The clinical dynamics of 18 cases of COVID-19 outside of Wuhan, China

Characteristics and outcomes of COVID-19 patients during the BA.5 omicron wave in Tehran, Iran: a prospective observational study

BACKGROUND

Omicron (B.1.1.529) is the fifth variant of concern of SARS-CoV-2, which has several subvariants. Clinical features of BA.1 and BA.2 infections have been described in the literature, but we have limited information about the clinical profile of BA.5, which caused the seventh wave in Iran.

METHODS

A prospective observational study was conducted on the BA.5 confirmed patients referred to Imam Khomeini Hospital Complex, Tehran, Iran, from 11th to 31st August 2022. The patients were divided into the two groups of outpatients and hospitalized patients, and their clinical, radiological, and laboratory data and outcomes were recorded and analyzed.

RESULTS

We included 193 patients with confirmed BA.5 infection, of whom 48 patients (24·8%) were hospitalized. The mean age of the patients was 45·3 ± 16·5 years, and 113 patients (58·5%) were female. The mean number of days patients had symptoms was 6·8 ± 2·4 days. The most common symptoms were weakness (69·9%), sore throat (67·4%), myalgia (66·3%), hoarseness (63·7%), headache (55·4%), fatigue (54·9%), and dry cough (50·3%). Fever and dyspnea were significantly more observed in the hospitalized patients (p < 0·0001). The COVID-19 vaccination rate was significantly lower in hospitalized patients than in outpatients (35/48-72·9% vs. 140/145 - 96·6%, p < 0·0001). The most common underlying diseases were hypertension (16·1%), diabetes mellitus (9·8%), and cardiovascular diseases (9·8%), all of which were significantly more common in hospitalized patients. Lung opacities were observed in 81·2% of hospitalized patients. By the end of our study, 1·5% of patients died despite receiving critical care services.

CONCLUSIONS

Our findings suggested that BA.5 symptoms are more non-respiratory and usually improve within 7 days. Although the proportion of hospitalized patients is still significant, very few patients require intensive care. COVID-19 vaccination is effective in reducing the hospitalization rate.

TRIAL REGISTRATION

Not applicable. This study is not a clinical trial.

Facteurs de risque de la sévérité de la COVID-19 chez des patients tunisiens aux Urgences de Sousse, Tunisie

INTRODUCTION

Despite the spread of COVID-19 in Tunisia and its impact on people, health and economy, few studies have investigated the profile of COVID-19 Tunisian patients.

AIM

Determine the epidemiological, clinical, para-clinical and therapeutic characteristics patients and identify the associated factors of severity.

METHODS

This is a retrospective study, conducted among confirmed COVID-19 patients consulting the hospital emergency department. We collected Data using from the patients' computerized files. We performed Data entry and analysis using SPSS 22.

RESULTS

We included 375 patients. The average age was 66.7±11.43 years with a sex ratio of 1.6. The most frequent comorbidities were diabetes (100%), hypertension (64.5%), and chronic heart disease (25.9%). The most frequent clinical signs were dyspnea (75.2%), asthenia (66.9%), cough (66.7%) and fever (60.3%). The most frequent biological abnormalities were biological inflammatory syndrome (96%) and elevation of troponin (69.3%). CT scans revealed lung damage in 34.1% of patients. As for treatments, 91.7% received antibiotics, 89% received corticosteroids, 89.3% received anticoagulants, and 85.1% received ventilation (42.6% non-invasive ventilation and 1.9% were intubated). Risk factors of severity were age, chronic heart disease and hypertension.

CONCLUSION

Knowing the particularities of Tunisian patients will help to install recommendations to improve the process of care and prevention.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

BACKGROUND

Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES

To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.

METHODS

The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.

RESULTS

A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.

CONCLUSION

We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

Influence Mechanism Analysis of the Spatial Evolution of Inter-Provincial Population Flow in China Based on Epidemic Prevention and Control

Based on big data on migration from the Baidu Map platform, this paper divides China's epidemic prevention and control efforts into four stages. Then, the characteristics and spatial patterns of daily population flows are studied by social network analysis. Subsequently, the exponential random graph model is used to investigate the influence of dynamic characteristics of changes in the spatial structure of the interprovincial population flow network during the postepidemic period. The spatial structure of the population flow network before, during, and after the epidemic shows significantly different characteristics, with epidemic prevention and control measures playing a significant role in restricting population flows. Interprovincial population flows have a certain degree of transmissibility, but two-way flows are not obvious. In addition, for regions with a larger resident population and a higher unemployment rate, a larger population tends to flow out. For regions with higher per capita GDP, the secondary and tertiary industries account for a relatively larger proportion, and the public environment is better. The more attractive a region is to the population, the higher is the tendency towards population inflows. Moreover, the level of medical care and epidemic prevention and control have become the main influencing factors of population movement.

Characteristics and Trends of COVID-19 Infection in a Tertiary Hospital in Romania: A Retrospective Study

(1) Background: The outbreak of the COVID-19 pandemic represented a real challenge for all of humanity. Characterized by a complex spectrum of signs and symptoms, by various severity degrees, the disease spread rapidly around the world. After more than two and half years since the beginning of COVID-19 pandemic, in the context of a paradoxical, enigmatic, and relentless COVID-19, the objective of the current study was to evaluate the characteristics and evolution of patients with SARS-CoV-2 infection, hospitalized in "St. Parascheva" Clinical Hospital of Infectious Diseases (Iasi, Romania). (2) Methods: This is a retrospective study that used the medical database recorded between July and November 2021 in order to highlight the characteristics of SARS-CoV-2 infection in patients from the northeastern region of Romania. (3) Results: We enrolled in the study a total of 1732 SARS-CoV-2 infected patients, mean age 67 ± 3.4 years, the female gender predominating (987 cases; 56.98%) as well as patients from the urban environment (982 patients; 56.69%). Moderate form of the disease predominated (814 cases; 47%), pulmonary imaging changes were found in 1042 (60.16%) cases, and 1242 (71.71%) patients had at least one underlying disease. After a median length of hospitalization of 9.5 days, 1359 (78.46%) patients were discharged cured, 48 (2.77%) were transferred to other services by decompensating the associated pathologies, 302 (17.43%) patients needed extensive support in the intensive care unit and there were 325 (18.76%) deaths. (4) Conclusions: The epidemiological characteristics of SARS-CoV-2 infection recorded in our study were mostly the same as characteristics of COVID-19 from all over the world.

Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review

Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.

CT Imaging Features and Clinical Characteristics of 2019 Novel Coronavirus Pneumonia (COVID-19) During Rehabilitation

This study aims to explore the clinical characteristics of the patients with novel coronavirus pneumonia (COVID-19) during rehabilitation. One hundred and twelve confirmed patients were enrolled, while 72 were females (64.3%) and 40 were males (35.7%). The age of the patients was 51.63 ± 4.07 years old. Those patients were divided into mild group, moderate group and severe group based on lesion volume and proportion of total lesion on CT images. The age, gender, past medical history, finger pulse oxygen (SPO2), heart rate (HR) and body temperature and other clinical characteristics of patients were collected. Lesion volume was measured by CT. Compared with mild group, age, lesion volume and total lesion proportion in moderate group were significantly higher. Age, lesion volume and total lesion proportion in severe group were also higher than those in moderate group. Age and past medical history were the risk factors for the lesion volume of COVID-19. Older the patient has larger CT lesion range (R = 0.232, P = 0.045). Without past medical history or combination of post-medical history, the COVID-19 patients had smaller CT lesion ranges, and the history of previous cardiovascular disease and pulmonary disease was important risk factors for the larger CT lesion ranges. The patients who were older or combined with chronic diseases, especially cardiovascular diseases, respiratory disease and diabetes, tended to have the larger lesions. Age and past medical history of patients with COVID-19 period are significantly related to the lesion volume and total lesion proportion on CT images.

Epidemiological and Clinical Characteristics of COVID-19 Patients in Northern Ethiopia: A Retrospective Cohort Study

Purpose

COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is an emerging global public health problem. The disease is believed to affect older people and is accompanied by clinical features such as fever, shortness of breath, and coughing. Currently, there is a lack of information regarding the characteristics of COVID-19 patients in Ethiopia. Thus, this paper aims to evaluate the epidemiological and clinical features of COVID-19 patients in Tigray, Northern Ethiopia.

