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Fakhar M, Najafi Ghobadi K, Barati N, Zafari S, Hosseini SA, Soleymani E, Motavallihaghi S. Prevalence of Toxoplasma gondii infection in COVID-19 patients: A systematic review and meta-analysis. Microb Pathog 2024; 197:107064. [PMID: 39442817 DOI: 10.1016/j.micpath.2024.107064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 10/18/2024] [Accepted: 10/20/2024] [Indexed: 10/25/2024]
Abstract
Toxoplasma gondii is a parasite that affects over one billion people worldwide. COVID-19, caused by the SARS-CoV-2 virus, has resulted in over 4.8 million deaths worldwide. Both diseases activate the innate immune response via the same pathway. Studies have suggested that toxoplasmosis may either protect against or worsen the severity of COVID-19. This meta-analysis investigated the relationship between toxoplasmosis prevalence and COVID-19. The selection of studies was based on a systematic search using specific keywords in Scopus, Web of Science, PubMed, and Google Scholar databases between 2019 and 2023. The study findings were analyzed using STATA software version 17.0, and the prevalence of toxoplasmosis in people with COVID-19 and its confidence interval were extracted from the selected studies. The study's heterogeneity was assessed using the I2 test, and publication bias was evaluated using a funnel plot and Egger's test. A p value of 0.05 was considered significant. The meta-analysis included nine studies with a total of 1745 COVID-19-positive individuals, and the results showed a significant association between toxoplasmosis and COVID-19 severity. The I2 statistic was almost 99 %, indicating large heterogeneity among the studies. The Egger's test showed no publication bias. The pooled prevalence of toxoplasmosis in COVID-19-positive individuals was 0.48 (95 % CI: 0.30-0.66), which was significantly different from that of 0 % (P < 0.001). The meta-analysis found that the prevalence oftoxoplasmosis was significantly higher in individuals with COVID-19 than in the general population, indicating a possible association between the two infections. However, the significant heterogeneity among the studies underscores the need for further research to understand the underlying mechanisms and clinical implications of this association.
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Affiliation(s)
- Mahdi Fakhar
- Iranian National Registry Center for Lophomoniasis (INRCL) and Toxoplasmosis (INRCT), Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran; Department of Medical Microbiology and Immunology, School of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Khadijeh Najafi Ghobadi
- Department of Biostatistics, Faculty of Health, Ilam University of Medical Sciences, Ilam, Iran
| | - Nastaran Barati
- Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Salman Zafari
- Department of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Student's Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Hosseini
- Department of Medical Parasitology and Mycology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Eissa Soleymani
- Department of Parasitology, Toxoplasmosis Research Center, Communicable Diseases Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Seyedmousa Motavallihaghi
- Department of Medical Parasitology and Mycology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
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Pezzutti DL, Makary MS. Role of Imaging in Diagnosis and Management of COVID-19: Evidence-Based Approaches. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1457:237-246. [PMID: 39283430 DOI: 10.1007/978-3-031-61939-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.
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Affiliation(s)
- Dante L Pezzutti
- Department of Radiology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, 4th Floor, Columbus, OH, 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave, 4th Floor, Columbus, OH, 43210, USA.
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Han J, Xue J, Ye X, Xu W, Jin R, Liu W, Meng S, Zhang Y, Hu X, Yang X, Li R, Meng F. Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:2557-2566. [PMID: 37334890 DOI: 10.1002/jum.16289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/14/2023] [Accepted: 05/30/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVE The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases. METHODS Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared. RESULTS There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs. CONCLUSION Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.
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Affiliation(s)
- Jing Han
- Ultrasound and Functional Diagnosis Center, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Jun Xue
- Department of Echocardiography, China Emergency General Hospital, Beijing, China
| | - Xiangyang Ye
- Department of Orthopaedics, Nanyang Central Hospital, Nanyang, China
| | - Wei Xu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ronghua Jin
- Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Weiyuan Liu
- Ultrasound and Functional Diagnosis Center, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Sha Meng
- Department of Science and Technology Department, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Yuan Zhang
- Ultrasound and Functional Diagnosis Center, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Xing Hu
- Ultrasound and Functional Diagnosis Center, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Xi Yang
- Department of ultrasound, Hanyang Hospital Affiliated to Wuhan University of science and technology, Wuhan, China
| | - Ruili Li
- Radiology Department, Beijing You An Hospital, Capital Medical University, Beijing, China
| | - Fankun Meng
- Ultrasound and Functional Diagnosis Center, Beijing You An Hospital, Capital Medical University, Beijing, China
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Lee JH, Koh J, Jeon YK, Goo JM, Yoon SH. An Integrated Radiologic-Pathologic Understanding of COVID-19 Pneumonia. Radiology 2023; 306:e222600. [PMID: 36648343 PMCID: PMC9868683 DOI: 10.1148/radiol.222600] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 01/18/2023]
Abstract
This article reviews the radiologic and pathologic findings of the epithelial and endothelial injuries in COVID-19 pneumonia to help radiologists understand the fundamental nature of the disease. The radiologic and pathologic manifestations of COVID-19 pneumonia result from epithelial and endothelial injuries based on viral toxicity and immunopathologic effects. The pathologic features of mild and reversible COVID-19 pneumonia involve nonspecific pneumonia or an organizing pneumonia pattern, while the pathologic features of potentially fatal and irreversible COVID-19 pneumonia are characterized by diffuse alveolar damage followed by fibrosis or acute fibrinous organizing pneumonia. These pathologic responses of epithelial injuries observed in COVID-19 pneumonia are not specific to SARS-CoV-2 but rather constitute universal responses to viral pneumonia. Endothelial injury in COVID-19 pneumonia is a prominent feature compared with other types of viral pneumonia and encompasses various vascular abnormalities at different levels, including pulmonary thromboembolism, vascular engorgement, peripheral vascular reduction, a vascular tree-in-bud pattern, and lung perfusion abnormality. Chest CT with different imaging techniques (eg, CT quantification, dual-energy CT perfusion) can fully capture the various manifestations of epithelial and endothelial injuries. CT can thus aid in establishing prognosis and identifying patients at risk for deterioration.
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Affiliation(s)
- Jong Hyuk Lee
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jaemoon Koh
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Yoon Kyung Jeon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Jin Mo Goo
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
| | - Soon Ho Yoon
- From the Departments of Radiology (J.H.L., J.M.G., S.H.Y.) and
Pathology (J.K., Y.K.J.), Seoul National University Hospital, Seoul National
University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea;
Department of Radiology, Seoul National University College of Medicine, Seoul,
Korea (J.M.G.); Institute of Radiation Medicine, Seoul National University
Medical Research Center, Seoul, Korea (J.M.G.); and Cancer Research Institute,
Seoul National University, Seoul, Korea (J.M.G.)
