of disease, requiring admission to our intensive care unit. Six months after discharge the majority of patients still experienced symptoms and 75% fulfilled the criteria for Long-Covid. Only 24% were completely asymptomatic (figure 1). Echocardiography detected reduced global longitudinal strain (GLS) in 11%. Cardiac MRI revealed pericardial effusion in 18%. Furthermore, cardiac MRI showed signs of former peri-or myocarditis in 4%. Pulmonary CT scans identified post-infectious residues, such as bilateral ground glass opacities and fibrosis in 22%. Exertional dyspnea was associated with either reduced forced vital capacity measured during pulmonary function tests in 11%, with reduced GLS and/or diastolic dysfunction, thus providing evidence for a cardiac and/or pulmonary cause. Independent predictors for Long-Covid were markers of a more severe disease course like length of in-hospital stay, admission to an intensive care unit, type of ventilation as well as higher NT-proBNP and/or troponin levels. Conclusion(s): Even 6 months after recovery from Covid-19 infection, the majority of previously hospitalized patients still suffer from at least one symptom, such as chronic fatigue and/or exertional dyspnea. While there was no association between fatigue and cardiopulmonary abnormalities, impaired lung function, reduced GLS and/or diastolic dysfunction were significantly more prevalent in patients presenting with exertional dyspnea. On chest CT approximately one fifth of all patients showed post infectious changes in chest CT including evidence for myo-and pericarditis as well as accumulation of pericardial effusions.

Long-term cardiopulmonary effects after Covid-19 infection

Introduction: The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic of 2020 has an influence on people's lives worldwide, impacting global health and putting pressure on health care systems. Multiple studies have described acute effects of the Covid-19 infection on the heart, but little is known about the long-term cardiac effects and complications after recovery. The aim of this analysis was to deliver a comprehensive report of symptoms and long-term impairment after Covid-19 infection. Methods: This study was a prospective, multicenter registry study. Patients with verified COVID-19 infection, who were treated at our dedicated COVID hospital (Klinik Favoriten), have been included in this study. In all patients, testing was performed approximately 6 months post discharge. During each study visit the following tests and investigations were performed: detailed patient history and clinical examination, transthoracic echocardiography, electrocardiography, cardiac magnetic resonance imaging (MRI), pulmonary computed tomography (CT) scan, lung function test, spiroergometry and six-minute walk test (6MWT), and a comprehensive list of laboratory parameters including cardiac bio markers such as brain natriuretic peptide (NTpro BNP) and troponin T. Results: In this interim analysis of an ongoing trial, the first 65 patients are presented. Baseline values are shown in table 1: 34 (59%) were male and the median age was 48.5 years (36.4-59.3). 86% of all patients included so far had an only mild to moderate course of disease and 14% of them had a severe course and were admitted to our intensive care unit. At the time of the study visit, the majority of patients still complained about symptoms: 40% presented with fatigue and weakness, 36% with exertional dyspnea, 21% with vertigo, 17% had an impaired taste or smell. Only 28% were completely asymptomatic (figure 1). From a cardiac perspective, the only abnormal findings noted in echocardiography studies were reduced left ventricular global longitudinal strain. Cardiac MRI revealed pericardial effusion in 19%, however, these were only minimal (≤9mm) and not visible in echocardiography. Furthermore, cardiac MRI showed positive late gadolinium enhancement in 11%. Pulmonary function tests were abnormal in 16%. Pulmonary CT scans showed post infectious residues like bilateral ground glass opacities and fibrosis in 45%. Exercise capacity as measured by the 6-minute walk test with BORG Dyspnea Score and by spiroergometry was reduced in almost 40% of our study participants. Conclusions: This interim analysis showed that most previously hospitalized patients still suffer from chronic fatigue, exertional dyspnea and impaired cardiopulmonary function after Covid-19 infection. Furthermore, even though cardiac and pulmonary imaging revealed numerous pathologic findings, and exercise capacity was reduced, no correlations could be found with persisting symptoms.

Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

INTRODUCTION: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease. METHODS: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated. RESULTS: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates. CONCLUSION: Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.