Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.
Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7.6 Million US Veterans for Use in Vaccination Prioritization.
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DOI：
10.1001/jamanetworkopen.2021.4347
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发表时间：
2021-04-01
影响因子：
13.8
通讯作者：
Berry K
中科院分区：
文献类型：
--
作者：
Ioannou GN;Green P;Fan VS;Dominitz JA;O'Hare AM;Backus LI;Locke E;Eastment MC;Osborne TF;Ioannou NG;Berry K
如何在一般人群中估计SARS-CoV-2相关死亡的风险，以用于疫苗接种优先级？在这项对退伍军人事务部卫生保健系统登记的760多万人进行的预后研究中，开发了一种逻辑回归模型（COVIDVax），使用以下10个特征来估计SARS-CoV-2相关死亡的风险：性别，年龄，种族，民族，体重指数，Charlson Comorbid指数，糖尿病，慢性肾脏疾病，充血性心力衰竭和护理评估需求评分。据估计，该模型比根据年龄或美国疾病控制和预防中心疫苗接种分配优先接种疫苗挽救了更多的生命。这些发现表明，根据本研究中开发的模型优先接种疫苗可以在疫苗推广期间预防大量SARS-CoV-2相关死亡。这项预后研究开发了一个模型，估计美国退伍军人事务部（VA）卫生保健系统所有登记者中SARS-CoV-2相关死亡率的风险。根据SARS-CoV-2相关死亡率的风险来优先接种SARS-CoV-2疫苗的策略将有助于最大限度地减少疫苗推广期间的死亡。建立一个模型，估计美国退伍军人事务部（VA）医疗保健系统所有登记者中SARS-CoV-2相关死亡率的风险。这项预后研究使用了截至2020年5月21日在VA医疗保健系统登记的7 635 064人的数据，以开发和内部验证逻辑回归模型（COVIDVax）预测SARS-CoV-2相关死亡（n = 2422）观察期内（2020年5月21日至11月2日）使用已知与SARS-CoV-2相关死亡率相关的基线特征，从VA电子健康记录（EHR）中提取。该队列分为培训期（5月21日至9月30日）和测试期（10月1日至11月2日）。SARS-CoV-2相关死亡，定义为SARS-CoV-2检测阳性后30天内死亡。VA EHR数据流被导入数据集成平台，以证明该模型可以实时执行，为所有当前VA注册者生成具有风险评分的仪表板。在7 635 064例受试者中，平均（SD）年龄为66.2（13.8）岁，大多数为男性（7 051 912 [92.4%]）和白色个体（4 887 338 [64.0%]），黑人个体为1 116 435（14.6%），西班牙裔个体为399 634（5.2%）。从16个潜在预测因子的起始池中，10个被纳入最终COVIDVax模型，如下所示：性别、年龄、人种、种族、体重指数、Charlson合并症指数、糖尿病、慢性肾脏疾病、充血性心力衰竭和护理评估需求评分。该模型显示出极好的区分度，受试者工作特征曲线下面积（AUROC）为85.3%（95%CI，84.6%-86.1%），上级于仅使用年龄分层风险的AUROC（72.6%; 95%CI，71.6%-73.6%）。假设疫苗接种在预防SARS-CoV-2相关死亡方面有90%的有效性，使用该模型来优先接种疫苗，估计在50%的VA登记者接种疫苗时，可以预防63.5%的死亡，明显高于按年龄优先接种疫苗的估计数（45.6%）或美国疾病控制和预防中心疫苗分配阶段（41.1%）。在这项对所有VA入组者进行的预后研究中，估计基于COVIDVax模型优先接种疫苗，可在实现足够的群体免疫力之前预防疫苗推广期间预计发生的大部分死亡。
How can the risk of SARS-CoV-2–related death be estimated in the general population to be used for vaccination prioritization? In this prognostic study of more than 7.6 million individuals enrolled in the Veterans Affairs health care system, a logistic regression model (COVIDVax) was developed to estimate risk of SARS-CoV-2–related death using the following 10 characteristics: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and the Care Assessment Need score. The model was estimated to save more lives than prioritizing vaccination based on age or on the US Centers for Disease Control and Prevention vaccination allocation. These findings suggest that prioritizing vaccination based on the model developed in this study could prevent a substantial number of SARS-CoV-2–related deaths during vaccine rollout. This prognostic study develops a model that estimates the risk of SARS-CoV-2–related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2–related mortality would help minimize deaths during vaccine rollout. To develop a model that estimates the risk of SARS-CoV-2–related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. This prognostic study used data from 7 635 064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2–related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2–related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). SARS-CoV-2–related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. Of 7 635 064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7 051 912 [92.4%]) and White individuals (4 887 338 [64.0%]), with 1 116 435 (14.6%) Black individuals and 399 634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2–related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved.
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BNT162B2 mRNA COVID-19疫苗的安全性和功效。
DOI：
10.1056/nejmoa2034577
发表时间：
2020-12-31
期刊：
The New England journal of medicine
影响因子：
--
作者：
Polack FP;Thomas SJ;Kitchin N;Absalon J;Gurtman A;Lockhart S;Perez JL;Pérez Marc G;Moreira ED;Zerbini C;Bailey R;Swanson KA;Roychoudhury S;Koury K;Li P;Kalina WV;Cooper D;Frenck RW Jr;Hammitt LL;Türeci Ö;Nell H;Schaefer A;Ünal S;Tresnan DB;Mather S;Dormitzer PR;Şahin U;Jansen KU;Gruber WC;C4591001 Clinical Trial Group
通讯作者：
C4591001 Clinical Trial Group
影响因子：
3.3
作者：
Sohn MW;Arnold N;Maynard C;Hynes DM
通讯作者：
Hynes DM
影响因子：
3.7
作者：
King JT Jr;Yoon JS;Rentsch CT;Tate JP;Park LS;Kidwai-Khan F;Skanderson M;Hauser RG;Jacobson DA;Erdos J;Cho K;Ramoni R;Gagnon DR;Justice AC
通讯作者：
Justice AC
影响因子：
3.7
作者：
Osborne, Thomas F.;Veigulis, Zachary P.;Curtin, Catherine M.
通讯作者：
Curtin, Catherine M.
影响因子：
7.2
作者：
DEYO, RA;CHERKIN, DC;CIOL, MA
通讯作者：
CIOL, MA