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COVID-19 患者发生重症和死亡的临床、实验室和影像学预测因素:系统评价和荟萃分析方案。

Clinical, laboratory and imaging predictors for critical illness and mortality in patients with COVID-19: protocol for a systematic review and meta-analysis.

机构信息

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.

Institute for TCM-X, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.

出版信息

BMJ Open. 2020 Dec 24;10(12):e039813. doi: 10.1136/bmjopen-2020-039813.

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.

摘要

简介

随着 COVID-19 全球大流行的威胁,确定非重症 COVID-19 患者出现危急情况的预后因素非常重要。预后因素和模型可以帮助一线临床医生快速识别高危患者,早期管理可改变的因素,进行适当的分诊,并优化有限的医疗资源的使用。我们旨在系统评估 COVID-19 患者严重或危急疾病和死亡的临床、实验室和影像学预测因素以及预测模型。

方法和分析

所有符合条件的研究都将包括在内,这些研究为具有纵向设计的同行评审和预印本原始文章,重点关注与 COVID-19 相关的危急疾病和死亡率的预后因素或模型。将对 11 个数据库(包括 PubMed、EMBASE、Web of Science、Cochrane 图书馆、CNKI、VIP、万方数据、SinoMed、bioRxiv、Arxiv 和 MedRxiv)进行系统搜索。研究选择将遵循系统评价和荟萃分析的首选报告项目的指南。使用改良的预测模型研究的批判性评估和数据提取清单来进行数据提取,并使用纽卡斯尔-渥太华量表和预后研究质量工具来评估质量。将综合预后因素与感兴趣结局之间的关联,并对以一致方式报告特定因素的三个或更多研究进行荟萃分析。

伦理和传播

本系统评价不需要伦理批准。我们将通过在同行评审期刊上发表研究结果来传播我们的发现。

PROSPERO 注册号:CRD 42020178798。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cbf/7768616/ebcf0d72be2f/bmjopen-2020-039813f01.jpg

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