https://www.socictopen.socict.org/files/original/23f93adb8957a22469e81690e3f66f80.pdf cfa066f483ebf3c1b1cf30c84e76367c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predictors of COVID-19 severity: a systematic review and meta-analysis [version 1; peer review: 2 approved] Creator An entity primarily responsible for making the resource Jonny Karunia Fajar, Abram L. Wagner, Kuldeep Dhama, Yeni Purnamasari, Muhammad Ilmawan, Mudatsir Mudatsir, Laksmi Wulandari, Gatot Soegiarto, Bagus Aulia Mahdi, Galih Dwi Jayanto, Suhendra Suhendra, Yennie Ayu Setianingsih, Romi Hamdani, Daniel Alexander Suseno, Kartika Agustina, Hamdan Yuwafi Naim, Muchamad Muchlas, Hamid Hunaif Dhofi Alluza, Nikma Alfi Rosida, Mayasari Mayasari, Mustofa Mustofa, Adam Hartono, Richi Aditya, Firman Prastiwi, Fransiskus Xaverius Meku, Monika Sitio, Abdullah Azmy, Anita Surya Santoso, Radhitio Adi Nugroho, Camoya Gersom, Ali A. Rabaan, Sri Masyeni, Firzan Nainu, Harapan Harapan Description An account of the resource Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context 10.12688/f1000research.26186.1 Source A related resource from which the described resource is derived Biotemas Publisher An entity responsible for making the resource available Universidade Federal de Santa Catarina Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science, Medicine