Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study.
Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study.
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DOI：
10.1136/bmj-2022-072976
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发表时间：
2023-06-21
影响因子：
105.7
通讯作者：
Coupland, Carol A. C.
中科院分区：
文献类型：
--
作者：
Hippisley-Cox, Julia;Khunti, Kamlesh;Sheikh, Aziz;Nguyen-Van-Tam, Jonathan S.;Coupland, Carol A. C.
导出并验证风险预测算法（qcovid - 4），以估计在该病毒组粒变体在英格兰占主导地位期间，SARS-CoV-2检测结果呈阳性的人群中与covid-19相关的死亡和住院风险，并与NHS Digital的高风险队列进行评估。队列研究。QResearch数据库与2021年12月11日至2022年3月31日期间有关covid-19疫苗接种、SARS-CoV-2检测结果、住院人数以及癌症和死亡率数据的英国国家数据相关联，并随访至2022年6月30日。衍生队列中有130万成年人，验证队列中有15万成年人，年龄在18-100岁之间，检测结果为SARS-CoV-2感染阳性。主要终点为covid-19相关死亡，次要终点为covid-19住院。带有预测变量的风险方程由衍生队列中拟合的模型推导而来。在一个单独的验证队列中评估性能。衍生队列中1 297 922例SARS-CoV-2感染阳性检测结果中，随访期间有18 756例（1.5%）因covid-19相关住院，3878例（0.3%）因covid-19相关死亡。最终的qcovid - 4模型包括年龄、剥夺评分和一系列健康和社会人口因素、covid-19疫苗接种次数以及以前的SARS-CoV-2感染。在接种covid-19疫苗的人群中，与covid-19相关的死亡风险较低，有证据表明存在剂量-反应关系（男性接种一剂疫苗风险降低42%，接种四剂或更多疫苗风险降低92%）。先前的SARS-CoV-2感染与covid-19相关死亡风险降低相关（男性降低49%）。qcovid - 4算法对男性covid-19相关死亡时间变异的解释率为76.0%(95%置信区间为73.9% ~ 78.2%)，D统计量为3.65 (3.43 ~ 3.86),Harrell’s C统计量为0.970（0.962 ~ 0.979）。女性的结果也类似。qcovid - 4校准良好。在正确识别covid-19相关死亡高风险患者方面，qcovid - 4比NHS数字算法有效得多。在验证队列中461例与covid-19相关的死亡中，333例（72.2%）属于qcovid - 4高风险组，95例（20.6%）属于NHS数字高风险组。qcovid - 4风险算法是根据SARS-CoV-2病毒组粒变体在英格兰占主导地位期间的数据建模的，现在包括疫苗接种剂量和以前的SARS-CoV-2感染，并预测了检测结果阳性的人与covid-19相关的死亡。与NHS Digital采用的方法相比，qcovid - 4更准确地确定了绝对风险最高的个体，需要进行有针对性的干预。QCOVID4表现良好，可用于covid-19疾病的靶向治疗。
To derive and validate risk prediction algorithms (QCOVID4) to estimate the risk of covid-19 related death and hospital admission in people with a positive SARS-CoV-2 test result during the period when the omicron variant of the virus was predominant in England, and to evaluate performance compared with a high risk cohort from NHS Digital. Cohort study. QResearch database linked to English national data on covid-19 vaccinations, SARS-CoV-2 test results, hospital admissions, and cancer and mortality data, 11 December 2021 to 31 March 2022, with follow-up to 30 June 2022. 1.3 million adults in the derivation cohort and 0.15 million adults in the validation cohort, aged 18-100 years, with a positive test result for SARS-CoV-2 infection. Primary outcome was covid-19 related death and secondary outcome was hospital admission for covid-19. Risk equations with predictor variables were derived from models fitted in the derivation cohort. Performance was evaluated in a separate validation cohort. Of 1 297 922 people with a positive test result for SARS-CoV-2 infection in the derivation cohort, 18 756 (1.5%) had a covid-19 related hospital admission and 3878 (0.3%) had a covid-19 related death during follow-up. The final QCOVID4 models included age, deprivation score and a range of health and sociodemographic factors, number of covid-19 vaccinations, and previous SARS-CoV-2 infection. The risk of death related to covid-19 was lower among those who had received a covid-19 vaccine, with evidence of a dose-response relation (42% risk reduction associated with one vaccine dose and 92% reduction with four or more doses in men). Previous SARS-CoV-2 infection was associated with a reduction in the risk of covid-19 related death (49% reduction in men). The QCOVID4 algorithm for covid-19 explained 76.0% (95% confidence interval 73.9% to 78.2%) of the variation in time to covid-19 related death in men with a D statistic of 3.65 (3.43 to 3.86) and Harrell’s C statistic of 0.970 (0.962 to 0.979). Results were similar for women. QCOVID4 was well calibrated. QCOVID4 was substantially more efficient than the NHS Digital algorithm for correctly identifying patients at high risk of covid-19 related death. Of the 461 covid-19 related deaths in the validation cohort, 333 (72.2%) were in the QCOVID4 high risk group and 95 (20.6%) in the NHS Digital high risk group. The QCOVID4 risk algorithm, modelled from data during the period when the omicron variant of the SARS-CoV-2 virus was predominant in England, now includes vaccination dose and previous SARS-CoV-2 infection, and predicted covid-19 related death among people with a positive test result. QCOVID4 more accurately identified individuals at the highest levels of absolute risk for targeted interventions than the approach adopted by NHS Digital. QCOVID4 performed well and could be used for targeting treatments for covid-19 disease.
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影响因子：
4.8
作者：
Royston, Patrick
通讯作者：
Royston, Patrick
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
DOI：
10.1016/s2213-8587(21)00089-9
发表时间：
2021-06
期刊：
The lancet. Diabetes & endocrinology
影响因子：
--
作者：
Gao M;Piernas C;Astbury NM;Hippisley-Cox J;O'Rahilly S;Aveyard P;Jebb SA
通讯作者：
Jebb SA
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：
--
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
通讯作者：
Lyons RA