Patients and Methods

A total of 6,637 symptomatic and asymptomatic COVID-19 patients collected from six isolation and treatment centers in Tigray between May 7 and October 28, 2020 were retrospectively analyzed. Chi-square test or Fisher’s exact test was used to compare the epidemiological and clinical characteristics of COVID-19 patients as appropriate. A p-value <0.05 was considered statistically significant.

Results

The mean age of the patients was 31.3±12.8. SARS-CoV-2 infects men more than women with a ratio of 1.85:1. About 16% of the patients were symptomatic, of which 13.3% (95% CI=11.3–15.4%) were admitted to intensive care units and 6.1% (95% CI=4.5–7.6%) were non-survivors. The mortality rate was increased up to 40.3% (95% CI=32.1–48.4%) among patients with severe illness. A higher proportion of deaths were observed in men (73.2%) and 55.4% were in the age group of ≥50 years. About 4.3% (282 of 6,637) had one or more coexisting comorbidities; the most common being cardiovascular diseases (30.1%) and diabetes mellitus (23.8%). The comorbidity rate in the non-survivor group was significantly higher than in the survivor group (p-value <0.001).

Conclusion

The proportion of symptomatic patients was low. Non-survival was linked with old age and the existence of comorbidities. The findings of this study can help in the design of appropriate management strategies for COVID-19 patients, such as giving due emphasis to COVID-19 patients who are old and with comorbidities.

Prevalence of comorbidities and symptoms stratified by severity of illness amongst adult patients with COVID-19: a systematic review

Introduction

We performed a systematic review of comorbidities and symptoms of adult patients with coronavirus disease 2019 (COVID-19) to evaluate comorbidities, symptoms, and severity.

Material and methods

We searched databases and extracted comorbidities and symptoms from the included studies. We stratified the similar signs and symptoms in groups and on the basis of severity and compared them with stratified analysis. Individual case reports and case series with < 5 patients were excluded.

Results

A total of 163 studies with 43,187 patients were included. Mean age was 54.6 years. There were significantly fewer women in the study (43.9% vs. 56.1%, p < 0.0001). Prevalent cardiovascular comorbidities were hypertension (31.9%), obesity (27.9%), hyperlipidemia (26.4%), smoking (18.9%), diabetes mellitus (17.2%), atherosclerotic disease (9.2%) and arrhythmia (5.0%). The most frequently reported constitutional symptoms of COVID-19 were fever (73.9%), fatigue (33.4%), malaise (29.9%), myalgia and/or arthralgia (19.2%), generalized weakness (19.0%), and chills (11.3%). For the cardiovascular system, chest pain and/or tightness were most often reported (19.6%), followed by palpitations (5.2%). Hypertension and diabetes were common in severe disease. Obesity and congestive heart failure were not observed in any non-severe cases. Severe cases compared to non-severe cases more frequently had fever (87.8% vs. 58.5%, p < 0.001), shortness of breath (47.4% vs. 20.6%, p < 0.001), cough (66.8% vs. 62.9%, p < 0.001), sputum production (35.4% vs. 26.5%, p < 0.001) and rhinorrhea (32.2% vs. 7.3%, p < 0.001).

Conclusions

Hypertension, diabetes, and atherosclerotic diseases are common comorbidities across the world, with obesity as the second most common in the US and more common in men.

The Persistence Time of SARS-CoV-2 RNA in hospitalized COVID-19 Patients: A prospective Study

BACKGROUND

In the late December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), the causative agent of coronavirus disease 2019 (COVID-19), spreads to almost all countries worldwide. The outbreak of this virus has also been confirmed since 19 February 2020 in Iran.

OBJECTIVE

The aim of this study was to investigate the time of viral RNA clearance in swab and serum samples of COVID-19 patients who received different medications. We also evaluated different factors that may be affected viral RNA persistence in patients infected by SARS-CoV-2.

METHODS

In March 2020, twenty-one hospitalized COVID-19 patients were participated in this prospective study. All patients received antiviral agents in their routine care. Throat swabs and blood samples were obtained from all patients in different intervals including day 3 or 5, day 7, day 10 and finally 14 days after the first positive real time RT-PCR (rRT-PCT) test Results: The median time from the SO to first negative rRT-PCR results for throat swabs and serum samples of COVID-19 patients were 18 and 14 days, respectively. These times were more significant in patients with lymphopenia, oxygen saturation ≤ 90% and comorbidity.

CONCLUSION

This preliminary study highlights that SASR-CoV-2 RNA was not detectable in the upper respiratory tract longer than three weeks. In addition, SARS-CoV may persist for a long period of time in the respiratory than the serum samples. This study support the idea that in the settings of limited resources the patients should cease to be tested earlier than three weeks for discharge management.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Risk factors of developing critical conditions in Iranian patients with COVID-19

COVID-19 due to novel Coronavirus was first reported in Wuhan, China. Nowadays, the Islamic Republic of Iran stands among countries with high COVID-19 prevalence and high burden of disease. Since the medical resources are limited, we aimed to identify the risk factors for patients developing critical conditions. This can help to improve resource management and treatment outcomes. In this retrospective study, we included 12,677 patients who were from 26 hospitals, supervised by Tehran University of Medical Sciences with signs and symptoms of COVID-19, until April 12. University integrated IT system was adopted to collect the data. We performed Logistic regression to evaluate the association between death in COVID-19 positive patients and other variables. Cough, respiratory distress and fever were the most common symptoms in our patients, respectively. Cancer, chronic lung diseases and chronic neurologic diseases were the strongest risk factors for death in COVID-19 patients.

A lab-on-a-chip platform for integrated extraction and detection of SARS-CoV-2 RNA in resource-limited settings

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the unprecedented global pandemic of coronavirus disease-2019 (COVID-19). Efforts are needed to develop rapid and accurate diagnostic tools for extensive testing, allowing for effective containment of the infection via timely identification and isolation of SARS-CoV-2 carriers. Current gold standard nucleic acid tests require many separate steps that need trained personnel to operate specialist instrumentation in laboratory environments, hampering turnaround time and test accessibility, especially in low-resource settings. We devised an integrated on-chip platform coupling RNA extraction based on immiscible filtration assisted by surface tension (IFAST), with RNA amplification and detection via colorimetric reverse-transcription loop mediated isothermal amplification (RT-LAMP), using two sets of primers targeting open reading frame 1a (ORF1a) and nucleoprotein (N) genes of SARS-CoV-2. Results were identified visually, with a colour change from pink to yellow indicating positive amplification, and further confirmed by DNA gel electrophoresis. The specificity of the assay was tested against HCoV-OC43 and H1N1 RNAs. The assay based on use of gene N primers was 100% specific to SARS-CoV-2 with no cross-reactivity to HCoV-OC43 nor H1N1. Proof-of-concept studies on water and artificial sputum containing genomic SARS-CoV-2 RNA showed our IFAST RT-LAMP device to be capable of extracting and detecting 470 SARS-CoV-2 copies mL^-1 within 1 h (from sample-in to answer-out). IFAST RT-LAMP is a simple-to-use, integrated, rapid and accurate COVID-19 diagnostic platform, which could provide an attractive means for extensive screening of SARS-CoV-2 infections at point-of-care, especially in resource-constrained settings.

Which criteria were used to describe patients with COVID-19? A systematic review and meta analysis of clinical, laboratory, and imaging features

Background: The unknowingness of COVID-19 compared to other respiratory diseases and gaining an overview of its diagnostic criteria led to this study, which was designed to summarize the signs and symptoms along with the clinical tests that described these patients. Methods: PubMed\MEDLINE, Web of Science, Core Collection, Scopus, and Google Scholar were systematically searched on September 27, 2020. After screening, we selected 56 articles based on clinical characteristics and laboratory and imaging findings in confirmed COVID-19 patients as eligibility criteria. To evaluate risk of bias, the Newcastle Ottawa scale, for publication bias, Egger's test, and for heterogeneity, I2 and tau test were used; and finally, random-effects models were used for pooled estimation. Results: Pooled estimates for frequently clinical symptoms were as follows: fever (78% [95% CI, 74-82]), cough (60% [95% CI, 57-63]), and fatigue (31% [95% CI, 26-36]); and they were as follows for laboratory findings in lymphocyte (1.02 [95% CI, 0.92-1.12]), CRP (19.64 [95% CI, 13.96- 25.32]), and platelet count (175.2 [95% CI, 165.2-185.2]); they were as follows for imaging findings in bilateral pneumonia (64% [95% CI, 56-72]), and ground glass opacity (60% [95% CI, 48-7]). Also, in the subgroup analysis, bilateral pneumonia with 18% and fatigue with 15%, had the highest difference in values between the groups. Conclusion: According to Forest plots, the CI and dispersion among studies were smaller in laboratory findings than in symptom and imaging findings, which might indicate a high alignment in the laboratory findings among studies.