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Zhang LJ, Yang J, Jin Z, Lu GM. Cardiothoracic Imaging in China: Opening Up New Horizons. J Thorac Imaging 2022; 37:353-354. [PMID: 36306266 PMCID: PMC9592163 DOI: 10.1097/rti.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
| | - Junjie Yang
- Senior Department of Cardiology, Sixth Medical Center of PLA General Hospital
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Guang Ming Lu
- Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province
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Hou N, Wang L, Li M, Xie B, He L, Guo M, Liu S, Wang M, Zhang R, Wang K. Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis. Front Public Health 2022; 10:939095. [PMID: 36311632 PMCID: PMC9616120 DOI: 10.3389/fpubh.2022.939095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/25/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Chest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside. METHODS Relevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China. RESULTS Of the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower. CONCLUSION Considering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.
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Affiliation(s)
- Nianzong Hou
- Center of Gallbladder Disease, Shanghai East Hospital, Institute of Gallstone Disease, School of Medicine, Tongji University, Shanghai, China
- Department of Hand and Foot Surgery, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Lin Wang
- Department of Critical Care Medicine, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Mingzhe Li
- School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom
| | - Bing Xie
- Department of Hand and Foot Surgery, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Lu He
- Department of Urology, Dongfeng Hospital, Hubei University of Medicine, Shiyan, China
| | - Mingyu Guo
- Department of Hand and Foot Surgery, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Shuo Liu
- Department of Hand and Foot Surgery, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Meiyu Wang
- Department of Cardiology, The People's Hospital of Zhangdian District, Zibo, China
| | - Rumin Zhang
- Department of Critical Care Medicine, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
| | - Kai Wang
- Department of Critical Care Medicine, Zibo Central Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Zibo, China
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Ma B, Qi J, Wu Y, Wang P, Li D, Liu S. Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm. DIGITAL SIGNAL PROCESSING 2022; 127:103577. [PMID: 35529477 PMCID: PMC9067002 DOI: 10.1016/j.dsp.2022.103577] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptible-exposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures.
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Affiliation(s)
- Baoshan Ma
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Jishuang Qi
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Yiming Wu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China
| | - Pengcheng Wang
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Di Li
- Department of Neuro Intervention, Dalian Medical University affiliated Dalian Municipal Central Hospital, Dalian, 116033, China
| | - Shuxin Liu
- Department of Nephrology, Dalian Medical University affiliated Dalian Municipal Central Hospital, Dalian, 116033, China
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Zaky S, Fathelbab HK, Elbadry M, El-Raey F, Abd-Elsalam SM, Makhlouf HA, Makhlouf NA, Metwally MA, Ali-Eldin F, Hasan AA, Alboraie M, Yousef AM, Shata HM, Eid A, Asem N, Khalaf A, Elnady MA, Elbahnasawy M, Abdelaziz A, Shaltout SW, Elshemy EE, Wahdan A, Hegazi MS, Abdel Baki A, Hassany M, On behalf of Ministry of Health and Population COVID-19 board, Egyptian Society of fever (ESF) and UCHID-COVID-19 special interest group. Egyptian Consensus on the Role of Lung Ultrasonography During the Coronavirus Disease 2019 Pandemic. Infect Drug Resist 2022; 15:1995-2013. [PMID: 36176457 PMCID: PMC9513721 DOI: 10.2147/idr.s353283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/28/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND & AIMS Coronavirus disease 2019 (COVID-19) is a global health problem, presenting with symptoms ranging from mild nonspecific symptoms to serious pneumonia. Early screening techniques are essential in the diagnosis and assessment of disease progression. This consensus was designed to clarify the role of lung ultrasonography versus other imaging modalities in the COVID-19 pandemic. METHODS A multidisciplinary team consisting of experts from different specialties (ie, pulmonary diseases, infectious diseases, intensive care unit and emergency medicine, radiology, and public health) who deal with patients with COVID-19 from different geographical areas was classified into task groups to review the literatures from different databases and generate 10 statements. The final consensus statements were based on expert physically panelists' discussion held in Cairo July 2021 followed by electric voting for each statement. RESULTS The statements were electronically voted to be either "agree," "not agree," or "neutral." For a statement to be accepted to the consensus, it should have 80% agreement. CONCLUSION Lung ultrasonography is a rapid and useful tool, which can be performed at bedside and overcomes computed tomography limitations, for screening and monitoring patients with COVID-19 with an accepted accuracy rate.
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Affiliation(s)
- Samy Zaky
- Department of Hepatogastroenterology and Infectious Diseases; Al-Azhar University, Cairo, Egypt
| | | | - Mohamed Elbadry
- Department of Endemic Medicine, Helwan University, Cairo, Egypt
| | - Fathiya El-Raey
- Department of Hepatogastroenterology and Infectious Diseases Al-Azhar University, Damietta, Egypt
| | | | | | - Nahed A Makhlouf
- Department of Tropical Medicine and Gastroenterology, Assiut University, Assiut, Egypt
| | - Mohamed A Metwally
- Department of Hepatology, Gastroenterology, and Infectious Diseases, Benha University, Benha, Egypt
| | - Fatma Ali-Eldin
- Department of Tropical medicine; Ain Shams University, Cairo, Egypt
| | | | - Mohamed Alboraie
- Department of Internal Medicine; Al-Azhar University, Cairo, Egypt
| | - Ahmed M Yousef
- Department of Community and Industrial Medicine, Damietta, Al-Azhar University, Damietta, Egypt
| | - Hanan M Shata
- Department of Chest Medicine; Mansoura University, Mansoura, Egypt
| | - Alshaimaa Eid
- Department of Hepatogastroenterology and Infectious Diseases; Al-Azhar University, Cairo, Egypt
| | - Noha Asem
- Department of Public Health and Community Medicine, Cairo University and Ministry of Health and Population, Cairo, Egypt
| | - Asmaa Khalaf
- Department of Radiology, Minia University, Minia, Egypt
| | - Mohamed A Elnady
- Department of Pulmonary Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamed Elbahnasawy
- Department of Emergency Medicine and Traumatology, Tanta University, Tanta, Egypt
| | - Ahmed Abdelaziz
- Department of Hepatogastroenterology and Infectious Diseases Al-Azhar University, Damietta, Egypt
