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Background. In the early months of the COVID-19 pandemic, the vast majority of infected persons were migrant workers living in dormitories who were young and with few medical co-morbidities. In 2021, this shifted to the more vulnerable and elderly population within the local community. We examined trends amongst the hospitalised cases, in order to demonstrate changes in disease severity in association with the evolving demographics. Demographic shifts in hospitalised patients with COVID-19. Proportion of hospitalised patients with COVID-19 requiring intensive care over time in Singapore Methods. All patients with PCR-positive SARS-CoV-2 admitted from February 2020 to October 2021 were included, and subsequently stratified by their year of admission (2020 or 2021). Demographics were also classified by sex, ethnicity, as well as mode of transmission, namely i) imported cases, ii) locally-transmitted cases outside of migrant worker dormitories, and iii) migrant worker dormitory cases. We compared the baseline clinical characteristics, clinical presentation and outcomes. Results. A majority of cases were seen in 2020 (n=1359), compared with 2021 (n=422), due to the large outbreaks in migrant worker dormitories. Nevertheless, the greater proportion of locally-transmitted cases outside of dormitories in 2021 (78.7% vs 12.3%) compared with 2020 meant a significantly older population with more medical co-morbidities were exposed to COVID-19. This led to an observably higher proportion of patients with severe disease, presenting with raised inflammatory markers, need for therapeutics, supplemental oxygenation and higher mortality. Baseline characteristics of hospitalised patients with COVID-19 in Singapore over time. Conclusion. Changing demographics and the characteristics of the exposed populations are associated with distinct differences in clinical presentation and outcomes. Understanding demographic shifts may be crucial in appropriate allocation of healthcare resources in managing hospitalised patients with COVID-19.
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Background. The Coronavirus disease 2019 (COVID-19) pandemic continues to threaten many countries globally. Large-scale vaccination exercises have helped to reduce transmission and severity of disease. We sought to modify an existing clinical score (the ISARIC-4C mortality score) to include serological status to better prognosticate hospitalized patients with COVID-19. Methods. We examined the first 1781 consecutive hospitalized patients with polymerase chain reaction (PCR) confirmed COVID-19 in a tertiary academic centre. We divided the study population into those requiring intensive care and those who did not require throughout their inpatient stay. Baseline characteristics examined include medical comorbidities, vaccination status, SARS-CoV-2 serology spike protein, duration of fever and haemodynamics were compared. Adverse outcomes were defined as patients who required intensive care or mortality. Performance of the risk scores were measured by the area under receiver operating characteristic curves (AUC) in predicting adverse outcomes. Results. The 55 patients requiring intensive care during their inpatient stay tended to have persistent fever beyond 72 hours and had lower titres of spike protein antibodies. (58.9 (+/-105.3) U/mL vs 144.2 (+/-116.2) U/mL, p = 0.007). A high spike protein antibody titre >75 U/mL was independently protective for adverse outcomes (adjusted OR 0.15, 95% CI 0.04-0.53), even after adjusting for the ISARIC-4C score and the presence of persistent fever. Adding the serological status and presence of persistent fever to the ISARIC-4C score improved its performance in predicting adverse outcomes (AUC 0.84, 95% CI 0.78-0.89). Conclusion. Addition of the SARS-CoV-2 serology spike protein titre and prolonged fever to the ISARIC-4C mortality score helps to better prognosticate adverse clinical outcomes in hospitalized patients with COVID-19.
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Background. Early in the coronavirus disease 2019 (COVID-19) pandemic, a low incidence of cardiovascular complications was reported amongst hospitalised patients with COVID-19 in Singapore. Little was known about the trend of cardiovascular complications as the pandemic progressed. As such, we sought to examine the evolving trends in electrocardiographic and cardiovascular manifestations in patients hospitalised for COVID-19 infection. Methods. We examined the first 1781 consecutive hospitalised patients with polymerase chain reaction (PCR) confirmed COVID-19 in a tertiary academic centre. We divided the population based on those who had an abnormal electrocardiogram (ECG) and those who had a normal ECG, comparing the baseline characteristics and outcomes between the 2 groups. Cardiovascular complications such as acute myocardial infarction, stroke, pulmonary embolism, myocarditis and mortality were also examined over time. Results. Of the 261 (14.7%) patients presenting with abnormal ECG, they were more likely to be symptomatic with complaints of breathlessness, palpitations and chest pain. Sinus tachycardia was the most common arrhythmia. Troponin I levels (41.6+/-264.3 vs 97.0+/-482.9, p=0.047) and C-reactive protein levels (20.1+/-50.7 vs 13.9+/-24.1 mumol/L, p=0.003) were significantly higher amongst those with abnormal ECGs at presentation, with a higher prevalence of myocarditis (1.9% vs 0.5%, p=0.021), pulmonary embolism (1.9% vs 0.3%, p=0.009) and acute myocardial infarction (1.1% vs 0.1%, p=0.025). Over time, there was a trend towards a higher proportion of hospitalised patients with cardiovascular complications. Baseline characteristics of hospitalised patients with COVID-19, with or without abnormal ECG at presentation Changes in percentage of hospitalised patients with COVID-19, experiencing cardiovascular events (acute myocardial infarction, stroke, myocarditis, pulmonary embolism and death) over time Conclusion. A baseline ECG at presentation is a simple test that provides valuable information on potential cardiovascular complications in the context of COVID-19. Although the prevalence of abnormal ECGs is relatively low, it appears to be increasing over time amongst hospitalised patients with COVID-19.
