英国利物浦大学Malcolm G Semple团队根据WHO临床特征描述方案开发了一个风险评分，对covid-19住院患者进行分层管理。2020年9月9日，该研究发表在《英国医学杂志》上。

为了开发和验证一个实用风险评分，以预测covid-19住院患者的死亡率，研究组在英格兰、苏格兰和威尔士的260家医院开展了一项前瞻性观察队列研究，对2020年2月6日至5月20日招募的一组患者进行模型训练，对2020年5月21日至6月29日开发模型后招募的第二组患者进行验证。所有患者的年龄均超过18岁，因covid-19住院至少四周。主要结局为住院死亡率。

衍生数据集包括35463名患者，死亡率为32.2%；验证数据集包括22361名患者，死亡率为30.1%。最终的4C死亡率评分包括八个变量，即年龄、性别、共病数量、呼吸频率、外周血氧饱和度、意识水平、尿素水平和C反应蛋白水平（评分范围为0-21分）。4C评分显示可高度区分死亡风险，且校准良好。得分在15分及以上的患者（4158例，19%）死亡率为62%，得分在3分及以下的患者（1650例，7%）死亡率为1%。4C评分的区分性表现高于现有的15个风险分层得分，其他covid-19队列的评分通常表现不佳。

总之，研究组开发验证了一个容易使用的风险分层评分，4C死亡率评分优于现有评分，可直接用于指导临床决策，以便对covid-19住院患者进行分层管理。

附：英文原文

Title: 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

Author: Stephen R Knight, Antonia Ho, Riinu Pius, Iain Buchan, Gail Carson, Thomas M Drake, Jake Dunning, Cameron J Fairfield, Carrol Gamble, Christopher A Green, Rishi Gupta, Sophie Halpin, Hayley E Hardwick, Karl A Holden, Peter W Horby, Clare Jackson, Kenneth A Mclean, Laura Merson, Jonathan S Nguyen-Van-Tam, Lisa Norman, Mahdad Noursadeghi, Piero L Olliaro, Mark G Pritchard, Clark D Russell, Catherine A Shaw, Aziz Sheikh, Tom Solomon, Cathie Sudlow, Olivia V Swann, Lance CW Turtle, Peter JM Openshaw, J Kenneth Baillie, Malcolm G Semple, Annemarie B Docherty, Ewen M Harrison

Issue&Volume: 2020/09/09

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 35463 patients were included in the derivation dataset (mortality rate 32.2%) and 22361 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.

DOI: 10.1136/bmj.m3339

Source: https://www.bmj.com/content/370/bmj.m3339