| | - Shaker W Shaltout
- Department of Tropical Medicine, Port Said University, Port Said, Egypt
| | - Eman E Elshemy
- Department of Hepatogastroenterology and Infectious Diseases; Al-Azhar University, Cairo, Egypt
| | - Atef Wahdan
- Department of Chest Diseases, Damietta, Al-Azhar University, Damietta, Egypt
| | - Mohamed S Hegazi
- Department of Hepatogastroenterology and Infectious Diseases Al-Azhar University, Damietta, Egypt
| | - Amin Abdel Baki
- Department Hepatology, Gastroenterology and Infectious diseases National Hepatology and Tropical Medicine Research Institute NHTMRI, Cairo, Egypt
| | - Mohamed Hassany
- Department Hepatology, Gastroenterology and Infectious diseases National Hepatology and Tropical Medicine Research Institute NHTMRI, Cairo, Egypt
| | - On behalf of Ministry of Health and Population COVID-19 board, Egyptian Society of fever (ESF) and UCHID-COVID-19 special interest group
- Department of Hepatogastroenterology and Infectious Diseases; Al-Azhar University, Cairo, Egypt
- Department of Endemic diseases; Minia University, Minia, Egypt
- Department of Endemic Medicine, Helwan University, Cairo, Egypt
- Department of Hepatogastroenterology and Infectious Diseases Al-Azhar University, Damietta, Egypt
- Department of Tropical Medicine, Tanta University, Tanta, Egypt
- Department of Chest, Assiut University, Assiut, Egypt
- Department of Tropical Medicine and Gastroenterology, Assiut University, Assiut, Egypt
- Department of Hepatology, Gastroenterology, and Infectious Diseases, Benha University, Benha, Egypt
- Department of Tropical medicine; Ain Shams University, Cairo, Egypt
- Department of Internal Medicine; Al-Azhar University, Cairo, Egypt
- Department of Community and Industrial Medicine, Damietta, Al-Azhar University, Damietta, Egypt
- Department of Chest Medicine; Mansoura University, Mansoura, Egypt
- Department of Public Health and Community Medicine, Cairo University and Ministry of Health and Population, Cairo, Egypt
- Department of Radiology, Minia University, Minia, Egypt
- Department of Pulmonary Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
- Department of Emergency Medicine and Traumatology, Tanta University, Tanta, Egypt
- Department of Tropical Medicine, Port Said University, Port Said, Egypt
- Department of Chest Diseases, Damietta, Al-Azhar University, Damietta, Egypt
- Department Hepatology, Gastroenterology and Infectious diseases National Hepatology and Tropical Medicine Research Institute NHTMRI, Cairo, Egypt
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Quan Y, Li L, Yin Z, Chen S, Yi J, Lang J, Zhang L, Yue Q, Zhao J. Bulbus Fritillariae Cirrhosae as a Respiratory Medicine: Is There a Potential Drug in the Treatment of COVID-19? Front Pharmacol 2022; 12:784335. [PMID: 35126123 PMCID: PMC8811224 DOI: 10.3389/fphar.2021.784335] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/03/2021] [Indexed: 01/08/2023] Open
Abstract
Bulbus fritillariae cirrhosae (BFC) is one of the most used Chinese medicines for lung disease, and exerts antitussive, expectorant, anti-inflammatory, anti-asthmatic, and antioxidant effects, which is an ideal therapeutic drug for respiratory diseases such as ARDS, COPD, asthma, lung cancer, and pulmonary tuberculosis. Through this review, it is found that the therapeutic mechanism of BFC on respiratory diseases exhibits the characteristics of multi-components, multi-targets, and multi-signaling pathways. In particular, the therapeutic potential of BFC in terms of intervention of “cytokine storm”, STAT, NF-κB, and MAPK signaling pathways, as well as the renin-angiotensin system (RAS) that ACE is involved in. In the “cytokine storm” of SARS-CoV-2 infection there is an intense inflammatory response. ACE2 regulates the RAS by degradation of Ang II produced by ACE, which is associated with SARS-CoV-2. For COVID-19, may it be a potential drug? This review summarized the research progress of BFC in the respiratory diseases, discussed the development potentiality of BFC for the treatment of COVID-19, explained the chemical diversity and biological significance of the alkaloids in BFC, and clarified the material basis, molecular targets, and signaling pathways of BFC for the respiratory diseases. We hope this review can provide insights on the drug discovery of anti-COVID-19.
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Affiliation(s)
- Yunyun Quan
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
- Department of Pharmacognosy, West China School of Pharmacy Sichuan University, Chengdu, China
| | - Li Li
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Zhujun Yin
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Shilong Chen
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Jing Yi
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Jirui Lang
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Lu Zhang
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Qianhua Yue
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
| | - Junning Zhao
- Translational Chinese Medicine Key Laboratory of Sichuan Province, Sichuan Academy of Chinese Medicine Sciences, Sichuan Institute for Translational Chinese Medicine, Chengdu, China
- Department of Pharmacognosy, West China School of Pharmacy Sichuan University, Chengdu, China
- *Correspondence: Junning Zhao,
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10
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Hu Q, Liu Y, Chen C, Sun Z, Wang Y, Xiang M, Guan H, Xia L. Reversible Bronchiectasis in COVID-19 Survivors With Acute Respiratory Distress Syndrome: Pseudobronchiectasis. Front Med (Lausanne) 2021; 8:739857. [PMID: 34917630 PMCID: PMC8669592 DOI: 10.3389/fmed.2021.739857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/25/2021] [Indexed: 12/20/2022] Open
Abstract
To retrospectively analyze whether traction bronchiectasis was reversible in coronavirus disease 2019 (COVID-19) survivors with acute respiratory distress syndrome (ARDS), and whether computed tomography (CT) findings were associated with the reversibility, 41 COVID-19 survivors with ARDS were followed-up for more than 4 months. Demographics, clinical data, and all chest CT images were collected. The follow-up CT images were compared with the previous CT scans. There were 28 (68%) patients with traction bronchiectasis (Group I) and 13 (32%) patients without traction bronchiectasis (Group II) on CT images. Traction bronchiectasis disappeared completely in 21 of the 28 (75%) patients (Group IA), but did not completely disappear in seven of the 28 (25%) patients (Group IB). In the second week after onset, the evaluation score on CT images in Group I was significantly higher than that in Group II (p = 0.001). The proportion of reticulation on the last CT images in Group IB was found higher than that in Group IA (p < 0.05). COVID-19 survivors with ARDS might develop traction bronchiectasis, which can be absorbed completely in most patients. Traction bronchiectasis in a few patients did not disappear completely, but bronchiectasis was significantly relieved. The long-term follow-up is necessary to further assess whether traction bronchiectasis represents irreversible fibrosis.
