Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.
Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.
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DOI：
10.1136/bmj.m3731
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发表时间：
2020-10-20
期刊：
影响因子：
--
通讯作者：
Hippisley-Cox J
中科院分区：
文献类型：
--
作者：
Clift AK;Coupland CAC;Keogh RH;Diaz-Ordaz K;Williamson E;Harrison EM;Hayward A;Hemingway H;Horby P;Mehta N;Benger J;Khunti K;Spiegelhalter D;Sheikh A;Valabhji J;Lyons RA;Robson J;Semple MG;Kee F;Johnson P;Jebb S;Williams T;Hippisley-Cox J
推导并验证风险预测算法，以估计成人因冠状病毒病2019年(新冠肺炎)的住院和死亡结果。基于人群的队列研究。QResearch数据库，包括英国的1205个一般实践，并链接到新冠肺炎测试结果、医院事件统计和死亡登记数据。派生数据集中包括608万年龄在19-100岁之间的成年人，验证数据集中包括217万。派生和第一次验证队列的时间为2020年1月24日至2020年4月30日。第二个时间验证队列涵盖2020年5月1日至2020年6月30日。主要结果是死于新冠肺炎的时间，根据死亡证明，定义为因确诊或疑似新冠肺炎而死亡，或发生于2020年1月24日至4月30日期间确诊感染严重急性呼吸系统综合症冠状病毒(SARS-CoV-2)的患者死亡。次要结果是确诊为SARS-CoV-2感染的患者入院时间。在派生队列中对模型进行拟合，以使用一系列预测变量导出风险方程。在每个验证时间段内对绩效进行评估，包括区分和校准措施。在随访期间，派生队列中发生了4,384例来自新冠肺炎的死亡，1,722例发生在第一次验证队列期间，621例发生在第二次验证队列期间。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有较好的校正效果。对于男性新冠肺炎死亡，它解释了死亡时间(R2)的73.1%(95%可信区间71.9%至74.3%)；D统计量为3.37%(95%可信区间3.273.47)，哈雷尔的C为0.928(0.919至0.938)。在两个结果和两个时间段内，女性都得到了类似的结果。在预测死亡风险最高的前5%的患者中，识别97天内死亡的敏感性为75.7%。在新冠肺炎所有死亡病例中，排名前20%预测死亡风险的人群占94%。QCOVID基于人群的风险算法表现良好，显示出由于新冠肺炎而导致的死亡和住院的非常高的歧视水平。然而，存在的绝对风险将随着时间的推移而变化，与流行的SARS-C0V-2感染率和社会疏远措施的程度一致，因此应谨慎解读。然而，该模型可以针对不同的时间段进行重新校准，并有可能随着大流行的演变而动态更新。
To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Population based cohort study. 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. 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. 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. 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. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
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影响因子：
7.7
作者：
Booth S;Riley RD;Ensor J;Lambert PC;Rutherford MJ
通讯作者：
Rutherford MJ
DOI：
10.1136/bmj.j4208
发表时间：
2017-09-20
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Hippisley-Cox J;Coupland C
通讯作者：
Coupland C
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
DOI：
10.1016/s2213-8587(20)30236-9
发表时间：
2020-08
期刊：
The lancet. Diabetes & endocrinology
影响因子：
--
作者：
Le Brocq S;Clare K;Bryant M;Roberts K;Tahrani AA;writing group form Obesity UK;Obesity Empowerment Network;UK Association for the Study of Obesity
通讯作者：
UK Association for the Study of Obesity
影响因子：
--
作者：
GILLON, R
通讯作者：
GILLON, R