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Objective: This is the first prospective cohort study in Singapore to investigate the COVID-19 vaccine-associated myocarditis to understand its pathophysiology. Introduction: Acute myocarditis and other cardiovascular symptoms have been observed to be associated with the two mRNA-based coronavirus disease 2019 (COVID-19) vaccines-namely Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273)-currently in-use in Singapore. The mechanisms through which myocarditis occurs are unknown, hence our study aims to understand the pathophysiology of myocarditis associated with COVID-19 vaccines. Method(s): Patients with onset of cardiac manifestations were recruited from multiple hospital outpatient clinics between November 2021 and September 2022. Clinical history and physical examination data was collected with blood sample collection, echocardiography, 12-lead electrocardiogram (ECG), coronary angiography and magnetic resonance imaging (MRI) at recruitment and 6-month follow-up. Analysis of biomarkers, genetic, serological and MRI data was conducted. Result(s): As of 6 September 2022, a total of 5 patients have been enrolled (4 males, 1 female). The most commonly reported symptoms across all patients were chest pain/discomfort (80%), followed by palpitations (40%). MRI evidence of myocarditis has been detected in 2 (50%) of the male patients, of which both reported two or more symptoms occurring 1-2 days post-vaccination. Both patients have each received at least two doses of either the Pfizer-BioNTech BNT162b2 vaccine or Moderna mRNA-1273 vaccine. Their MRI findings were consistent with myocarditis. On late gadolinium enhancement (LGE) imaging, epicardial enhancement at the basal inferolateral segment and mid-wall enhancement at the apical anterior, lateral and inferior walls were observed in one patient. Patchy, mid-wall LGE in the basal inferior/inferolateral wall was observed in the other patient. No MRI evidence of myocarditis was available for the sole female patient. Conclusion(s): While more data is needed to definitively prove the association of the two mRNA-based Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273 COVID-19 vaccines with post-vaccination myocarditis, we believe our findings may support further investigations to enable risk stratification for vaccine-associated myocarditis and identify potential preventative strategies accordingly.
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Background: Hemorrhage, coagulopathy and thrombosis (HECTOR) are reported complications of coronavirus disease 2019 (COVID-19) however, more information is needed on the prevalence of these complications and their associated outcomes in intensive care unit (ICU) settings. Aim(s): To determine the prevalence and outcomes of HECTOR complications in ICU patients with COVID-19. Method(s): Observational cohort study spanning 229 ICUs across 32 countries. Patients >=16 years admitted for severe COVID-19 from 1st January 2020, through 31st December 2021 were included. Patient characteristics and clinical data were collected. Survival analysis estimated the instantaneous impact of HECTOR complications on ICU-mortality and discharge. Result(s): HECTOR complications occurred in 1,735 (14%) of 11,972 study-eligible patients. Acute thrombosis occurred in 1,249 (10%) patients, including 712 (57%) with pulmonary embolism, 413 (33%) with myocardial infarction, 93 (7.4%) with deep vein thrombosis, and 49 (3.9%) with ischemic stroke. Hemorrhagic complications were reported in 582 (4.9%) patients, including 276 (48%) with gastrointestinal hemorrhage, 83 (14%) with hemorrhagic stroke, and 77 (13%) with pulmonary hemorrhage. Disseminated intravascular coagulation occurred in 11 (0.09%) patients. Univariate analysis identified diabetes, hypertension, cardiac and kidney disease and ECMO as statistically-significant risk factors for HECTOR complications. Patients with versus without HECTOR complications suffered higher ICU-mortality at 28 days (25%vs.13%, p < 0.001), 90 days (32%vs.15%, p < 0.0001) and overall (44%vs.36%, p < 0.001). Among ICU survivors, the ICU stay was longer (median days 19vs.12, p < 0.001). ICU mortality was similar between patients with and without HECTOR complications (HR = 1.01, 95%CI 0.92-1.12, p = 0.783) where an increased hazard of ICU mortality with hemorrhage (HR = 1.26, 1.09-1.45, p = 0.002) was balanced by a reduced hazard of thrombosis (HR = 0.88, 0.79-0.99, p = 0.03). Kaplan-Meier curves are presented in the Figure. Conclusion(s): HECTOR events are frequent complications of severe COVID-19 in ICU patients. Hemorrhagic, but not thrombotic complications are associated with increased ICU-mortality.