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Affiliation(s)
- Qiongjie Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiwen Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chong Chen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyan Sun
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yujin Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Xiang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hanxiong Guan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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11
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Mohamed IAI, Hasan HA, Abdel-Tawab M. CT characteristics and laboratory findings of COVID-19 pneumonia in relation to patient outcome. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [PMCID: PMC7808117 DOI: 10.1186/s43055-020-00385-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background This study aimed to investigate the chest computed tomography (CT) characteristics and laboratory findings in patients with confirmed COVID-19 pneumonia and to evaluate their relationship with clinical outcome. This retrospective study assessed 164 consecutive CT chests of COVID-19 patients during April 2020. The chest CT and laboratory data were analyzed. The primary endpoint was patient survival either died or survived. The relationship between CT and laboratory findings was correlated to patient outcome. Results The study group included 164 patients (86 male, 78 women; average age, 44.3 ± 16.5 years) whose RT-PCR were positive for COVID-19. Only 120 (73.2%) patients had pulmonary manifestations. Ground glass opacities of peripheral distribution and multifocal affection were the major CT finding in COVID-19 patients. Univariate analysis revealed that CT severity score, D-dimer level, age, total leucocytic count, and absolute lymphocytic count were predictive for death. Conclusion CT has an emerging role in the diagnosis of COVID-19 pneumonia and in assessing disease severity. CT severity score, D-dimer, total leucocytic count, and absolute lymphocytic count significantly predict patient survival.
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12
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Yu L, Shi X, Liu X, Jin W, Jia X, Xi S, Wang A, Li T, Zhang X, Tian G, Sun D. Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19. Front Microbiol 2021; 12:729455. [PMID: 34650534 PMCID: PMC8507494 DOI: 10.3389/fmicb.2021.729455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/17/2021] [Indexed: 01/14/2023] Open
Abstract
Objectives: COVID-19 is highly infectious and has been widely spread worldwide, with more than 159 million confirmed cases and more than 3 million deaths as of May 11, 2021. It has become a serious public health event threatening people's lives and safety. Due to the rapid transmission and long incubation period, shortage of medical resources would easily occur in the short term of discovering disease cases. Therefore, we aimed to construct an artificial intelligent framework to rapidly distinguish patients with COVID-19 from common pneumonia and non-pneumonia populations based on computed tomography (CT) images. Furthermore, we explored artificial intelligence (AI) algorithms to integrate CT features and laboratory findings on admission to predict the clinical classification of COVID-19. This will ease the burden of doctors in this emergency period and aid them to perform timely and appropriate treatment on patients. Methods: We collected all CT images and clinical data of novel coronavirus pneumonia cases in Inner Mongolia, including domestic cases and those imported from abroad; then, three models based on transfer learning to distinguish COVID-19 from other pneumonia and non-pneumonia population were developed. In addition, CT features and laboratory findings on admission were combined to predict clinical types of COVID-19 using AI algorithms. Lastly, Spearman's correlation test was applied to study correlations of CT characteristics and laboratory findings. Results: Among three models to distinguish COVID-19 based on CT, vgg19 showed excellent diagnostic performance, with area under the curve (AUC) of the receiver operating characteristic (ROC) curve at 95%. Together with laboratory findings, we were able to predict clinical types of COVID-19 with AUC of the ROC curve at 90%. Furthermore, biochemical markers, such as C-reactive protein (CRP), LYM, and lactic dehydrogenase (LDH) were identified and correlated with CT features. Conclusion: We developed an AI model to identify patients who were positive for COVID-19 according to the results of the first CT examination after admission and predict the progression combined with laboratory findings. In addition, we obtained important clinical characteristics that correlated with the CT image features. Together, our AI system could rapidly diagnose COVID-19 and predict clinical types to assist clinicians perform appropriate clinical management.
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Affiliation(s)
- Lan Yu
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China.,Department of Endocrinology, Inner Mongolia People's Hospital, Hohhot, China
| | - Xiaoli Shi
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoling Liu
- Department of Otolaryngology, Inner Mongolia People's Hospital, Hohhot, China
| | - Wen Jin
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Xiaoqing Jia
- Baotou City Hospital for Infectious Diseases, Baotou, China
| | - Shuxue Xi
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Ailan Wang
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Tianbao Li
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiao Zhang
- Clinical Medical Research Center/Inner Mongolia Key Laboratory of Gene Regulation of the Metabolic Diseases, Inner Mongolia People's Hospital, Hohhot, China
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Dejun Sun
- Department of Pulmonary and Critical Care Medicine/Key Laboratory of National Health Commission for the Diagnosis & Treatment of COPD, Inner Mongolia People's Hospital, Hohhot, China
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13
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Sezer R, Esendagli D, Erol C, Hekimoglu K. New challenges for management of COVID-19 patients: Analysis of MDCT based "Automated pneumonia analysis program". Eur J Radiol Open 2021; 8:100370. [PMID: 34307790 PMCID: PMC8289632 DOI: 10.1016/j.ejro.2021.100370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/14/2021] [Accepted: 07/17/2021] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The aim of this study is to define the role of an "Automated Multi Detector Computed Tomography (MDCT) Pneumonia Analysis Program'' as an early outcome predictor for COVID-19 pneumonia in hospitalized patients. MATERIALS AND METHODS A total of 96 patients who had RT-PCR proven COVID-19 pneumonia diagnosed by non-contrast enhanced chest MDCT and hospitalized were enrolled in this retrospective study. An automated CT pneumonia analysis program was used for each patient to see the extent of disease. Patients were divided into two clinical subgroups upon their clinical status as good and bad clinical course. Total opacity scores (TOS), intensive care unit (ICU) entry, and mortality rates were measured for each clinical subgroups and also laboratory values were used to compare each subgroup. RESULTS Left lower lobe was the mostly effected side with a percentage of 78.12 % and followed up by right lower lobe with 73.95 %. TOS, ICU entry, and mortality rates were higher in bad clinical course subgroup. TOS values were also higher in patients older than 60 years and in patients with comorbidities including, Hypertension (HT), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and malignancy. CONCLUSION Automated MDCT analysis programs for pneumonia are fast and an objective way to define the disease extent in COVID-19 pneumonia and it is highly correlated with the disease severity and clinical outcome thus providing physicians with valuable knowledge from the time of diagnosis.
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Affiliation(s)
- Rahime Sezer
- Baskent University Faculty of Medicine, Department of Radiology, Turkey
| | - Dorina Esendagli
- Baskent University Faculty of Medicine, Department of Chest Diseases, Turkey
| | - Cigdem Erol
- Baskent University Faculty of Medicine, Department of Infectious Diseases, Turkey
| | - Koray Hekimoglu
- Baskent University Faculty of Medicine, Department of Radiology, Turkey
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14
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Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J Radiol 2021. [DOI: 10.4329/wjr.v13.i6.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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15
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Pezzutti DL, Wadhwa V, Makary MS. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J Radiol 2021; 13:171-191. [PMID: 34249238 PMCID: PMC8245752 DOI: 10.4329/wjr.v13.i6.171] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/15/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023] Open
Abstract
The role of radiology and the radiologist have evolved throughout the coronavirus disease-2019 (COVID-19) pandemic. Early on, chest computed tomography was used for screening and diagnosis of COVID-19; however, it is now indicated for high-risk patients, those with severe disease, or in areas where polymerase chain reaction testing is sparsely available. Chest radiography is now utilized mainly for monitoring disease progression in hospitalized patients showing signs of worsening clinical status. Additionally, many challenges at the operational level have been overcome within the field of radiology throughout the COVID-19 pandemic. The use of teleradiology and virtual care clinics greatly enhanced our ability to socially distance and both are likely to remain important mediums for diagnostic imaging delivery and patient care. Opportunities to better utilize of imaging for detection of extrapulmonary manifestations and complications of COVID-19 disease will continue to arise as a more detailed understanding of the pathophysiology of the virus continues to be uncovered and identification of predisposing risk factors for complication development continue to be better understood. Furthermore, unidentified advancements in areas such as standardized imaging reporting, point-of-care ultrasound, and artificial intelligence offer exciting discovery pathways that will inevitably lead to improved care for patients with COVID-19.