A Multiplex and Colorimetric Reverse Transcription Loop-Mediated Isothermal Amplification Assay for Sensitive and Rapid Detection of Novel SARS-CoV-2

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a major threat to public health. Rapid molecular testing for convenient and timely diagnosis of SARS-CoV-2 infections represents a challenge that could help to control the current pandemic and prevent future outbreaks. We aimed to develop and validate a multiplex and colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay using lyophilized LAMP reagents for sensitive and rapid detection of SARS-CoV-2. LAMP primers were designed for a set of gene targets identified by a genome-wide comparison of viruses. Primer sets that showed optimal features were combined into a multiplex RT-LAMP assay. Analytical validation included assessment of the limit of detection (LoD), intra- and inter-assay precision, and cross-reaction with other respiratory pathogens. Clinical performance compared to that of real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) was assessed using 278 clinical RNA samples isolated from swabs collected from individuals tested for COVID-19. The RT-LAMP assay targeting the RNA-dependent RNA polymerase (RdRp), membrane (M), and ORF1ab genes achieved a comparable LoD (0.65 PFU/mL, CT=34.12) to RT-qPCR and was 10-fold more sensitive than RT-qPCR at detecting viral RNA in clinical samples. Cross-reactivity to other respiratory pathogens was not observed. The multiplex RT-LAMP assay demonstrated a strong robustness and acceptable intra- and inter-assay precision (mean coefficient of variation, 4.75% and 8.30%). Diagnostic sensitivity and specificity values were 100.0% (95% CI: 97.4-100.0%) and 98.6% (95% CI: 94.9-99.8%), respectively, showing high consistency (Cohen's kappa, 0.986; 95% CI: 0.966-1.000; p<0.0001) compared to RT-qPCR. The novel one-step multiplex RT-LAMP assay is storable at room temperature and showed similar diagnostic accuracy to conventional RT-qPCR, while being faster (<45 min), simpler, and cheaper. The new assay could allow reliable and early diagnosis of SARS-CoV-2 infections in primary health care. It may aid large-scale testing in resource-limited settings, especially if it is integrated into a point-of-care diagnostic device.

Epidemiological and clinical characteristics of non-severe and severe pediatric and adult COVID-19 patients across different geographical regions in the early phase of pandemic: a systematic review and meta-analysis of observational studies

This systematic and meta-review aimed to compare clinical presentation, outcomes, and care management among patients with COVID-19 during the early phase of the pandemic. A total of 77 peer-reviewed publications were identified between January 1, 2020 and April 9, 2020 from PubMed, Google Scholar, and Chinese Medical Journal databases. Subsequently, meta-analysis of 40 non-overlapping studies, comprising of 4844 patients from seven countries, was conducted to see differences in clinical characteristics and laboratory outcomes across patients from different geographical regions (Wuhan, other parts of China and outside China), severity (non-severe, severe and fatal) and age groups (adults and children). Patients from Wuhan had a higher mean age (54.3 years) and rates of dyspnea (39.5%) compared with patients from other parts of China and outside China. Myalgia, fatigue, acute respiratory distress syndrome (ARDS) and fatalities were also significantly more prevalent among Wuhan patients. A significant dose–response increase in prevalence of diabetes, D-dimer, white blood cells, neutrophil levels and ARDS was seen from non-severe to severe and fatal outcomes. A significant increase in mean duration of symptom onset to admission was seen between non-severe cases (4.2 days) and severe and fatal cases (6.3 days and 8.8 days, respectively). Proportion of asymptomatic cases was higher in children (20%) compared with adults (2.4%). In conclusion, patients with COVID-19 from Wuhan displayed more severe clinical disease during the early phase of the pandemic, while disease severity was significantly lesser among pediatric cases. This review suggests that biomarkers at admission may be useful for prognosis among patients with COVID-19.

Digestive system manifestations and clinical significance of coronavirus disease 2019: A systematic literature review

BACKGROUND AND AIM

A worldwide outbreak of coronavirus disease 2019 (COVID-19) has drawn global attention. Several reports have described the gastrointestinal (GI) manifestations in the infected patients. The systematic review was designed to highlight the gaps in our knowledge about the prevalence and clinical significance of GI symptoms in patients with COVID-19.

METHODS

We searched PubMed database and Google articles published in both English and Chinese up to June 3, 2020, using search terms "clinical features," "2019 novel coronavirus," "2019-nCoV," "COVID-19," or "SARS-Cov-2." Observational studies, case reports, or letters describing the clinical features or observational studies regarding the detection and/or isolation of severe acute respiratory syndrome coronavirus 2 viruses in stools were included.

RESULTS

A total of 22 publications were finally selected. It was reported that GI symptoms occurred in about 3-40.7% of patients. GI manifestations included nausea, diarrhea, anorexia, vomiting, abdominal pain, belching, abdominal distension, and GI hemorrhage. Diarrhea was the most common GI symptom. Infected patients had various degrees of liver dysfunction, and the severity of liver dysfunction was significantly associated with the severity of the disease. Therapy focusing on digestive system like liver supportive therapy or nutrition support or probiotics has been demonstrated to be effective interventions, which greatly improve prognosis. Fecal-oral transmission route is a potential risk for transmission.

CONCLUSIONS

GI symptoms are common in COVID-19. Strengthening the recognition on abnormalities in digestive system of patients with COVID-19 is crucial for early identification and timely treatment, especially for those atypical patients. Hygiene protection and keeping the drainpipe free flowing are necessary for everyone.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

How Prevalent Is Cancer in Confirmed Cases with Coronaviruses and Severe Acute Respiratory Syndromes?

Novel coronavirus disease 2019 (COVID-19) has posed a crucial hazard to global health. The new species share similarities with the two previously emerged entities: severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS) that have caused outbreaks in 2002 and 2012, respectively. Interestingly, all of these coronaviruses can cause potentially fatal respiratory syndromes, though behave differently in patients with cancer compared to patients without cancer. Accordingly, the present chapter aims to, through a systematic investigation, estimate the prevalence of cancer among COVID-19, SARS, and MERS confirmed cases. Our analysis based on data from 78 studies with SARS, MERS, and COVID-19 confirmed cases showed that the prevalence of cancer (4.94%) stands at fourth place after hypertension (20.8%), diabetes (11.39%), and cardiovascular diseases (7.46%). According to the findings of the present study, comorbidities are significantly more common in patients with MERS compared to patients with COVID-19 and SARS, and this was the cancer case as well. Further studies need to address whether or not patients with coronaviruses and cancer are different from patients with coronaviruses without cancer in terms of clinical manifestations, laboratory findings, outcomes, and men to women ratio.

Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities

The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius; °C), dew point (degrees Celsius; °C), relative humidity (percent; %), air pressure (kilopascal; kPa), windspeed (meters per second; m/s) and visibility (kilometer; km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform.

Diagnostic accuracy of CT and radiographic findings for novel coronavirus 2019 pneumonia: Systematic review and meta-analysis

PURPOSE

To evaluate pooled prevalence, sensitivity, and specificity of chest computed tomography (CT) and radiographic findings for novel coronavirus-2019 (COVID-19) pneumonia.

METHODS

We performed systematic literature search in PubMed and Embase to identify articles reporting baseline imaging findings of COVID-19 pneumonia. The quality of the articles was assessed using NIH quality assessment tool for case series studies. The pooled prevalence, sensitivity, specificity, and diagnostic odds ratio of imaging findings were calculated.

