School of Health, Victoria University of Wellington, Wellington, New Zealand
Usher Institute, The University of Edinburgh, Edinburgh, UK.
Thorax. 2022 May;77(5):497-504. doi: 10.1136/thoraxjnl-2021-217580. Epub 2021 Nov 15.
The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland.
We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020.
Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively.
Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.
QCovid 算法是一种风险预测工具,可用于根据 COVID-19 住院和死亡风险对个体进行分层。该算法的版本 1 是使用 2020 年 1 月 24 日至 4 月 30 日期间覆盖英格兰 1050 万患者的数据进行训练的。我们在苏格兰对 QCovid 算法的版本 1 进行了外部验证。
我们使用苏格兰人口的个人水平数据建立了一个全国 COVID-19 数据平台(540 万居民)。初级保健数据与逆转录 PCR(RT-PCR)病毒学检测、住院和死亡率数据相关联。我们评估了 QCovid 算法在我们的数据集预测 COVID-19 住院和死亡的性能,该数据集与原始研究的两个时间段相匹配:2020 年 3 月 1 日至 4 月 30 日,以及 2020 年 5 月 1 日至 6 月 30 日。
我们的数据集包括 5384819 人,代表 2020 年居住在苏格兰的估计人口(546.33 万)的 99%。该算法在第一阶段显示出良好的校准,但在时间重新校准之前,第二阶段的风险被系统高估。女性和男性在第一阶段的死亡哈雷尔 C 分别为 0.95(95%CI 0.94 至 0.95)和 0.93(95%CI 0.92 至 0.93)。女性和男性在第一阶段的住院哈雷尔 C 分别为 0.81(95%CI 0.80 至 0.82)和 0.82(95%CI 0.81 至 0.82)。
QCovid 算法的版本 1 在预测苏格兰成年居民 COVID-19 住院和死亡风险方面表现出高水平的区分度,适用于最初研究的两个时间段,但可能需要前瞻性的持续重新校准。