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Affiliation(s)
- Dante L Pezzutti
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
| | - Vibhor Wadhwa
- Department of Radiology, Weill Cornell Medical Center, New York City, NY 10065, United States
| | - Mina S Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States
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16
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Sun J, Peng L, Li T, Adila D, Zaiman Z, Melton GB, Ingraham N, Murray E, Boley D, Switzer S, Burns JL, Huang K, Allen T, Steenburg SD, Gichoya JW, Kummerfeld E, Tignanelli C. A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals. ARXIV 2021:arXiv:2106.02118v2. [PMID: 34099980 PMCID: PMC8183017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 06/07/2021] [Indexed: 11/22/2022]
Abstract
Importance An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite significant efforts, many limitations and biases exist in previously developed AI diagnostic models for COVID-19. Utilizing a large set of local and international CXR images, we developed an AI model with high performance on temporal and external validation. Objective Investigate real-time performance of an AI-enabled COVID-19 diagnostic support system across a 12-hospital system. Design Prospective observational study. Setting Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-I 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International), Indiana University (Indiana, USA), and Emory University (Georgia, USA). Participants Internal (training, temporal, and real-time validation): 51,592 CXRs; Public: 27,424 CXRs; External (Indiana University): 10,002 CXRs; External (Emory University): 2002 CXRs. Main Outcome and Measure Model performance assessed via receiver operating characteristic (ROC), Precision-Recall curves, and F1 score. Results Patients that were COVID-19 positive had significantly higher COVID-19 Diagnostic Scores (median .1 [IQR: 0.0-0.8] vs median 0.0 [IQR: 0.0-0.1], p < 0.001) than patients that were COVID-19 negative. Pre-implementation the AI-model performed well on temporal validation (AUROC 0.8) and external validation (AUROC 0.76 at Indiana U, AUROC 0.72 at Emory U). The model was noted to have unrealistic performance (AUROC > 0.95) using publicly available databases. Real-time model performance was unchanged over 19 weeks of implementation (AUROC 0.70). On subgroup analysis, the model had improved discrimination for patients with "severe" as compared to "mild or moderate" disease, p < 0.001. Model performance was highest in Asians and lowest in whites and similar between males and females. Conclusions and Relevance AI-based diagnostic tools may serve as an adjunct, but not replacement, for clinical decision support of COVID-19 diagnosis, which largely hinges on exposure history, signs, and symptoms. While AI-based tools have not yet reached full diagnostic potential in COVID-19, they may still offer valuable information to clinicians taken into consideration along with clinical signs and symptoms.
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Affiliation(s)
- Ju Sun
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Le Peng
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Taihui Li
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Dyah Adila
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Zach Zaiman
- Department of Computer Science, Emory University, Atlanta, GA
| | - Genevieve B. Melton
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
- Department of Surgery, University of Minnesota, Minneapolis, MN
| | - Nicholas Ingraham
- Department of Medicine, University of Minnesota, Division of Pulmonary and Critical Care, Minneapolis, MN
| | - Eric Murray
- M Health Fairview Informatics, Minneapolis, MN
| | - Daniel Boley
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
| | - Sean Switzer
- Department of Medicine, University of Minnesota, Minneapolis, MN
| | - John L. Burns
- The School of Medicine, Indiana University, Indianapolis, IN
| | - Kun Huang
- The School of Medicine, Indiana University, Indianapolis, IN
| | - Tadashi Allen
- Department of Radiology, University of Minnesota, Minneapolis, MN
| | | | | | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
| | - Christopher Tignanelli
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN
- Department of Surgery, University of Minnesota, Minneapolis, MN
- Department of Surgery, North Memorial Health Hospital, Robbinsdale, MN
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17
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Pang C, Hou Q, Yang Z, Ren L. Chest computed tomography as a primary tool in COVID-19 detection: an update meta-analysis. Clin Transl Imaging 2021; 9:341-351. [PMID: 34055674 PMCID: PMC8149579 DOI: 10.1007/s40336-021-00434-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 05/19/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE A growing number of publications have paid close attention to the chest computed tomography (CT) detection of COVID-19 with inconsistent diagnostic accuracy, the present meta-analysis assessed the available evidence regarding the overall performance of chest CT for COVID-19. METHODS 2 × 2 diagnostic table was extracted from each of the included studies. Data on specificity (SPE), sensitivity (SEN), negative likelihood ratio (LR-), positive likelihood ratio (LR+), and diagnostic odds ratio (DOR) were calculated purposefully. RESULTS Fifteen COVID-19 related publications met our inclusion criteria and were judged qualified for the meta-analysis. The following were summary estimates for diagnostic parameters of chest CT for COVID-19: SPE, 0.49 (95% CI 46-52%); SEN, 0.94 (95% CI 93-95%); LR-, 0.15 (95% CI 11-20%); LR+, 1.93 (95% CI 145-256%); DOR, 17.14 (95% CI 918-3199%); and the area under the receiver operating characteristic curve (AUC), 0.93. CONCLUSION Chest CT has high SEN, but the SPE is not ideal. It is highly recommended to use a combination of different diagnostic tools to achieve sufficient SEN and SPE. It should be taken into account as a diagnostic tool for current COVID-19 detection, especially for patients with symptoms. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s40336-021-00434-z.