RESULTS

Fifty-six studies (6007 patients, age, 2.1-70 years, 2887 females, 5762 CT, 396 radiographs,) were included. The mean interval between onset of symptoms and CT acquisition was 1-8 days. On CT, the pooled prevalence of ground glass opacities (GGO), GGO plus consolidation, and consolidation only was 66.9% (95% CI 60.8-72.4%), 44.9% (38.7-51.3%), and 32.1 (23.6-41.9%) respectively. Pooled sensitivity and specificity of GGO on CT was 73% (71%-80%) and 61% (41%-78%), respectively. For GGO plus consolidation and consolidation only, the pooled sensitivities/ specificities were 58% (48%-68%)/ 58% (41%-73%) and 49% (20%-78%)/ 56% (30%-78%), respectively. The pooled prevalence of GGO and consolidation on chest radiograph was 38.7% (22.2%-58.3%) and 46.9% (29.7%-64.9%), respectively. The diagnostic accuracy of radiographic findings could not be assessed due to small number of studies.

CONCLUSION

GGO on CT has the highest diagnostic performance for COVID-19 pneumonia, followed by GGO plus consolidation and consolidation only. However, the moderate to low sensitivity and specificity suggest that CT should not be used as the primary tool for diagnosis. Chest radiographic abnormalities are seen in half of the patients.

Mortality in patients admitted to intensive care with COVID-19: an updated systematic review and meta-analysis of observational studies

The COVID-19 pandemic continues to cause critical illness and deaths internationally. Up to 31 May 2020, mortality in patients admitted to intensive care units (ICU) with COVID-19 was 41.6%. Since then, changes in therapeutics and management may have improved outcomes. Also, data from countries affected later in the pandemic are now available. We searched MEDLINE, Embase, PubMed and Cochrane databases up to 30 September 2020 for studies reporting ICU mortality among adult patients with COVID-19 and present an updated systematic review and meta-analysis. The primary outcome measure was death in intensive care as a proportion of completed ICU admissions, either through discharge from intensive care or death. We identified 52 observational studies including 43,128 patients, and first reports from the Middle East, South Asia and Australasia, as well as four national or regional registries. Reported mortality was lower in registries compared with other reports. In two regions, mortality differed significantly from all others, being higher in the Middle East and lower in a single registry study from Australasia. Although ICU mortality (95%CI) was lower than reported in June (35.5% (31.3-39.9%) vs. 41.6% (34.0-49.7%)), the absence of patient-level data prevents a definitive evaluation. A lack of standardisation of reporting prevents comparison of cohorts in terms of underlying risk, severity of illness or outcomes. We found that the decrease in ICU mortality from COVID-19 has reduced or plateaued since May 2020 and note the possibility of some geographical variation. More standardisation in reporting would improve the ability to compare outcomes from different reports.

Gastrointestinal and renal complications in SARS-CoV-2-infected patients: Role of immune system

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease has been accompanied by various gastrointestinal (GI) and renal manifestations in significant portion of infected patients. Beside studies on the respiratory complications of coronavirus infection, understanding the essential immunological processes underlying the different clinical manifestations of virus infection is crucial for the identification and development of effective therapies. In addition to the respiratory tract, the digestive and urinary systems are the major sources of virus transmission. Thus, knowledge about the invasion mechanisms of SARS-CoV-2 in these systems and the immune system responses is important for implementing the infection prevention strategies. This article presents an overview of the gut and renal complications in SARS-CoV-2 infection. We focus on how SARS-CoV-2 interacts with the immune system and the consequent contribution of immune system, gut, and renal dysfunctions in the development of disease.

Neurological manifestations and complications of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis

BACKGROUND

The spectrum of neurological involvement in COVID-19 is not thoroughly understood. To the best of our knowledge, no systematic review with meta-analysis and a sub-group comparison between severe and non-severe cases has been published. The aim of this study is to assess the frequency of neurological manifestations and complications, identify the neurodiagnostic findings, and compare these aspects between severe and non-severe COVID-19 cases.

METHODS

A systematic search of PubMed, Scopus, EBSCO, Web of Science, and Google Scholar databases was conducted for studies published between the 1st of January 2020 and 22nd of April 2020. In addition, we scanned the bibliography of included studies to identify other potentially eligible studies. The criteria for eligibility included studies published in English language (or translated to English), those involving patients with COVID-19 of all age groups, and reporting neurological findings. Data were extracted from eligible studies. Meta-analyses were conducted using comprehensive meta-analysis software. Random-effects model was used to calculate the pooled percentages and means with their 95% confidence intervals (CIs). Sensitivity analysis was performed to assess the effect of individual studies on the summary estimate. A subgroup analysis was conducted according to severity. The main outcomes of the study were to identify the frequency and nature of neurological manifestations and complications, and the neuro-diagnostic findings in COVID-19 patients.

RESULTS

44 articles were included with a pooled sample size of 13,480 patients. The mean age was 50.3 years and 53% were males. The most common neurological manifestations were: Myalgia (22.2, 95% CI, 17.2 to 28.1%), taste impairment (19.6, 95% CI, 3.8 to 60.1%), smell impairment (18.3, 95% CI, 15.4 to 76.2%), headache (12.1, 95% CI, 9.1 to 15.8%), dizziness (11.3, 95% CI, 8.5 to 15.0%), and encephalopathy (9.4, 95% CI, 2.8 to 26.6%). Nearly 2.5% (95% CI, 1 to 6.1%) of patients had acute cerebrovascular diseases (CVD). Myalgia, elevated CK and LDH, and acute CVD were significantly more common in severe cases. Moreover, 20 case reports were assessed qualitatively, and their data presented separately.

CONCLUSIONS

Neurological involvement is common in COVID-19 patients. Early recognition and vigilance of such involvement might impact their overall outcomes.

Association between clinical, laboratory findings and chest CT in COVID-19 in a secondary hospital in Jakarta, Indonesia

Introduction

A new emerging infectious disease caused by SARS-CoV-2 has caused a global pandemic. Early diagnosis is essential to prevent and halt the spread of the disease, patient management and isolation. In this study, we aimed to reveal correlations between clinical and laboratory findings with chest CT.

Methods

This in an observational case series single center study in a secondary hospital in Jakarta, Indonesia. Patients were included if they had typical symptoms and positive RT-PCR for SARS-CoV-2.

Results

Forty-two patients with positive RT-PCR were included in this study. Typical CT findings were present in 33 (78.6%). We found a positive correlation between patients in whom the imaging was performed after the 4^th day of symptoms and chest CT findings (r=0.365 p<0.05). In receiver operating characteristic analysis of this parameter, the area under curve (AUC) was 0.678, and the sensitivity and specificity were 0.96 and 0.44, respectively.

Conclusions

Early diagnosis of COVID-19 is essential to promptly isolate and treat suspected patients. Utilization of chest CT to help diagnosis in this pandemic era needs to be considered by healthcare facilities especially if RT-PCR is limited.

Occupational burnout among active physiotherapists working in clinical hospitals during the COVID-19 pandemic in south-eastern Poland

BACKGROUND

Occupational burnout, which is more and more commonly encountered among medical professionals and investigated by researchers worldwide, may in particular affect health care workers during the COVID-19 pandemic.

OBJECTIVES

The aim of the study was to assess the risk of occupational burnout among physiotherapists working actively in clinical hospitals in south-eastern Poland during the COVID-19 pandemic.

METHODS

The level of burnout among the studied physiotherapists was assessed using the Polish version of the Maslach Burnout Inventory by Maslach (MBI). The study was conducted from 20 March to 3 May, 2020 among physiotherapists working professionally during the COVID-19 pandemic in the south east of Poland, during which time health services related to therapeutic rehabilitation were suspended. The study was conducted among 1,540 physiotherapists with a license to practice who worked in clinical departments. Considering the inclusion and exclusion criteria, 106 physiotherapists were qualified for the study.