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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a 'neutral' emotion. The other emotions-'bad', 'good', 'terrible', and 'great'-were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that 'Twitter for Android';was associated with 35.2% of tweets. It was followed by 'Twitter Web App';, 'Twitter for iPhone';, 'Twitter for iPad';, 'TweetDeck';, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021.
ABSTRACT
This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a neutral emotion. The other emotions - bad, good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish, French, Italian, and other languages. Third, the findings from source tracking showed that Twitter for Android was associated with 35.2% of tweets. It was followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other sources. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, and other domains. Finally, to support similar research in this field, we have developed a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.
Subject(s)
COVID-19ABSTRACT
This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.
Subject(s)
COVID-19ABSTRACT
COVID-19, a pandemic that the world has not seen in decades, has resulted in presenting a multitude of unprecedented challenges for student learning across the globe. The global surge in COVID-19 cases resulted in several schools, colleges, and universities closing in 2020 in almost all parts of the world and switching to online or remote learning, which has impacted student learning in different ways. This has resulted in both educators and students spending more time on the internet than ever before, which may be broadly summarized as both these groups investigating, learning, and familiarizing themselves with information, tools, applications, and frameworks to adapt to online learning. This paper takes an explorative approach to further investigate and analyze the impact of COVID-19 on such web behavior data related to online learning to interpret the associated interests, challenges, and needs. The study specifically focused on investigating Google Search-based web behavior data as Google is the most popular search engine globally. The impact of COVID-19 related to online learning-based web behavior on Google was studied for the top 20 worst affected countries in terms of the total number of COVID-19 cases, and the findings have been published as an open-access dataset. Furthermore, to interpret the trends in web behavior data related to online learning, the paper discusses a case study in terms of the impact of COVID-19 on the education system of one of these countries.
Subject(s)
COVID-19ABSTRACT
The United States of America has been the worst affected country in terms of the number of cases and deaths on account of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or COVID-19, a highly transmissible and pathogenic coronavirus that started spreading globally in late 2019. On account of the surge of infections, accompanied by hospitalizations and deaths due to COVID-19, and lack of a definitive cure at that point, a national emergency was declared in the United States on March 13, 2020. To prevent the rapid spread of the virus, several states declared stay at home and remote work guidelines shortly after this declaration of an emergency. Such guidelines caused schools, colleges, and universities, both private and public, in all the 50-United States to switch to remote or online forms of teaching for a significant period of time. As a result, Google, the most widely used search engine in the United States, experienced a surge in online shopping of remote learning-based software, systems, applications, and gadgets by both educators and students from all the 50-United States, due to both these groups responding to the associated needs and demands related to switching to remote teaching and learning. This paper aims to investigate, analyze, and interpret these trends of Google Shopping related to remote learning that emerged since March 13, 2020, on account of COVID-19 and the subsequent remote learning adoption in almost all schools, colleges, and universities, from all the 50-United States. The study was performed using Google Trends, which helps to track and study Google Shopping-based online activity emerging from different geolocations. The results and discussions show that the highest interest related to Remote Learning-based Google Shopping was recorded from Oregon, which was followed by Illinois, Florida, Texas, California, and the other states.