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Affiliation(s)
- Caishuang Pang
- Chongqing Medical University, NO.1, Yi Xue Yuan Road, Yuzhong District, Chongqing, 400016 China
| | - Qingtao Hou
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016 China
| | - Zhaowei Yang
- Chongqing Medical University, NO.1, Yi Xue Yuan Road, Yuzhong District, Chongqing, 400016 China
| | - Liwei Ren
- Chongqing Medical University, NO.1, Yi Xue Yuan Road, Yuzhong District, Chongqing, 400016 China
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18
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Abadi MQH, Rahmati S, Sharifi A, Ahmadi M. HSSAGA: Designation and scheduling of nurses for taking care of COVID-19 patients using novel method of Hybrid Salp Swarm Algorithm and Genetic Algorithm. Appl Soft Comput 2021; 108:107449. [PMID: 33967657 PMCID: PMC8086267 DOI: 10.1016/j.asoc.2021.107449] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 04/12/2021] [Accepted: 04/14/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic is viewed as the most basic worldwide disaster that humankind has observed since the second World War. There is no report of any clinically endorsed antiviral medications or antibodies that are successful against COVID-19. It has quickly spread everywhere, presenting tremendous well-being, financial, ecological, and social difficulties to the whole human populace. The COVID flare-up is seriously disturbing the worldwide economy. Practically all the countries are battling to hinder the transmission of the malady by testing and treating patients, isolating speculated people through contact following, confining huge social affairs, keeping up total or incomplete lockdown, etc. Proper scheduling of nursing workers and optimal designation of nurses may significantly affect the quality of clinical facilities. It is delivered by eliminating unbalanced workloads or undue stress, which could lead to decreased nurse performance and potential human errors., Nurses are frequently asked to leave while caring for all sick patients. However, regular scheduling formulas are not thought to consider this possibility because they are out of scheduling control in typical scenarios. In this paper, a novel model of the Hybrid Salp Swarm Algorithm and Genetic Algorithm (HSSAGA) is proposed to solve nurses’ scheduling and designation. The findings of the suggested test function algorithm demonstrate that this algorithm has outperformed state-of-the-art approaches.
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Affiliation(s)
- Moein Qaisari Hasan Abadi
- Department of Industrial Engineering, K. N. Toosi University of Technology, P.O. Box: 15875-4416, Tehran, Iran
| | - Sara Rahmati
- Department of Industrial Engineering, Islamic Azad University Najaf-Abad Branch, P.O. Box: 8514143131, Isfahan, Iran
| | - Abbas Sharifi
- Department of Mechanical Engineering, Urmia University of Technology (UUT), P.O. Box: 57166-419, Urmia, Iran
| | - Mohsen Ahmadi
- Department of Industrial Engineering, Urmia University of Technology (UUT), P.O. Box: 57166-419, Urmia, Iran
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19
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Fan L, Le W, Zou Q, Zhou X, Wang Y, Tang H, Han J, Liu S. Initial CT features of COVID-19 predicting clinical category. CHINESE JOURNAL OF ACADEMIC RADIOLOGY 2021; 4:241-247. [PMID: 33644690 PMCID: PMC7896877 DOI: 10.1007/s42058-021-00056-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 01/27/2021] [Accepted: 02/03/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To analyze the initial CT features of different clinical categories of COVID-19. MATERIAL AND METHODS A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher's exact test, or Mann-Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection. RESULTS Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category. CONCLUSION The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s42058-021-00056-4.
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Affiliation(s)
- Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
| | - Wenqing Le
- Department of Critical Care, Wuhan Hankou Hospital, Er Qi Side, Road No. 7, Hubei, 430010 China
| | - Qin Zou
- Department of Radiology, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
| | - Xiuxiu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
| | - Yun Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
| | - Hao Tang
- Department of Respiratory and Critical Care Medicine, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
- Department of Critical Care, Wuhan Huoshenshan Hospital, Hubei, 430100 China
| | - Jiafa Han
- Department of Radiology, Wuhan Hankou Hospital, Er Qi Side Road, No. 7, Hubei, 430010 China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, No. 415 Fengyang Road, Shanghai, 200003 China
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20
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Correlation between CT findings and outcomes in 46 patients with coronavirus disease 2019. Sci Rep 2021; 11:1103. [PMID: 33441572 PMCID: PMC7806649 DOI: 10.1038/s41598-020-79183-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 11/30/2020] [Indexed: 02/07/2023] Open
Abstract
The aim of this study was to analyze initial chest computed tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Patients with RT-PCR-confirmed COVID-19 infection were assigned to recovery group if they made a full recovery and to death group if they died within 2 months of hospitalization. Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1–5 for each lobe, range; 0, none; 25, maximum). 40 patients who recovered from COVID-19 and six patients who died were enrolled. The initial chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who died had fibrosis in contrast to six (15%) patients who recovered from COVID-19. Most patients had subpleural lesions (89.0%) as well as bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus but only one (2.5%) patient who recovered (p < 0.001). In the death group of patients, the total CT score was higher than that of the recovery group (p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). A high CT score and diffuse distribution of lung lesions in COVID-19 are indicative of disease severity and short-term mortality.
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Kouhsari E, Azizian K, Sholeh M, Shayestehpour M, Hashemian M, Karamollahi S, Yaghoubi S, Sadeghiifard N. Clinical, epidemiological, laboratory, and radiological characteristics of novel Coronavirus (2019-nCoV) in retrospective studies: A systemic review and meta-analysis. Indian J Med Microbiol 2021; 39:104-115. [PMID: 33610239 PMCID: PMC7667392 DOI: 10.1016/j.ijmmb.2020.10.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Ebrahim Kouhsari
- Clinical Microbiology Research Center, Ilam University of Medical Sciences, Ilam, Iran; Laboratory Sciences Research Center, Golestan University of Medical Sciences, Gorgan, Iran
| | - Khalil Azizian
- Department of Lab Science, Sirjan School of Medical Sciences, Sirjan, Iran
| | - Mohammad Sholeh
- Department of Microbiology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Shayestehpour
- Department of Microbiology and Immunology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Autoimmune Diseases Research Center, Kashan University of Medical Sciences, Kashan, Iran
| | - Marzieh Hashemian
- Clinical Microbiology Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Somayeh Karamollahi
- Clinical Microbiology Research Center, Ilam University of Medical Sciences, Ilam, Iran
| | - Sajad Yaghoubi
- Department of Clinical Microbiology, Iranshahr University of Medical Sciences, Iranshahr, Iran.
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22
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Mathew RP, Jose M, Jayaram V, Joy P, George D, Joseph M, Sleeba T, Toms A. Current status quo on COVID-19 including chest imaging. World J Radiol 2020; 12:272-288. [PMID: 33510852 PMCID: PMC7802080 DOI: 10.4329/wjr.v12.i12.272] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/07/2020] [Accepted: 12/16/2020] [Indexed: 02/06/2023] Open
Abstract
With each day the number coronavirus disease 2019 (COVID-19) cases continue to rise rapidly and our imaging knowledge of this disease is expeditiously evolving. The role of chest computed tomography (CT) in the screening or diagnosis of COVID-19 remains the subject of much debate. Despite several months having passed since identifying the disease, and numerous studies related to it, controversy and concern still exists regarding the widespread use of chest CT in the evaluation and management of COVID-19 suspect patients. Several institutes and organizations around the world have released guidelines, recommendations and statements against the use of CT for diagnosing or screening COVID-19 infection and advocating its use only for those cases with a strong clinical suspicion of complication or an alternate diagnosis. However, these guidelines and recommendations are in disagreement with majority of the widely available literature, which strongly favour CT as a pivotal tool in the early diagnosis, management and even follow-up of COVID-19 infection. This article besides comprehensively reviewing the current status quo on COVID-19 disease in general, also writes upon the current consensus statements/recommendations on the use of diagnostic imaging in COVID-19 as well as highlighting the precautions and various disinfection procedures being employed world-wide at the workplace to prevent the spread of infection.