RESULTS

The current findings show that during the COVID-19 pandemic physiotherapists present high burnout rates in all three dimensions: EE (Mean 32.31; CI 29.47-35.15); DP (Mean 16.25; CI 14.48-18.03); PA (Mean 26.25; CI 24.41-28.10). As for gender-related effects, higher burnout rates were observed in the male workers, compared to the females, in all three domains: EE (Men: Mean 34.70; CI 29.90-39.50 -Women: Mean 31.03; CI 27.45-34.60); DP (Men: Mean 18.78; CI 15.98-21.59 -Women: Mean 14.90; CI 12.64-17.16) and PA (Men: Mean 24.54; CI 21.32-27.76 -Women: Mean 27.17; CI 24.90-29.44). The highest burnout rates, presented by the physiotherapists working in the profession for more than 20 years, were identified in the domain of EE (Mean: 35.30; CI 30.51-40.10) and in those with 10-15 years of experience, in the domains of DP (Mean: 18.31; CI 14.89-21.73) and PA (Mean: 23.97; CI 20.13-27.81). The highest rate of occupational burnout, reflected by the scores in all three domains (EE, DP, PA), was identified in Department I -Intensive Care and Anaesthesiology Department: EE - (Mean: 40.89, CI 35.27-46.52); DP - (Mean: 21.39, CI 17.90-24.88); and PA - (Mean: 23.07, CI 20.04-26.10), compared to the other departments. The subjects who rarely participated in courses or training programs showed the highest burnout rates (EE- Mean: 33.55, CI 29.33-37.77; DP- Mean: 16.71, CI 13.99-19.43; PA- Mean: 25.45, CI 22.47-28.43).

CONCLUSIONS

Occupational burnout during the COVID-19 pandemic is noticeable among physiotherapists working in clinical departments. The current findings show high burnout rates in all three domains: emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). A comparative analysis of these findings with reference to related studies published before the pandemic shows that the burnout rates among physiotherapists may have significantly increased during the COVID-19 pandemic. However, given the scarcity of scientific evidence related to this specific problem in Poland and worldwide, it is necessary to continue research in occupational burnout affecting physiotherapists, particularly during the second wave of the pandemic, in order to gain a better understanding of the possible effects of social isolation and greater personal work-related health risks on the mental health of these medical professionals.

Gastroenterological and hepatic manifestations of patients with COVID-19, prevalence, mortality by country, and intensive care admission rate: systematic review and meta-analysis

BACKGROUND AND AIMS

Patients infected with the SARS-CoV-2 usually report fever and respiratory symptoms. However, multiple gastrointestinal (GI) manifestations such as diarrhoea and abdominal pain have been described. The aim of this study was to evaluate the prevalence of GI symptoms, elevated liver enzymes and mortality of patients with COVID-19.

METHODS

A systematic review and meta-analysis of published studies that included a cohort of patients infected with SARS-CoV-2 were performed from 1 December 2019 to 15 December 2020. Data were collected by conducting a literature search using PubMed, Embase, Scopus, and Cochrane according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We analysed pooled data on the prevalence of individual GI symptoms and elevated liver enzymes and performed subanalyses to investigate the relationship between GI symptoms/elevated liver enzymes, geographical location, mortality, and intensive care unit (ICU) admission.

RESULTS

The available data of 78 798 patients positive for SARS-CoV-2 from 158 studies were included in our analysis. The most frequent manifestations were diarrhoea (16.5%, 95% CI 14.2% to 18.4%), nausea (9.7%, 95% CI 9.0% to 13.2%) and elevated liver enzymes (5.6%, 95% CI 4.2% to 9.1%). The overall mortality and GI mortality were 23.5% (95% CI 21.2% to 26.1%) and 3.5% (95% CI 3.1% to 6.2%), respectively. Subgroup analysis showed non-statistically significant associations between GI symptoms/elevated liver enzymes and ICU admissions (OR=1.01, 95% CI 0.55 to 1.83). The GI mortality was 0.9% (95% CI 0.5% to 2.2%) in China and 10.8% (95% CI 7.8% to 11.3%) in the USA.

CONCLUSION

GI symptoms/elevated liver enzymes are common in patients with COVID-19. Our subanalyses showed that the presence of GI symptoms/elevated liver enzymes does not appear to affect mortality or ICU admission rate. Furthermore, the proportion of GI mortality among patients infected with SARS-CoV-2 varied based on geographical location.

COVID-19, nausea, and vomiting

Exclusion of nausea (N) and vomiting (V) from detailed consideration as symptoms of COVID-19 is surprising as N can be an early presenting symptom. We examined the incidence of NV during infection before defining potential mechanisms. We estimate that the overall incidence of nausea (median 10.5%), although variable, is comparable with diarrhea. Poor definition of N, confusion with appetite loss, and reporting of N and/or V as a single entity may contribute to reporting variability and likely underestimation. We propose that emetic mechanisms are activated by mediators released from the intestinal epithelium by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) modulate vagal afferents projecting to the brainstem and after entry into the blood, activate the area postrema (AP) also implicated in anorexia. The receptor for spike protein of SARS-CoV-2, angiotensin 2 converting enzyme (ACE2), and transmembrane protease serine (for viral entry) is expressed in upper gastrointestinal (GI) enterocytes, ACE2 is expressed on enteroendocrine cells (EECs), and SARS-CoV-2 infects enterocytes but not EECs (studies needed with native EECs). The resultant virus-induced release of epithelial mediators due to exocytosis, inflammation, and apoptosis provides the peripheral and central emetic drives. Additionally, data from SARS-CoV-2 show an increase in plasma angiotensin II (consequent on SARS-CoV-2/ACE2 interaction), a centrally (AP) acting emetic, providing a further potential mechanism in COVID-19. Viral invasion of the dorsal brainstem is also a possibility but more likely in delayed onset symptoms. Overall, greater attention must be given to nausea as an early symptom of COVID-19 and for the insights provided into the GI effects of SARS-CoV-2.

Age-Structured Modeling of COVID-19 Epidemic in the USA, UAE and Algeria

As the COVID-19 is still spreading in more than 180 countries, according to WHO. There is a need to understand the dynamics of this infection and predict its the impact on the public health capacity. This work aims to forecast the progress of the disease in three countries from different continents: The United States of America, the United Arab Emirates and Algeria. The existing data shows that the fatality of the disease is high in elderly people and people with comorbidity. Therefore, we consider an age-structured model. Our model also takes into consider two main components of the COVID-19 (a) the number of Infected hospitalized people, therefore, we estimate the number of beds (acute and critical) needed (2) the possible infection of the healthcare personals (HCP). Hence, the model predict the peak time and the number of infectious cases at the peak before and after the implementation of non-pharmaceutical interventions (NPI), and we also compare this finding with case of full lockdown. Finally, we investigate the impact of the shortage of proper personal protective equipment (PPE) on the spread of the disease.

Is liver involvement overestimated in COVID-19 patients? A meta-analysis

Background: Considering transaminase more than the upper limit of normal value as liver injury might overestimate the prevalence of liver involvement in COVID-19 patients. No meta-analysis has explored the impact of varied definitions of liver injury on the reported prevalence of liver injury. Moreover, few studies reported the extent of hypertransaminasemia stratified by COVID-19 disease severity. Methods: A literature search was conducted using PubMed and Embase. The pooled prevalence of liver injury and hypertransaminasemia was estimated. Results: In total, 60 studies were included. The overall prevalence of liver injury was 25%. Compared to subgroups with the non-strict definition of liver injury (33%) and subgroups without giving detailed definition (26%), the subgroup with a strict definition had a much lower prevalence of liver injury (9%). The overall prevalence of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) elevation was 19% and 22%. The prevalence of elevated ALT and AST were significantly higher in severe COVID-19 cases compare to non-severe cases (31% vs 16% and 44% vs 11%). In critically ill and fatal cases, no difference was found in the prevalence of elevated ALT (24% vs 30%) or AST (54% vs 49%). Sensitivity analyses indicated that the adjusted prevalence of ALT elevation, AST elevation, and liver injury decreased to 14%, 7%, and 12%. Conclusion: The overall prevalence of liver injury and hypertransaminasemia in COVID-19 patients might be overestimated. Only a small fraction of COVID-19 patients have clinically significant liver injury. The prevalence of hypertransaminasemia was significantly higher in severe COVID-19 cases compare to non-severe cases. Hence, in severe COVID-19 patients, more attention should be paid to liver function tests.

