英国纽菲尔德初级保健科学部门Julia Hippisley-Cox团队针对covid-19患者的住院和死亡风险开发了一种生命风险预测算法（QCOVID）。2020年10月20日，该研究发表在《英国医学杂志》上。

为了推导并验证一种风险预测算法，以评估成人covid-19患者的住院率和死亡率，研究组进行了一项基于人群的队列研究。

使用QResearch数据库，该数据库与covid-19检测结果、医院统计数据和死亡登记数据关联。衍生数据集包含608万名19-100岁的成年人，验证数据集包括217万成年人。

衍生和首次验证队列时间段为2020年1月24日至2020年4月30日。第二次验证队列时间段为2020年5月1日到2020年6月30日。主要结局为患covid-19后的死亡时间。次要结局为确诊SARS-CoV-2感染的住院时间。

在随访期间，衍生队列中有4384例covid-19患者死亡，在首次验证队列期间有1722例死亡，在第二次验证队列期间有621例死亡。最终的风险算法包括年龄、种族、贫困、体重指数和一系列合并症。该算法在第一个验证队列中具有良好的校准效果。

对于死于covid-19的男性，该算法解释了73.1％的死亡时间变化，D统计量为3.37，Harrell'C为0.928。对于女性而言，在两个时间段均获得与男性相似的结果。在预测死亡风险最高的前5％患者中，97天内死亡的敏感性为75.7％。预测死亡风险中排名前20％的人占covid-19所有死亡人数的94％。

基于QCOVID人群的风险算法性能良好，可准确辨识covid-19导致的死亡和住院。然而，该算法所呈现的绝对风险随着时间的推移而变化，这与该时期的SARS-CoV-2流行率和社交疏离措施的程度相一致，因此应谨慎解释。

附：英文原文

Title: Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

Author: Ash K Clift, Carol A C Coupland, Ruth H Keogh, Karla Diaz-Ordaz, Elizabeth Williamson, Ewen M Harrison, Andrew Hayward, Harry Hemingway, Peter Horby, Nisha Mehta, Jonathan Benger, Kamlesh Khunti, David Spiegelhalter, Aziz Sheikh, Jonathan Valabhji, Ronan A Lyons, John Robson, Malcolm G Semple, Frank Kee, Peter Johnson, Susan Jebb, Tony Williams, Julia Hippisley-Cox

Issue&Volume: 2020/10/20

Abstract:

Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

Design Population based cohort study.

Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. 

DOI: 10.1136/bmj.m3731

Source: https://www.bmj.com/content/371/bmj.m3731