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Affiliation(s)
| | - Merin Jose
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Vinayak Jayaram
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Paul Joy
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Danny George
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Maria Joseph
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Teena Sleeba
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
| | - Ajith Toms
- Department of Radiology, Rajagiri Hospital, Kochi 683112, Kerala, India
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23
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Khatami F, Saatchi M, Zadeh SST, Aghamir ZS, Shabestari AN, Reis LO, Aghamir SMK. A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis. Sci Rep 2020; 10:22402. [PMID: 33372194 PMCID: PMC7769992 DOI: 10.1038/s41598-020-80061-2] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
Nowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85-90%), 46% (95% CI 29-63%), 69% (95% CI 56-72%), and 89% (95% CI 82-96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.
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Affiliation(s)
- Fatemeh Khatami
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Saatchi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Alireza Namazi Shabestari
- Department of Geriatric Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Leonardo Oliveira Reis
- UroScience and Department of Surgery (Urology), School of Medical Sciences, University of Campinas, Unicamp, and Pontifical Catholic University of Campinas, PUC-Campinas, Campinas, São Paulo, Brazil
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24
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Hefeda MM. CT chest findings in patients infected with COVID-19: review of literature. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [PMCID: PMC7691695 DOI: 10.1186/s43055-020-00355-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is a highly infectious disease causing severe respiratory distress syndrome that was first discovered by the end of 2019 in Wuhan, China. Main text A wide variety of CT findings in COVID-19 have been reported in different studies, and the CT findings differ according to the stage of the disease and disease severity and associated co-morbidities. We will discuss each sign separately and its importance in diagnosis and prognosis. Conclusion CT plays a pivotal role in the diagnosis and management of COVID-19 pneumonia. The typical appearance of COVID-19 pneumonia is bilateral patchy areas of ground glass infiltration, more in the lower lobes. The appearance of other signs like consolidation, air bronchogram, crazy pavement appearance, and air bubble signs appear during the course of the disease. In the context of pandemic, the CT chest can be used as a screening tool in symptomatic patients as it is cheaper, available, and time saving.
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25
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Affiliation(s)
- Yuan-Qiang Lu
- Department of Emergency Medicine, Zhejiang Provincial Key Laboratory for Diagnosis and Treatment of Aging and Physic-Chemical Injury Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, People's Republic of China.
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26
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Xu G, Yang Y, Du Y, Peng F, Hu P, Wang R, Yin M, Li T, Tu L, Sun J, Jiang T, Chang C. Clinical Pathway for Early Diagnosis of COVID-19: Updates from Experience to Evidence-Based Practice. Clin Rev Allergy Immunol 2020; 59:89-100. [PMID: 32328954 PMCID: PMC7180681 DOI: 10.1007/s12016-020-08792-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic is a significant global event in the history of infectious diseases. The SARS-CoV-2 appears to have originated from bats but is now easily transmissible among humans, primarily through droplet or direct contact. Clinical features of COVID-19 include high fever, cough, and fatigue which may progress to ARDS. Respiratory failure can occur rapidly after this. The primary laboratory findings include lymphopenia and eosinopenia. Elevated D-dimer, procalcitonin, and CRP levels may correlate with disease severity. Imaging findings include ground-glass opacities and patchy consolidation on CT scan. Mortality is higher in patients with hypertension, cardiac disease, diabetes mellitus, cancer, and COPD. Elderly patients are more susceptible to severe disease and death, while children seem to have lower rates of infection and lower mortality. Diagnostic criteria and the identification of persons under investigation have evolved as more data has emerged. However, the approach to diagnosis is still very variable from region to region, country to country, and even among different hospitals in the same city. The importance of a clinical pathway to implement the most effective and relevant diagnostic strategy is of critical importance to establish the control of this virus that is responsible for more and more deaths each day.
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Affiliation(s)
- Guogang Xu
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yongshi Yang
- Department of Allergy & Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, National Clinical Research Center for Immunologic Diseases, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China
| | - Yingzhen Du
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Fujun Peng
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 9, Dongdan 3rd, Dongcheng District, Beijing, 100005, China
- Suzhou Institute of Systems Medicine, Suzhou, 215123, Jiangsu, China
| | - Peng Hu
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Runsheng Wang
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Ming Yin
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tianzhi Li
- Department of Respiratory Medicine, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Medical College of PLA, Chinese PLA General Hospital, Beijing, 100853, China
| | - Lei Tu
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Jinlyu Sun
- Department of Allergy & Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, National Clinical Research Center for Immunologic Diseases, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
| | - Taijiao Jiang
- Center for Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 9, Dongdan 3rd, Dongcheng District, Beijing, 100005, China.
- Suzhou Institute of Systems Medicine, Suzhou, 215123, Jiangsu, China.
| | - Christopher Chang
- Division of Pediatric Immunology and Allergy, Joe DiMaggio Children's Hospital, 1131 N 35th Avenue, Suite 220, Hollywood, FL, 33021, USA.
- Division of Rheumatology, Allergy and Clinical Immunology, University of California, Davis, CA, 95616, USA.
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27
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Zhu Y, Gao ZH, Liu YL, Xu DY, Guan TM, Li ZP, Kuang JY, Li XM, Yang YY, Feng ST. Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19). J Infect 2020; 81:147-178. [PMID: 32277968 PMCID: PMC7194958 DOI: 10.1016/j.jinf.2020.03.033] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 03/21/2020] [Indexed: 01/01/2023]
Affiliation(s)
- Ying Zhu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China; Institution of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China
| | - Zhen-Hua Gao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China; Department of Radiology, Hui Ya Hospital of The First Affiliated Hospital, Sun Yat-sen University, Huizhou 516080, Guangdong, PR China
| | - Yang-Li Liu
- Division of Pulmonary and Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China
| | - Dan-Yang Xu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China
| | - Tian-Ming Guan
- Department of Radiology, Hui Ya Hospital of The First Affiliated Hospital, Sun Yat-sen University, Huizhou 516080, Guangdong, PR China
| | - Zi-Ping Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China
| | - Jian-Yi Kuang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China
| | - Xiang-Min Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China.
| | - You-You Yang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China.
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, PR China.
