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
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
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DOI：
10.1136/bmj.m3339
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发表时间：
2020-09-09
影响因子：
105.7
通讯作者：
Harrison, Ewen M.
中科院分区：
文献类型：
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作者：
Knight, Stephen R.;Ho, Antonia;Harrison, Ewen M.
目的开发并验证一种实用的风险评分方法，以预测2019冠状病毒病（covid-19）住院患者的死亡率。前瞻性观察队列研究。国际严重急性呼吸道和新发感染联盟（ISARIC）世界卫生组织（WHO）英国临床特征协议（CCP-UK）研究(由ISARIC冠状病毒临床特征联盟（ISARIC - 4c）在英格兰、苏格兰和威尔士的260家医院进行。对2020年2月6日至5月20日期间招募的一组患者进行了模型培训，并对2020年5月21日至6月29日期间在模型开发后招募的第二组患者进行了验证。参与者在最终数据提取前至少四周因covid-19入院的成年人（18岁）。主要结局指标：住院死亡率。结果衍生数据集中纳入35 463例患者（死亡率32.2%），验证数据集中纳入22 361例患者（死亡率30.1%）。最终的4C死亡率评分包括8个可在医院初始评估时获得的变量：年龄、性别、合并症数量、呼吸频率、外周氧饱和度、意识水平、尿素水平和C反应蛋白（评分范围0-21分）。4C评分对死亡率的判别性较高（衍生队列：受试者工作特征曲线下面积0.79,95%可信区间0.78 ~ 0.79；验证队列：0.77,0.76 ~ 0.77），校准效果良好（验证队列：校准大=0，斜率=1.0）。评分至少为15分的患者（n=4158, 19%）死亡率为62%（阳性预测值62%），而评分为3分或以下的患者死亡率为1% （n=1650, 7%；阴性预测值99%）。歧视性表现高于15个预先存在的风险分层评分（受试者工作特征曲线下面积范围为0.61-0.76），其他covid-19队列的评分通常表现较差（范围为0.63-0.73）。结论：基于医院就诊时常用的参数，开发并验证了一种易于使用的风险分层评分。4C死亡率评分优于现有评分，显示出直接为临床决策提供信息的效用，并可用于将covid-19住院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。研究注册号是66726260
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 ConsortiumISARIC-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 a18 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: calibrationin-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