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利用 ISARIC WHO 临床特征协议对因 COVID-19 住院的患者进行风险分层:4C 死亡率评分的制定和验证。

Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.

机构信息

Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK.

Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow, UK.

出版信息

BMJ. 2020 Sep 9;370:m3339. doi: 10.1136/bmj.m3339.


DOI:10.1136/bmj.m3339
PMID:32907855
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7116472/
Abstract

OBJECTIVE: To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). DESIGN: Prospective observational cohort study. SETTING: International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020 PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. MAIN OUTCOME MEASURE: In-hospital mortality. RESULTS: 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). CONCLUSIONS: An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. STUDY REGISTRATION: ISRCTN66726260.

摘要

目的:开发并验证一种实用的风险评分模型,以预测因 2019 年冠状病毒病(COVID-19)住院的患者的死亡率。

设计:前瞻性观察队列研究。

地点:国际严重急性呼吸与新发感染联盟(ISARIC)世界卫生组织(WHO)临床特征方案英国(CCP-UK)研究(由 ISARIC 冠状病毒临床特征联盟-ISARIC-4C 进行),在英格兰、苏格兰和威尔士的 260 家医院进行。模型训练在 2020 年 2 月 6 日至 5 月 20 日之间招募的患者队列中进行,验证在模型开发后于 2020 年 5 月 21 日至 6 月 29 日之间招募的第二个患者队列中进行。

参与者:至少在最后一次数据提取前四周因 COVID-19 住院的成年患者(年龄≥18 岁)。

主要观察结果:院内死亡率。

结果:35463 例患者纳入推导数据集(死亡率 32.2%),22361 例患者纳入验证数据集(死亡率 30.1%)。最终的 4C 死亡率评分纳入了 8 个在初始医院评估时即可获得的变量:年龄、性别、合并症数量、呼吸频率、外周血氧饱和度、意识水平、尿素水平和 C 反应蛋白(评分范围 0-21 分)。4C 评分对死亡率具有较高的区分度(推导队列:受试者工作特征曲线下面积 0.79,95%置信区间 0.78-0.79;验证队列:0.77,0.76-0.77),且具有很好的校准度(验证:大范围校准=0,斜率=1.0)。评分≥15 分(n=4158,19%)的患者死亡率为 62%(阳性预测值 62%),而评分≤3 分(n=1650,7%)的患者死亡率为 1%(阴性预测值 99%)。与其他已有的风险分层评分(受试者工作特征曲线下面积范围 0.61-0.76)相比,该评分的区分性能更高,而其他在 COVID-19 队列中开发的评分性能往往较差(范围 0.63-0.73)。

结论:基于入院时常见的参数,开发并验证了一种易于使用的风险分层评分模型。4C 死亡率评分优于现有的评分,具有实用价值,可直接指导临床决策,并可用于将因 COVID-19 住院的患者分为不同的管理组。该评分应进一步验证,以确定其在其他人群中的适用性。

研究注册:ISRCTN66726260。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/f53defd0f29a/knir060903.f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/5a02d3f68800/knir060903.f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/a47e37e1438a/knir060903.f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/f53defd0f29a/knir060903.f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/5a02d3f68800/knir060903.f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/a47e37e1438a/knir060903.f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82bf/8029664/f53defd0f29a/knir060903.f3.jpg

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本文引用的文献

[1]
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.

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Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.

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