Simple scoring tool to estimate risk of hospitalization and mortality in ambulatory and emergency department patients with COVID-19.
Simple scoring tool to estimate risk of hospitalization and mortality in ambulatory and emergency department patients with COVID-19.
复制标题
DOI：
10.1371/journal.pone.0261508
复制
发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Bledsoe J
中科院分区：
文献类型：
--
作者：
Webb BJ;Levin NM;Grisel N;Brown SM;Peltan ID;Spivak ES;Shah M;Stenehjem E;Bledsoe J
随着单克隆抗体等可用性有限的疗法的出现，识别高风险预后不良的COVID-19患者的准确方法变得尤为重要。在这里，我们描述了一个简单但准确的评分工具，以分类住院和死亡的风险的发展和验证。包括2020年3月25日至10月1日在Intermountain Healthcare系统内检测SARS-CoV-2阳性的所有连续患者。该队列随机分为70%推导队列和30%验证队列。多变量logistic回归模型拟合14天住院。然后将最佳模型调整为简单的概率评分，并应用于验证队列，并评估住院和28天死亡率的预测。纳入了22，816例患者;平均年龄为40岁，50.1%为女性，44%确定为非白人或西班牙裔/拉丁裔。6.2%需要住院治疗，0.4%死亡。简单模型中的标准包括：（每十年0.5分）;高风险合并症（各2分）：糖尿病、严重免疫功能低下状态和肥胖（体重指数≥30）;非白人/西班牙裔或拉丁裔（2分），每项1分：男性、呼吸困难、高血压、冠状动脉疾病、心律失常、充血性心力衰竭、慢性肾病、慢性肺病、慢性肝病、脑血管疾病和慢性神经系统疾病。在衍生队列中（n = 16，030）受试者-操作者特征曲线下面积（AUROC）为0.82（95% CI 0.81-0.84）住院和0.91（0.83-0.94）;在验证队列（n = 6，786）中，住院的AUROC为0.8（CI 0.78-0.82），死亡率为0.8（CI 0.69-0.9）。基于广泛可用的患者属性的预测评分在检测时准确地对COVID-19患者进行风险分层。应用包括针对预防非住院患者疾病进展的治疗（包括单克隆抗体）的患者选择。需要在独立的医疗保健环境中进行外部验证。
Accurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality. All consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality. 22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n = 16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81–0.84) for hospitalization and 0.91 (0.83–0.94) for 28-day mortality; in the validation cohort (n = 6,786) AUROC for hospitalization was 0.8 (CI 0.78–0.82) and for mortality 0.8 (CI 0.69–0.9). A prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.
登录
查看更多内容
DOI：
10.1136/bmj.m4509
发表时间：
2020-11-27
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Pastor-Barriuso R;Pérez-Gómez B;Hernán MA;Pérez-Olmeda M;Yotti R;Oteo-Iglesias J;Sanmartín JL;León-Gómez I;Fernández-García A;Fernández-Navarro P;Cruz I;Martín M;Delgado-Sanz C;Fernández de Larrea N;León Paniagua J;Muñoz-Montalvo JF;Blanco F;Larrauri A;Pollán M;ENE-COVID Study Group
通讯作者：
ENE-COVID Study Group
影响因子：
7.2
作者：
DEYO, RA;CHERKIN, DC;CIOL, MA
通讯作者：
CIOL, MA
影响因子：
3
作者：
Elixhauser, A;Steiner, C;Coffey, RN
通讯作者：
Coffey, RN
影响因子：
4.2
作者：
Fisman DN;Greer AL;Hillmer M;Tuite R
通讯作者：
Tuite R
中国武汉市 138 例 2019 新型冠状病毒感染肺炎住院患者的临床特征。
影响因子：
120.7
作者：
Wang, Dawei;Hu, Bo;Peng, Zhiyong
通讯作者：
Peng, Zhiyong