Subject(s)
COVID-19ABSTRACT
Background. Over 32 million cases of COVID-19 have been reported in the US. Outcomes range from mild upper respiratory infection to hospitalization, acute respiratory failure, and death. We assessed risk factors associated with severe disease, defined as hospitalization within 21 days of diagnosis or death, using US electronic health records (EHR). Methods. Patients in the Optum de-identified COVID-19 EHR database who were diagnosed with COVID-19 in 2020 were included in the analysis. Regularized multivariable logistic regression was used to identify risk factors for severe disease. Covariates included demographics, comorbidities, history of influenza vaccination, and calendar time. Results. Of the 193,454 eligible patients, 36,043 (18.6%) were hospitalized within 21 days of COVID-19 diagnosis, and 6,397 (3.3%) died. Calendar time followed an inverse J-shaped relationship where severe disease rates rapidly declined in the first 25 weeks of the pandemic. BMI followed an asymmetric V-shaped relationship with highest rates of disease severity observed at the extremes. In the multivariable model, older age had the strongest association with disease severity (odds ratios and 95% confidence intervals of significant associations in Figure). Other risk factors were male sex, uninsured status, underweight and obese BMI, higher Charlson Comorbidity Index, and individual comorbidities including hypertension. Asthma and overweight BMI were not associated with disease severity. Blacks, Hispanics, and Asians experienced higher odds of disease severity compared to Whites. Conclusion. Odds of hospitalization or death have decreased since the start of the pandemic, with the steepest decline observed up to mid-August, possibly reflecting changes in both testing and treatment. Older age is the most important predictor of severe COVID-19. Obese and underweight, but not overweight, BMI were associated with increased odds of disease severity when compared to normal weight. Hypertension, despite not being included in many guidelines for vaccine prioritization, is a significant risk factor. Pronounced health disparities remain across race and ethnicity after accounting for comorbidities, with minorities experiencing higher disease severity.
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Background. COVID-19 remains a threat to public health, with over 30 million cases in the US alone. As understanding of optimal patient care has improved, treatment guidelines have continued to evolve. This study characterized real-world trends in treatment for US patients hospitalized with COVID-19, stratified by whether patients required invasive ventilation. Methods. US patients diagnosed and hospitalized with COVID-19 between March 23 and December 31, 2020, in the Optum de-identified COVID-19 electronic health record (EHR) data set were identified. Both drug and procedure codes were used to ascertain medications, and both procedure and diagnostic codes were used to detect invasive ventilation during hospitalization. Medication trends were estimated by computing proportions of hospitalized patients receiving each drug weekly during the study period. Results. In this cohort of 71,366 hospitalized patients, the largest observed change in care was related to chloroquine/hydroxychloroquine (HCQ) (Figure). HCQ usage peaked at 87% of patients receiving invasive ventilation (54% without ventilation) in the first week of this study (March 23-29), but declined to < 5% of patients, regardless of ventilation status, by the end of May. In contrast, dexamethasone usage was 10% at baseline in patients receiving ventilation (1% without ventilation) and increased to a steady state of >85% of patients receiving ventilation ( >50% without ventilation) by the end of June. Similarly, remdesivir usage increased sharply from a baseline of 2% of patients and continued to rise to a peak of 79% of patients receiving invasive ventilation (44% without ventilation) in November before declining. Conclusion. Meaningful shifts in treatments for US patients hospitalized with COVID-19 were observed from March through December 2020. A dramatic decline was observed for HCQ use, likely owing to safety concerns, while usage of dexamethasone and remdesivir increased as evidence of their efficacy mounted. Across medications, usage was substantially more prevalent among patients requiring invasive ventilation compared with patients with less severe cases.
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INTRODUCTION: Despite reports suggesting an association between COVID-19 mRNA vaccination and pericarditis and myocarditis, detailed nationwide population-based data are sparsely available. We describe the incidence of pericarditis and myocarditis by age categories and sex after COVID-19 mRNA vaccination from a nationwide mass vaccination programme in Singapore. METHODS: The incidence of adjudicated cases of pericarditis and myocarditis following COVID-19 mRNA vaccination that were reported to the vaccine safety committee between January to July 2021 was compared with the background incidence of myocarditis in Singapore. RESULTS: As of end July 2021, a total of 34 cases were reported (9 pericarditis only, 14 myocarditis only, and 11 concomitant pericarditis and myocarditis) with 7,183,889 doses of COVID-19 mRNA vaccine administered. Of the 9 cases of pericarditis only, all were male except one. The highest incidence of pericarditis was in males aged 12-19 years with an incidence of 1.11 cases per 100,000 doses. Of the 25 cases of myocarditis, 80% (20 cases) were male and the median age was 23 years (range 12-55 years) with 16 cases after the second dose. A higher-than-expected number of cases were seen in males aged 12-19 and 20-29 years, with incidence rates of 3.72 and 0.98 case per 100,000 doses, respectively. CONCLUSION: Data from the national registry in Singapore indicate an increased incidence of pericarditis and myocarditis in younger men after COVID-19 mRNA vaccination.
