Epidemiology and infection
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During the coronavirus disease 2019 (COVID-19) pandemic, a new phenomenon manifesting as a multisystem inflammatory syndrome in children (MIS-C) which has a similar clinical presentation to Kawasaki disease, toxic shock syndrome and severe sepsis has emerged. Although the number of MIS-C reports is increasing, rare reports in Asia is still available. To our knowledge, this study is the largest series of published MIS-C cases in Iran. ⋯ Pleural, ascitic, ileitis and pericardial effusions were found in 18%, 11%, 4% and 2% of cases, respectively. In conclusion, this is a first large case series of hospitalised children who met criteria for MIS-C in Iran. There was a wide spectrum of presenting signs and symptoms; evidence of inflammation with abnormal values of CRP, ESR, D-dimer, ferritin and albumin; and multi-organ involvement.
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As the Middle East respiratory syndrome coronavirus (MERS-CoV) continues to occur in small outbreaks in Saudi Arabia, we aimed to assess the knowledge, attitudes and intended practices of healthcare workers (HCWs) during the early stage of the COVID-19 pandemic and compare worry levels with previous findings during the MERS-CoV outbreak in 2015. We sent an adapted version of our previously published MERS-CoV questionnaire to the same cohort of HCWs at a tertiary hospital in Saudi Arabia. ⋯ HCWs from the current cohort who felt greater anxiety from COVID-19 compared to MERS-CoV were less likely to have been exposed to MERS-CoV infected/suspected cases (odds ratio (OR) = 0.646, P = 0.042) and were less likely to have attended the hospital awareness campaign on COVID-19 (OR = 0.654, P = 0.035). We concluded that previous experience with MERS-CoV was associated with increased knowledge and adherence to protective hygienic practices, and reduction of anxiety towards COVID-19.
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Our study aimed to systematically analyse the risk factors of coronavirus disease 2019 (COVID-19) patients with severe disease. An electronic search in eight databases to identify studies describing severe or critically ill COVID-19 patients from 1 January 2020 to 3 April 2020. In the end, we meta-analysed 40 studies involving 5872 COVID-19 patients. ⋯ Patients with severe disease showed significantly lower platelet count (WMD = -18.63, 95%CI -30.86 to -6.40) and lymphocyte count (WMD = -0.35, 95%CI -0.41 to -0.30) but higher C-reactive protein (CRP; WMD = 42.7, 95%CI 31.12-54.28), lactate dehydrogenase (LDH; WMD = 137.4, 95%CI 105.5-169.3), white blood cell count（WBC), procalcitonin（PCT）, D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine（Cr）. Similarly, patients who died showed significantly higher WBC, D-dimer, ALT, AST and Cr but similar platelet count and LDH as patients who survived. These results indicate that older age, low platelet count, lymphopenia, elevated levels of LDH, ALT, AST, PCT, Cr and D-dimer are associated with severity of COVID-19 and thus could be used as early identification or even prediction of disease progression.
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The spread of COVID-19 is recent in India, which has within 4 months caused over 190 000 infections, as of 1 June 2020, despite four stringent lockdowns. With the current rate of the disease transmission in India, which is home to over 1.35 billion people, the infection spread is predicted to be worse than the USA in the upcoming months. To date, there is a major lack of understanding of the transmission dynamics and epidemiological characteristics of the disease in India, inhibiting effective measures to control the pandemic. ⋯ Given the current doubling time of infections, several states may face a major shortage of public beds and healthcare facilities soon. Weather has minimal effect on the infection spread in most Indian states. The study results will help policymakers to predict the trends of the disease spread in the upcoming months and devise better control measures.
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Observational Study
Development and validation of prognosis model of mortality risk in patients with COVID-19.
This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. ⋯ A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.