Clinical Characteristics and Diagnostic Challenges of COVID-19: An Update From the Global Perspective

Clinical characteristics are essential for the correct diagnosis of diseases. The current review aimed to summarize the global clinical characteristics of the COVID-19 patients systematically and identify their diagnostic challenges to help the medical practitioners properly diagnose and for better management of COVID-19 patients. We conducted a systematic search in PubMed, Web of Science, Scopus, Science Direct, and Google Scholar databases for original articles containing clinical information of COVID-19 published up to 7th May 2020. Two researchers independently searched the databases to extract eligible articles. A total of 34 studies from 8 different countries with 10889 case-patients were included for clinical characteristics. The most common clinical symptoms were cough 59.6, fever 46.9, fatigue 27.8, and dyspnea 20.23%. The prominent laboratory findings were lymphocytopenia 55.9, elevated levels of CRP 61.9, aspartate aminotransferase 53.3, LDH 40.8, ESR 72.99, serum ferritin 63, IL-6 52, and prothrombin time 35.47%, and decreased levels of platelets 17.26, eosinophils 59.0, hemoglobin 29, and albumin 38.4%. CT scan of the chest showed an abnormality in 93.50% cases with bilateral lungs 71.1%, ground-glass opacity 48%, lesion in lungs 78.3%, and enlargement of lymph node 50.7%. Common comorbidities were hypertension, diabetes, obesity, and cardiovascular diseases. The estimated median incubation period was 5.36 days, and the overall case fatality rate was 16.9% (Global case fatality outside China was 22.24%: USA 21.24%, Italy 25.61%, and others 0%; whereas the case fatality inside the Hubei Province of China was found to be 11.71%). Global features on the clinical characteristics of COVID-19 obtained from laboratory tests and CT scan results will provide useful information to the physicians to diagnose the disease and for better management of the patients as well as to address the diagnostic challenges to control the infection.

SARS-CoV-2: Pathogenic Mechanisms and Host Immune Response

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, positive-sense RNA coronavirus responsible for the COVID-19 pandemic. Since December 2019, coronavirus disease 2019 (COVID-19) has affected more than 127 million people, 2.7 million deaths globally (as per WHO dashboard, dated 31 March, 2020), the virus is capable of transmitting from human to human via inhalation of infected respiratory droplets or aerosols or contact with infected fomites. Clinically, patients with COVID-19 present with severe respiratory distress syndrome, which is very similar to the presentation of other respiratory viral infections. A huge variation in the host response exists, with the resulting symptoms varying from mild to moderate. Comorbidities such as cardiovascular disease, hypertension, diabetes, coagulation dysfunction, stroke, malignant tumor and multiple organ dysfunction syndrome, as well as age and sex, are associated with severe COVID-19 cases. So far, no targeted therapies have been developed to treat this disease and existing drugs are being investigated for repurposing. This chapter discusses the epidemiology, clinical features of COVID-19, pathogenesis and the innate and adaptive immune response mounted by the host to the SARS-CoV-2 infection. A deeper understanding of the host-pathogen interaction is fundamental to the development of a vaccine.

SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis

BACKGROUND

Viral load kinetics and duration of viral shedding are important determinants for disease transmission. We aimed to characterise viral load dynamics, duration of viral RNA shedding, and viable virus shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in various body fluids, and to compare SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV) viral dynamics.

METHODS

In this systematic review and meta-analysis, we searched databases, including MEDLINE, Embase, Europe PubMed Central, medRxiv, and bioRxiv, and the grey literature, for research articles published between Jan 1, 2003, and June 6, 2020. We included case series (with five or more participants), cohort studies, and randomised controlled trials that reported SARS-CoV-2, SARS-CoV, or MERS-CoV infection, and reported viral load kinetics, duration of viral shedding, or viable virus. Two authors independently extracted data from published studies, or contacted authors to request data, and assessed study quality and risk of bias using the Joanna Briggs Institute Critical Appraisal Checklist tools. We calculated the mean duration of viral shedding and 95% CIs for every study included and applied the random-effects model to estimate a pooled effect size. We used a weighted meta-regression with an unrestricted maximum likelihood model to assess the effect of potential moderators on the pooled effect size. This study is registered with PROSPERO, CRD42020181914.

FINDINGS

79 studies (5340 individuals) on SARS-CoV-2, eight studies (1858 individuals) on SARS-CoV, and 11 studies (799 individuals) on MERS-CoV were included. Mean duration of SARS-CoV-2 RNA shedding was 17·0 days (95% CI 15·5-18·6; 43 studies, 3229 individuals) in upper respiratory tract, 14·6 days (9·3-20·0; seven studies, 260 individuals) in lower respiratory tract, 17·2 days (14·4-20·1; 13 studies, 586 individuals) in stool, and 16·6 days (3·6-29·7; two studies, 108 individuals) in serum samples. Maximum shedding duration was 83 days in the upper respiratory tract, 59 days in the lower respiratory tract, 126 days in stools, and 60 days in serum. Pooled mean SARS-CoV-2 shedding duration was positively associated with age (slope 0·304 [95% CI 0·115-0·493]; p=0·0016). No study detected live virus beyond day 9 of illness, despite persistently high viral loads, which were inferred from cycle threshold values. SARS-CoV-2 viral load in the upper respiratory tract appeared to peak in the first week of illness, whereas that of SARS-CoV peaked at days 10-14 and that of MERS-CoV peaked at days 7-10.

INTERPRETATION

Although SARS-CoV-2 RNA shedding in respiratory and stool samples can be prolonged, duration of viable virus is relatively short-lived. SARS-CoV-2 titres in the upper respiratory tract peak in the first week of illness. Early case finding and isolation, and public education on the spectrum of illness and period of infectiousness are key to the effective containment of SARS-CoV-2.

FUNDING

None.

Clinical, epidemiological, laboratory, and radiological characteristics of novel Coronavirus (2019-nCoV) in retrospective studies: A systemic review and meta-analysis

BACKGROUND

In December 2019, a novel pneumonia related to the 2019 coronavirus unexpectedly developed in Wuhan, China. We aimed to review data of the novel Coronavirus (2019-nCoV) by analyzing all the published retrospective studies on the clinical, epidemiological, laboratory, and radiological characteristics of patients with 2019-nCoV.

METHODS

We searched in four bibliographic databases PubMed, Scopus, Embase, and Web of Science) for studies March 10, 2020 focused on the clinical, epidemiological, laboratory, and radiological characteristics of patients with 2019-nCoV for meta-analysis. The Newcastle-Ottawa Scale was used to quality assessment, and publication bias was analyzed by Egger's test. In the meta-analysis, a random-effects model with Stata/SE software, v.14.1 (StataCorp, College Station, TX) was used to obtain a pooled incidence rate.

RESULTS

Fifty studies were included in this systematic review and meta-analysis with 8815 patients and the mean age was 46 years and 4647 (52.7%) were male. The pooled incidences rate of clinical symptoms were: fever (83%, 95% CI: 0.77, 0.89), cough (59%, 95% CI: 0.48, 0.69), myalgia or fatigue (31%, 95% CI: 0.23, 0.39), sputum production (29%, 95% CI: 0.21, 0.39), and dyspnea (19%, 95% CI: 0.12, 0.26). The pooled incidence rate of acute respiratory distress syndrome (ARDS) was (22%, 95% CI: 0.00, 0.60).

CONCLUSION

The results of this systemic review and meta-analysis present a quantitative pooled incidence rate of different characters of 2019-nCoV and has great potential to develop diagnosis and patient's stratification in 2019-nCoV. However, this conclusions of this study still requisite to be warranted by more careful design, larger sample size multivariate studies to corroborate the results of this meta-analysis.

Liver injury in COVID-19: The hepatic aspect of the respiratory syndrome - what we know so far

The 2019 novel coronavirus disease (COVID-19) pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to global public health. Although primarily, the infection causes lung injury, liver enzyme abnormalities have also been reported to occur during the course of the disease. We conducted an extensive literature review using the PubMed database on articles covering a broad range of issues related to COVID-19 and hepatic injury. The present review summarizes available information on the spectrum of liver involvement, the possible mechanisms and risk factors of liver injury due to SARS-CoV-2 infection, and the prognostic significance of the presence of liver injury. Hopefully, this review will enable clinicians, especially the hepatologists, to understand and manage the liver derangements they may encounter in these patients better and provide guidance for further studies on the liver injury of COVID-19.

Highly sensitive and specific diagnosis of COVID-19 by reverse transcription multiple cross-displacement amplification-labelled nanoparticles biosensor

Background

The ongoing outbreak of the novel human coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (also known as 2019-nCoV) has become a global health concern. Rapid and easy-to-use diagnostic techniques are urgently needed to diagnose SARS-CoV-2 infection.

