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Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis.
Lai, Xinxing; Liu, Jian; Zhang, Tianyi; Feng, Luda; Jiang, Ping; Kang, Ligaoge; Liu, Qiang; Gao, Ying.
  • Lai X; Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  • Liu J; Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
  • Zhang T; Institute for Brain Disorders, Beijing University of Chinese Medicine, Beijing, China.
  • Feng L; Department of TCM Pulmonary Diseases, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China.
  • Jiang P; Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  • Kang L; Beijing University of Chinese Medicine, Beijing, China.
  • Liu Q; Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
  • Gao Y; Beijing University of Chinese Medicine, Beijing, China.
BMJ Open ; 10(12): e039813, 2020 12 24.
Article in English | MEDLINE | ID: covidwho-999256
ABSTRACT

INTRODUCTION:

With the threat of a worldwide pandemic of COVID-19, it is important to identify the prognostic factors for critical conditions among patients with non-critical COVID-19. Prognostic factors and models may assist front-line clinicians in rapid identification of high-risk patients, early management of modifiable factors, appropriate triaging and optimising the use of limited healthcare resources. We aim to systematically assess the clinical, laboratory and imaging predictors as well as prediction models for severe or critical illness and mortality in patients with COVID-19. METHODS AND

ANALYSIS:

All peer-reviewed and preprint primary articles with a longitudinal design that focused on prognostic factors or models for critical illness and mortality related to COVID-19 will be eligible for inclusion. A systematic search of 11 databases including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, Wanfang Data, SinoMed, bioRxiv, Arxiv and MedRxiv will be conducted. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction will be performed using the modified version of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and quality will be evaluated using the Newcastle-Ottawa Scale and the Quality In Prognosis Studies tool. The association between prognostic factors and outcomes of interest will be synthesised and a meta-analysis will be conducted with three or more studies reporting a particular factor in a consistent manner. ETHICS AND DISSEMINATION Ethical approval was not required for this systematic review. We will disseminate our findings through publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD 42020178798.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnostic Imaging / Critical Illness / Pandemics / Clinical Laboratory Services / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: BMJ Open Year: 2020 Document Type: Article Affiliation country: Bmjopen-2020-039813

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnostic Imaging / Critical Illness / Pandemics / Clinical Laboratory Services / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Reviews / Systematic review/Meta Analysis Limits: Humans Language: English Journal: BMJ Open Year: 2020 Document Type: Article Affiliation country: Bmjopen-2020-039813