Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.
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DOI：
10.23889/ijpds.v5i4.1697
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发表时间：
2020
影响因子：
--
通讯作者：
Lyons RA
中科院分区：
文献类型：
--
作者：
Lyons J;Nafilyan V;Akbari A;Davies G;Griffiths R;Harrison EM;Hippisley-Cox J;Hollinghurst J;Khunti K;North L;Sheikh A;Torabi F;Lyons RA
COVID-19风险预测算法可用于从短期严重不利COVID-19结果（如住院和死亡）中识别风险个体。重要的是在不同和多样化的人群中验证这些算法，以帮助指导风险管理决策，并将疫苗接种和治疗计划瞄准社会中最脆弱的个体。对QCOVID风险预测算法进行外部验证，该算法可预测英国威尔士成年人群中COVID-19的死亡率结果。我们使用安全匿名信息链接（SAIL）数据库中常规收集的个人水平数据进行了一项回顾性队列研究。该队列包括年龄在19至100岁之间的个体，于2020年1月24日居住在威尔士，在提供SAIL的全科诊所注册，并随访至死亡或研究结束（2020年7月28日）。使用人口统计学、初级和二级医疗保健以及配药数据来推导用于开发已发表的QCOVID算法的所有预测变量。死亡率数据用于定义至确诊或疑似COVID-19死亡的时间。计算了两个时间段（2020年1月24日至4月30日和2020年5月1日至7月28日）的性能指标，包括R2值（解释的变异）、Brier评分以及区分度和校准指标，以评估算法性能。其中包括1，956，760人。第一和第二时间段分别发生了1，192例（0.06%）和610例（0.03%）COVID-19死亡。该算法很好地拟合了威尔士数据和人群，解释了第一阶段男性至死亡时间的68.8%（95%CI：66.9-70.4）变异，Harrell C统计量：0.929（95%CI：0.921-0.937）和D统计量：3.036（95%CI：2.913-3.159）。女性和第二个时间段的两性结果相似。英格兰开发的QCOVID算法可用于威尔士成年人口的公共卫生风险管理。
COVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society. To validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK. We conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance. 1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell’s C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes. The QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population.
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影响因子：
2
作者：
Austin PC;Steyerberg EW;Putter H
通讯作者：
Putter H
影响因子：
3.5
作者：
Lyons RA;Jones KH;John G;Brooks CJ;Verplancke JP;Ford DV;Brown G;Leake K
通讯作者：
Leake K
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.
影响因子：
15.8
作者：
Harrison, Stephanie L.;Fazio-Eynullayeva, Elnara;Lip, Gregory Y. H.
通讯作者：
Lip, Gregory Y. H.
DOI：
10.1001/jama.2020.6775
发表时间：
2020-01-01
期刊：
JAMA, Journal of the American Medical Association
影响因子：
--
作者：
Richardson, Safiya;Hirsch, Jamie S.;,
通讯作者：
,