Methods

We devised a reverse transcription multiple cross-displacement amplification (RT-MCDA) coupled with a nanoparticle-based biosensor assay (RT-MCDA-BS) for rapid, sensitive and specific diagnosis of coronavirus disease 2019 (COVID-19). Two primer sets were designed to target the open reading frame 1a/b and nucleoprotein gene of SARS-CoV-2. A total of 183 clinical samples, including 65 patients with COVID-19 infection and 118 patients with other pathogen infections were used to testify the assay's feasibility. Diagnosis results were reported visually using the biosensor.

Findings

The assay designed was performed using a simple instrument which could maintain the reaction in a constant temperature at 64°C for only 35 min. The total COVID-19 RT-MCDA-BS test procedure could be finished within 1 h. The COVID-19 RT-MCDA-BS could detect down to five copies of target sequences. Among 65 clinical samples from the COVID-19 patients, 22 (33.8%) positive results were obtained from faeces, nasal, pharyngeal and anal swabs via COVID-19 RT-MCDA-BS assay, while real-time reverse transcription-PCR assay only detected 20 (30.7%) positive results in these samples. No positive results were obtained from clinical samples with non-COVID-19 infections.

Interpretation

COVID-19 RT-MCDA-BS was a rapid, reliable, low-cost and easy-to-use assay, which could provide an attractive laboratory tool to diagnose COVID-19 in multiple clinical specimens, especially for field, clinic laboratories and primary care facilities in resource-poor settings.

We devised a novel method (COVID-19 RT-MCDA-BS) to diagnose COVID-19. Its rapidity, low cost and ease of use make the method an ideal tool for use in field, primary and clinical laboratories, especially for resource-poor settings.https://bit.ly/3j08CU2

A blood transcriptome-based analysis of disease progression, immune regulation, and symptoms in coronavirus-infected patients

COVID-19 patients show heterogeneity in clinical presentation and outcomes that makes pandemic control and strategy difficult; optimizing management requires a systems biology approach of understanding the disease. Here we sought to potentially understand and infer complex disease progression, immune regulation, and symptoms in patients infected with coronaviruses (35 SARS-CoV and 3 SARS-CoV-2 patients and 57 samples) at two different disease progression stages. Further, we compared coronavirus data with healthy individuals (n = 16) and patients with other infections (n = 144; all publicly available data). We applied inferential statistics (the COVID-engine platform) to RNA profiles (from limited number of samples) derived from peripheral blood mononuclear cells (PBMCs). Compared to healthy individuals, a subset of integrated blood-based gene profiles (signatures) distinguished acute-like (mimicking coronavirus-infected patients with prolonged hospitalization) from recovering-like patients. These signatures also hierarchically represented multiple (at the system level) parameters associated with PBMC including dysregulated cytokines, genes, pathways, networks of pathways/concepts, immune status, and cell types. Proof-of-principle observations included PBMC-based increases in cytokine storm-associated IL6, enhanced innate immunity (macrophages and neutrophils), and lower adaptive T and B cell immunity in patients with acute-like disease compared to those with recovery-like disease. Patients in the recovery-like stage showed significantly enhanced TNF, IFN-γ, anti-viral, HLA-DQA1, and HLA-F gene expression and cytolytic activity, and reduced pro-viral gene expression compared to those in the acute-like stage in PBMC. Besides, our analysis revealed overlapping genes associated with potential comorbidities (associated diabetes) and disease-like conditions (associated with thromboembolism, pneumonia, lung disease, and septicemia). Overall, our COVID-engine inferential statistics platform and study involving PBMC-based RNA profiling may help understand complex and variable system-wide responses displayed by coronavirus-infected patients with further validation.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

Host-pathogen interaction in COVID-19: Pathogenesis, potential therapeutics and vaccination strategies

The current coronavirus pandemic, COVID-19, is the third outbreak of disease caused by the coronavirus family, after Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. It is an acute infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This severe disease is characterised by acute respiratory distress syndrome, septic shock, metabolic acidosis, coagulation dysfunction, and multiple organ dysfunction syndromes. Currently, no drugs or vaccines exist against the disease and the only course of treatment is symptom management involving mechanical ventilation, immune suppressants, and repurposed drugs. The severe form of the disease has a relatively high mortality rate. The last six months have seen an explosion of information related to the host receptors, virus transmission, virus structure-function relationships, pathophysiology, co-morbidities, immune response, treatment and the most promising vaccines. This review takes a critically comprehensive look at various aspects of the host-pathogen interaction in COVID-19. We examine the genomic aspects of SARS-CoV-2, modulation of innate and adaptive immunity, complement-triggered microangiopathy, and host transmission modalities. We also examine its pathophysiological impact during pregnancy, in addition to emphasizing various gaps in our knowledge. The lessons learnt from various clinical trials involving repurposed drugs have been summarised. We also highlight the rationale and likely success of the most promising vaccine candidates.

The burden and outcomes of COVID-19 among patients with co-morbid disease in Africa: protocol for a systematic review and meta-analysis

The coronavirus disease 2019 (COVID-19) outbreak is now a global public health concern and has had an enormous adverse impact in both developed and developing countries. In Africa, in August 2020, the total number of confirmed cases was 1 022 401 cases, with 704 704 recovered and 22 501 deaths. People with co-morbidities are at increased risk of complications and COVID-19-related death. Evidence on the burden and outcome among patients with co-morbid diseases has not been published in Africa, so this systematic review and meta-analysis aims to quantify these. Observational studies reporting on the burden and outcome of COVD-19 among patients with co-morbid diseases in Africa will be included and a search of online databases PubMed/MEDLINE, EMBASE, HINARI, Cochrane Library, World Health Organization COVID-19 database, Africa Wide Knowledge and Web of Science will be applied. Two independent authors will carry out data extraction and assess the risk of bias using a predetermined and structured method of data collection. Disagreements will be resolved by discussion after mutual consensus with a third reviewer who is an experienced researcher (AH) in meta-analysis studies. We will use random-effects to estimate the overall burden and outcome of COVID-19 among patients with co-morbid diseases in Africa. To assess possible publication bias, funnel plot test and Egger's test methods will be used. This systematic review and meta-analysis protocol will be reported based on the Preferred Reporting Items for Systematic reviews and Meta-Analysis protocol guidelines. Results will be stratified by the African geographic region, diagnostic methods and co-morbidity. COVID-19 distribution data will be shown by interest variables such as residence/geographic region, diagnostic methods, type of co-morbidity and outcomes of co-morbidity. The findings of this review will notify health-care professionals about the burden and outcome of COVID-19 among patients with co-morbid diseases while providing evidence to bring about the requisite improvements in clinical practice for these patients.

Prevalence and impact of diabetes in patients with COVID-19 in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that has spread rapidly around the world. Previous studies have indicated that COVID-19 patients with diabetes are prone to having poor clinical outcomes.

AIM

To systematically evaluate the prevalence of diabetes among COVID-19 patients in China and its impact on clinical outcomes, including ICU admission, progression to severe cases, or death.

METHODS

We searched studies published in PubMed, Web of Science, and EMBASE from December 1, 2019 to March 31, 2020 to identify relevant observational study that investigated the prevalence of diabetes among COVID-19 patients or its impact on clinical outcomes. We used a random-effects or fixed-effects model to estimate the pooled prevalence of diabetes and risk ratio (RR) and its 95% confidence interval (CI) of diabetes on outcomes. Funnel plots were used to evaluate the publication bias and the heterogeneity was evaluated by I 2 statistic.

RESULTS

Twenty-three eligible articles including 49564 COVID-19 patients (1573 with and 47991 without diabetes) were finally included. The pooled prevalence of diabetes was 10% (95%CI: 7%-15%) in COVID-19 patients. In the subgroup analyses, the pooled prevalence of diabetes was higher in studies with patients aged > 50 years (13%; 95%CI: 11%-16%) than in studies with patients aged ≤ 50 years (7%; 95%CI: 6%-8%), in severe patients (17%; 95%CI: 14%-20%) than in non-severe patients (6%; 95%CI: 5%-8%), and in dead patients (30%; 95%CI: 13%-46%) than in survivors (8%; 95%CI: 2%-15%) (P < 0.05 for all). Compared with patients without diabetes, the risk of severe cases was higher (RR = 2.13, 95%CI: 1.76-2.56, I 2 = 49%) in COVID-19 patients with diabetes. The risk of death was also higher in COVID-19 patients with diabetes (RR = 3.16, 95%CI: 2.64-3.78, I 2 = 34%). However, diabetes was not found to be significantly associated with admission to ICU (RR = 1.16, 95%CI: 0.15-9.11).