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Background: Acute ischemic stroke (AIS) is a life-threatening complication of coronavirus disease 2019 (COVID-19) infection. Increasing reports suggest an association between COVID-19 and AIS, although the underlying mechanism remains uncertain. Objectives: We performed a systematic review to characterize the clinical characteristics, neuroimaging findings, and outcomes of AIS in COVID-19 patients. Methods: A literature search was performed in PubMed and Embase using a suitable keyword search strategy from 1st December 2019 to 29th May 2020. All studies reporting AIS occurrence in COVID-19 patients were included. Results: A total of 39 studies comprising 135 patients were studied. The pooled incidence of AIS in COVID-19 patients from observational studies was 1.2% (54/4466) with a mean age of 63.4 ± 13.1 years. The mean duration of AIS from COVID-19 symptoms onset was 10 ± 8 days, and the mean NIHSS score was 19 ± 8. Laboratory investigations revealed an elevated mean D-dimer (9.2 ± 14.8 mg/L) and fibrinogen (5.8 ± 2.0 g/L). Antiphospholipid antibodies were detected in a significant number of cases. The majority of AIS neuroimaging patterns observed was large vessel thrombosis, embolism or stenosis (62.1%, 64/103), followed by multiple vascular territory (26.2%, 27/103). A high mortality rate was reported (38.0%, 49/129). Conclusion: We report the pooled incidence of AIS in COVID-19 patients to be 1.2%, with a high mortality rate. Elevated D-dimer, fibrinogen and the presence of antiphospholipid antibodies appear to be prominent in COVID-19 patients with concomitant AIS, but further mechanistic studies are required to elucidate their role in pathogenesis.
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BACKGROUND/INTRODUCTION: There are little data on outcomes of COVID-19 patients with the presence of fever compared to the presence of symptoms. AIM: We examined the associations between symptomology, presence of fever and outcomes of a COVID-19 cohort. DESIGN AND METHODS: Between 23 January and 30 April 2020, 554 COVID-19 patients were admitted to a tertiary hospital in Singapore. They were allocated into four groups based on symptomology and fever-Group 1: asymptomatic and afebrile, Group 2: symptomatic but afebrile, Group 3: febrile but asymptomatic and Group 4: symptomatic and febrile. The primary outcomes were intensive care unit (ICU) admissions and mortality. The composite end-point included ICU admissions, mortality or any COVID-19 related end-organ involvement. RESULTS: There were differences in ferritin (P=0.003), C-reactive protein (CRP) levels (P<0.001) and lymphopenia (P=0.033) across all groups, with the most favourable biochemical profile in Group 1, and the least in Group 4. Symptomatic groups (Groups 2 and 4) had higher ICU admissions (1.9% and 6.0%, respectively, P=0.003) than asymptomatic groups (Groups 1 and 3). Composite end-point was highest in Group 4 (24.0%), followed by Group 3 (8.6%), Group 2 (4.8%) and Group 1 (2.4%) (P<0.001). The presence of fever (OR 4.096, 95% CI 1.737-9.656, P=0.001) was associated with the composite end-point after adjusting for age, pulse rate, comorbidities, lymphocyte, ferritin and CRP. Presence of symptoms was not associated with the composite end-point. DISCUSSION/CONCLUSION: In this COVID-19 cohort, presence of fever was a predictor of adverse outcomes. This has implications on the management of febrile but asymptomatic COVID-19 patients.
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COVID-19 , Humans , SARS-CoV-2ABSTRACT
Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine whether an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically linked coronavirus disease 2019 (COVID-19) cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.