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28
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Fu L, Wang B, Yuan T, Chen X, Ao Y, Fitzpatrick T, Li P, Zhou Y, Lin YF, Duan Q, Luo G, Fan S, Lu Y, Feng A, Zhan Y, Liang B, Cai W, Zhang L, Du X, Li L, Shu Y, Zou H. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. J Infect 2020; 80:656-665. [PMID: 32283155 PMCID: PMC7151416 DOI: 10.1016/j.jinf.2020.03.041] [Citation(s) in RCA: 636] [Impact Index Per Article: 127.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 03/15/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To better inform efforts to treat and control the current outbreak with a comprehensive characterization of COVID-19. METHODS We searched PubMed, EMBASE, Web of Science, and CNKI (Chinese Database) for studies published as of March 2, 2020, and we searched references of identified articles. Studies were reviewed for methodological quality. A random-effects model was used to pool results. Heterogeneity was assessed using I2. Publication bias was assessed using Egger's test. RESULTS 43 studies involving 3600 patients were included. Among COVID-19 patients, fever (83.3% [95% CI 78.4-87.7]), cough (60.3% [54.2-66.3]), and fatigue (38.0% [29.8-46.5]) were the most common clinical symptoms. The most common laboratory abnormalities were elevated C-reactive protein (68.6% [58.2-78.2]), decreased lymphocyte count (57.4% [44.8-69.5]) and increased lactate dehydrogenase (51.6% [31.4-71.6]). Ground-glass opacities (80.0% [67.3-90.4]) and bilateral pneumonia (73.2% [63.4-82.1]) were the most frequently reported findings on computed tomography. The overall estimated proportion of severe cases and case-fatality rate (CFR) was 25.6% (17.4-34.9) and 3.6% (1.1-7.2), respectively. CFR and laboratory abnormalities were higher in severe cases, patients from Wuhan, and older patients, but CFR did not differ by gender. CONCLUSIONS The majority of COVID-19 cases are symptomatic with a moderate CFR. Patients living in Wuhan, older patients, and those with medical comorbidities tend to have more severe clinical symptoms and higher CFR.
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Affiliation(s)
- Leiwen Fu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Bingyi Wang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China; State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin, China; College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
| | - Tanwei Yuan
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Xiaoting Chen
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yunlong Ao
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Thomas Fitzpatrick
- Department of Internal Medicine, University of Washington, Seattle, Washington, USA
| | - Peiyang Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Yiguo Zhou
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Yi-Fan Lin
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China; School of Mathematical and Physical Sciences/Statistics, The University of Newcastle, Callaghan, Australia
| | - Qibin Duan
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; Kirby Institute, University of New South Wales, Sydney, Australia
| | - Ganfeng Luo
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Song Fan
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yong Lu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Anping Feng
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Yuewei Zhan
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Bowen Liang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Weiping Cai
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China
| | - Lin Zhang
- Department of Anesthesia and Intensive Care and Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Xiangjun Du
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China
| | - Linghua Li
- Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.
| | - Yuelong Shu
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China.
| | - Huachun Zou
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 510080, China; Kirby Institute, University of New South Wales, Sydney, Australia; Shenzhen Center for Disease Control and Prevention, Shenzhen, China; School of Public Health, Shanghai Jiao Tong University,Shanghai, China.
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29
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Kumar A, Arora A, Sharma P, Anikhindi SA, Bansal N, Singla V, Khare S, Srivastava A. Gastrointestinal and hepatic manifestations of Corona Virus Disease-19 and their relationship to severe clinical course: A systematic review and meta-analysis. Indian J Gastroenterol 2020; 39:268-284. [PMID: 32749643 PMCID: PMC7399358 DOI: 10.1007/s12664-020-01058-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/19/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Many case series on Corona Virus Disease (COVID-19) have reported gastrointestinal (GI) and hepatic manifestations in a proportion of cases; however, the data is conflicting. The relationship of GI and hepatic involvement with severe clinical course of COVID-19 has also not been explored. OBJECTIVES The main objectives were to determine the frequency of GI and hepatic manifestations of COVID-19 and to explore their relationship with severe clinical course. METHODS We searched PubMed for studies published between January 1, 2020, and March 25, 2020, with data on GI and hepatic manifestations in adult patients with COVID-19. These data were compared between patients with severe and good clinical course using the random-effects model and odds ratio (OR) as the effect size. If the heterogeneity among studies was high, sensitivity analysis was performed for each outcome. RESULTS We included 62 studies (8301 patients) in the systematic review and 26 studies (4676 patients) in the meta-analysis. Diarrhea was the most common GI symptom (9%), followed by nausea/vomiting (5%) and abdominal pain (4%). Transaminases were abnormal in approximately 25%, bilirubin in 9%, prothrombin time (PT) in 7%, and low albumin in 60%. Up to 20% patients developed severe clinical course, and GI and hepatic factors associated with severe clinical course were as follows: diarrhea (OR 2), high aspartate aminotransferase (OR 1.4), high alanine aminotransferase (OR 1.6), high bilirubin (OR 2.4), low albumin (OR 3.4), and high PT (OR 3). CONCLUSIONS GI and hepatic involvement should be sought in patients with COVID-19 since it portends severe clinical course. The pathogenesis of GI and hepatic involvement needs to be explored in future studies.
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Affiliation(s)
- Ashish Kumar
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India.
| | - Anil Arora
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Praveen Sharma
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Shrihari Anil Anikhindi
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Naresh Bansal
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Vikas Singla
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Shivam Khare
- Institute of Liver, Gastroenterology, and Pancreatico-Biliary Sciences, Sir Ganga Ram Hospital, Rajinder Nagar, New Delhi, 110 060, India
| | - Abhishyant Srivastava
- Dr. Baba Saheb Ambedkar Medical College and Hospital, Rohini, New Delhi, 110 085, India
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30
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Wang A, Zhao W, Xu Z, Gu J. Timely blood glucose management for the outbreak of 2019 novel coronavirus disease (COVID-19) is urgently needed. Diabetes Res Clin Pract 2020; 162:108118. [PMID: 32179126 PMCID: PMC7102524 DOI: 10.1016/j.diabres.2020.108118] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 03/06/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Aihong Wang
- Department of Endocrinology, PLA Strategic Support Force Characteristic Medical Center (The 306th Hospital of PLA), Beijing, China.
| | - Weibo Zhao
- Department of Endocrinology, PLA Strategic Support Force Characteristic Medical Center (The 306th Hospital of PLA), Beijing, China
| | - Zhangrong Xu
- Department of Endocrinology, PLA Strategic Support Force Characteristic Medical Center (The 306th Hospital of PLA), Beijing, China
| | - Jianwen Gu
- Prevention and Control Group of COVID-19, PLA Strategic Support Force Characteristic Medical Center, Beijing, China.
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