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Background : Critically-ill COVID-19 patients demonstrate a hypercoagulable state, hence necessitating thromboprophylaxis. However, in non-critically ill COVID-19 patients, the haemostatic profile is unknown. Aims : A prospective, observational study was performed to evaluate coagulation parameters, and thrombotic outcomes in critically and non-critically ill COVID-19 patients. Methods : Informed consent was obtained from 10 critically ill (oxygen dependent, PaO2/FiO2 ratio<300) PCR positive COVID-19 patients matched for age and gender with 10 non-critically ill patients (nonoxygen dependent). On recruitment, laboratory (FBC/LDH/CRP/ procalcitonin) and coagulation tests (PT/APTT/D-Dimer/Fibrinogen/ TCT/Factors II,V,VII,VIII,IX,X,XI/vWF/anti-thrombinIII/ProteinC/ ProteinS/antiphospholipid antibodies), Thromboelastography(TEG), Clot Waveform Analysis(CWA) were performed, with repeat TEG/ CWA every 3 days, till 21 days of admission or discharge. This study was DSRB approved and supported by an NHG-NCID grant. Results : The median age was 60 years(49.5, 64.5) with 16 males and 4 females. Median Padua score of critically ill patients was 5 with PaO2/FiO2 ratio 194.5 (174, 241). Hypercoagulability was present in critically ill patients with elevated median levels of Fibrinogen 5.6 g/L(4.9, 6.6), D-dimer 1.0 μg/ml(0.6, 1.4), Factor VIII 206%(171, 230), von Willebrand Factor 265%(206, 321) as compared with lower levels in non-critically ill patients. Hypercoagulability was shown in TEG with increased CRT Angle 78.9°(78.3, 80.0), CFF MA 34.6 mm(27.4, 38.6) and CFF A10 30.9 s (25.5, 34.0);and CWA had increased clot velocity, aPTT Min1 7.7%/s(6.4, 8.3). CK K, CK Angle, CK MA, CRT MA were higher in critically ill patients (Table 1). In noncritically ill patients, D-dimer levels were normal, 0.3 μg/mL(0.3, 0.4) while Factor VIII levels of 176%(157, 192) and vWF levels of 225%(158, 237) were mildly elevated, with TEG and CWA demonstrating no hypercoagulability. 2 critically-ill patients developed thromboembolism(stroke, DVT) while no non-critically ill patients (not on thromboprophylaxis) had thrombosis. Conclusions : Critically ill COVID-19 patients demonstrate a hypercoagulable state with raised fibrinogen and Factor VIII levels correlating with raised CK, CRT, CFF maximal amplitude and increased CWA clot velocity(min1), while non-critical patients showed an absence of hypercoagulability in global tests of haemostasis.
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Coronavirus disease 2019 (COVID-19) is associated with an increased risk of thromboembolic events in the acute setting. However, the abnormal thrombotic diathesis is not known to persist into the recovery phase of COVID-19 infection. We described 3 cases of ST-segment elevation myocardial infarction in healthy male patients who recovered from COVID-19 with no prior cardiovascular risk factors. They shared features of elevated von Willebrand factor antigen, factor VIII and D-dimer level. One patient had a borderline positive lupus anticoagulant. Intravascular ultrasound of culprit vessels revealed predominantly fibrotic plaque with minimal necrotic core. Clot waveform analysis showed parameters of hypercoagulability. They were treated with dual antiplatelet therapy, angiotensin-converting-enzyme inhibitor, beta blocker and statin. These cases highlight the strong thrombogenic nature of COVID-19 that persisted among patients who recovered from infection. Several suspected mechanisms could explain the association between vascular thrombosis in the convalescent period (endothelial dysfunction, hypercoagulability, systemic inflammatory response and vasculopathy). Additional studies on "long COVID" are essential for identifying endotheliopathy and thrombotic sequalae.