CONCLUSION

Nearly one in ten COVID-19 patients have diabetes in China. Diabetes is associated with a higher risk of severe illness and death. The present study suggested that targeted early intervention is needed in COVID-19 patients with diabetes.

Clinical characteristics, laboratory findings, radiographic signs and outcomes of 61,742 patients with confirmed COVID-19 infection: A systematic review and meta-analysis

INTRODUCTION

In the current time where we face a COVID-19 pandemic, there is no vaccine or effective treatment at this time. Therefore, the prevention of COVID-19 and the rapid diagnosis of infected patients is crucial.

METHOD

We searched all relevant literature published up to February 28, 2020. We used Random-effect models to analyze the appropriateness of the pooled results.

RESULT

Eighty studies were included in the meta-analysis, including 61,742 patients with confirmed COVID-19 infection. 62.5% (95% CI 54.5-79, p < 0.001) of patients had a history of recent travel endemic area or contact with them. The most common symptoms among COVID-19 infected patients were fever 87% (95% CI 73-93, p < 0.001), and cough 68% (95% CI 55.5-74, p < 0.001)), respectively. The laboratory analysis showed that thrombocytosis was present in 61% (95% CI 41-78, p < 0.001) CRP was elevated in 79% (95% CI 65-91, p < 0.001), and lymphopenia in 57.5% (95% CI 42-79, p < 0.001). The most common radiographic signs were bilateral involvement in 81% (95% CI 62.5-87, p < 0.001), consolidation in 73.5% (95% CI 50.5-91, p < 0.001), and ground-glass opacity 73.5% (95% CI 40-90, p < 0.001) of patients. Case fatality rate (CFR) in <15 years old was 0.6%, in >50 years old was 39.5%, and in all range group was 6%.

CONCLUSIONS

Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting only confirmed cases.

Pulmonary Lobectomy After COVID-19

Concomitant coronavirus disease 19 (COVID-19) is a major risk factor for complications in any type of surgical procedure, especially in thoracic surgery, were the primary organ involved, the lung, is manipulated to perform parenchymal resection. However, it is not clear whether previous infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to increased morbidity and mortality for subsequent procedures once radiologic resolution is achieved. We report a young patient with lung cancer who successfully underwent a right upper lobectomy for primary adenocarcinoma by video-assisted thoracoscopic surgery with no complication in the early postoperative phase.

The effect of control measures on COVID-19 transmission in Italy: Comparison with Guangdong province in China

BACKGROUND

COVID-19 has spread all around the world. Italy is one of the worst affected countries in Europe. Although there is a trend of relief, the epidemic situation hasn't stabilized yet. This study aims to investigate the dynamics of the disease spread in Italy and provide some suggestions on containing the epidemic.

METHODS

We compared Italy's status at the outbreak stage and control measures with Guangdong Province in China by data observation and analysis. A modified autonomous SEIR model was used to study the COVID-19 epidemic and transmission potential during the early stage of the outbreak in Italy. We also utilized a time-dependent dynamic model to study the future disease dynamics in Italy. The impact of various non-pharmaceutical control measures on epidemic was investigated through uncertainty and sensitivity analyses.

RESULTS

The comparison of specific measures implemented in the two places and the time when the measures were initiated shows that the initial prevention and control actions in Italy were not sufficiently timely and effective. We estimated parameter values based on available cumulative data and calculated the basic reproduction number to be 4.32 before the national lockdown in Italy. Based on the estimated parameter values, we performed numerical simulations to predict the epidemic trend and evaluate the impact of contact limitation, detection and diagnosis, and individual behavior change due to media coverage on the epidemic.

CONCLUSIONS

Italy was in a severe epidemic status and the control measures were not sufficiently timely and effective in the beginning. Non-pharmaceutical interventions, including contact restrictions and improvement of case recognition, play an important role in containing the COVID-19 epidemic. The effect of individual behavior changes due to media update of the outbreak cannot be ignored. For policy-makers, early and strict blockade measures, fast detection and improving media publicity are key to containing the epidemic.

Risk factors for the COVID-19 severity and its correlation with viral shedding: A retrospective cohort study

Coronavirus disease 2019 (COVID‐19) have become a pandemic in the world. This study is aim to explore risk factors for COVID‐19 severity in the early stage and the correlation between the viral shedding and COVID‐19 severity. We included inpatient with laboratory confirmed COVID‐19 who had been discharged by 9 March 2020. The medical record data and dynamic change of biochemical indicators in‐hospital were compared between common and severe patients. Eighty patients were included in this study. Multivariable regression demonstrated increasing odds of severity associated with the duration of fever (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.10‐1.82, per day increase; P = .007), C‐reactive protein (CRP) (OR, 1.26; 95% CI, 1.04‐1.52; P = .02), and PO₂ < 80 mm Hg (28.07, 95% CI, 1.50‐524.12; P = .026) on admission. We found severe acute respiratory syndrome coronavirus 2 viral RNA could be long‐term presence in respiratory tract and fecal sample, up to 43 and 46 days, respectively. However, the duration of viral shedding have no correlation with the COVID‐19 severity. The duration of fever, elevated CRP and PO₂ < 80 mm Hg on admission were associated with the COVID‐19 severity in the early stage and there is no correlation between the viral shedding and COVID‐19 severity.

In this study, we found that the duration of fever, elevated CRP and PO2 < 80 mmHg on admission were associated with the COVID‐19 severity in the early stage and there is no correlation between the viral shedding and COVID‐19 severity.

Diagnostic Performance of CT and Reverse Transcriptase Polymerase Chain Reaction for Coronavirus Disease 2019: A Meta-Analysis

Background Recent studies have suggested that chest CT scans could be used as a primary screening or diagnostic tool for coronavirus disease 2019 (COVID-19) in epidemic areas. Purpose To perform a meta-analysis to evaluate diagnostic performance measures, including predictive values of chest CT and initial reverse transcriptase polymerase chain reaction (RT-PCR). Materials and Methods Medline and Embase were searched from January 1, 2020, to April 3, 2020, for studies on COVID-19 that reported the sensitivity, specificity, or both of CT scans, RT-PCR assays, or both. The pooled sensitivity and specificity were estimated by using random-effects models. The actual prevalence (ie, the proportion of confirmed patients among those tested) in eight countries was obtained from web sources, and the predictive values were calculated. Meta-regression was performed to reveal the effect of potential explanatory factors on the diagnostic performance measures. Results The pooled sensitivity was 94% (95% confidence interval [CI]: 91%, 96%; I2 = 95%) for chest CT and 89% (95% CI: 81%, 94%; I2 = 90%) for RT-PCR. The pooled specificity was 37% (95% CI: 26%, 50%; I2 = 83%) for chest CT. The prevalence of COVID-19 outside China ranged from 1.0% to 22.9%. For chest CT scans, the positive predictive value (PPV) ranged from 1.5% to 30.7%, and the negative predictive value (NPV) ranged from 95.4% to 99.8%. For RT-PCR, the PPV ranged from 47.3% to 96.4%, whereas the NPV ranged from 96.8% to 99.9%. The sensitivity of CT was affected by the distribution of disease severity, the proportion of patients with comorbidities, and the proportion of asymptomatic patients (all P < .05). The sensitivity of RT-PCR was negatively associated with the proportion of elderly patients (P = .01). Conclusion Outside of China where there is a low prevalence of coronavirus disease 2019 (range, 1%-22.9%), chest CT screening of patients with suspected disease had low positive predictive value (range, 1.5%-30.7%). © RSNA, 2020 Online supplemental material is available for this article.

Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis

INTRODUCTION

Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19).

METHODS

EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I2, T2, and Cochrane Q, sensitivity analyses, and assessed publication bias.

RESULTS

148 studies met the inclusion criteria for the systematic review and meta-analysis with 12'149 patients (5'739 female) and a median age of 47.0 [35.0-64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78-1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I2; range: 1.5-98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05).

CONCLUSIONS

Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.