{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:40:01Z","timestamp":1774611601275,"version":"3.50.1"},"update-to":[{"DOI":"10.12688\/f1000research.26186.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000}}],"reference-count":94,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014538","name":"Lembaga Pengelola Dana Pendidikan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014538","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns4:p>\n                    <ns4:bold>Background<\/ns4:bold>\n                    : 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.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Objective<\/ns4:bold>\n                    : To identify the predictors associated with poor clinical outcomes in patients with COVID-19.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Methods<\/ns4:bold>\n                    : Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). 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.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Results<\/ns4:bold>\n                    : 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.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Conclusion<\/ns4:bold>\n                    : 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.\n                  <\/ns4:p>","DOI":"10.12688\/f1000research.26186.2","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T03:45:19Z","timestamp":1609904719000},"page":"1107","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":54,"title":["Predictors of COVID-19 severity: a systematic review and meta-analysis"],"prefix":"10.12688","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5643-9384","authenticated-orcid":false,"given":"Mudatsir","family":"Mudatsir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0309-5813","authenticated-orcid":false,"given":"Jonny 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status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.30622.r76646, Morteza Arab-Zozani, Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran, 19 Jan 2021, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/9-1107\/v2#referee-response-76646","order":0,"name":"referee-response-76646","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.28897.r72568, Annelies Wilder-Smith, Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany, 02 Nov 2020, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/9-1107\/v1#referee-response-72568","order":1,"name":"referee-response-72568","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Mudatsir Mudatsir<\/b>; \n<i>Posted: 03 Nov 2020<\/i>; Response to comments from reviewers: Reviewer 2# 1. The strength of this paper is the meta-analysis in terms of effect estimates. The weakness is the focus of data from China, while we should learn more from global data including the comparison between HIC and LMIC. In China, severity was also found to correlate with the force of infection, eg those in high transmission areas had more severe disease outcomes than those from lower transmission areas in China, see: Exposure to SARS-CoV-2 in a high transmission setting increases the risk of severe COVID-19 compared with exposure to a low transmission setting? Chen D, Hu C, Su F, Song Q, Wang Z. J Travel Med. 2020 Aug 20;27(5):taaa094. doi: 10.1093\/jtm\/taaa094.1 Response: The additional limitation has been added, as suggested  2.&nbsp;The authors highlight the need for a scoring system for the prediction of severity. There is another reason why it is important to identify risk factors for severe disease: to guide prioritization of high risk target populations for vaccination. Response: The additional clinical implication has been added, as suggested.","URL":"https:\/\/f1000research.com\/articles\/9-1107\/v2#referee-comment-6090","order":2,"name":"referee-comment-6090","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"We thank to Lembaga Pengelola Dana Pendidikan (LPDP) Republik Indonesia for supporting this project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}