Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.
Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.
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DOI：
10.1093/ofid/ofaa463
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发表时间：
2020-11
影响因子：
4.2
通讯作者：
Tuite R
中科院分区：
文献类型：
--
作者：
Fisman DN;Greer AL;Hillmer M;Tuite R
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）目前正在造成高死亡率的全球大流行。这种病毒引起的疾病的临床谱很广，从无症状感染到器官衰竭和死亡。对2019冠状病毒病（COVID-19）患者进行风险分层对于管理和试验入组的优先顺序是可取的。我们在加拿大安大略的一个基于人群的队列中开发了COVID-19死亡率的预测规则。从安大略省iPHIS系统中提取的数据是在2020年1月23日至5月15日期间提取的。Logistic回归为基础的预测规则和规则推导出一个考克斯比例风险模型的开发和验证使用分裂半验证。采用不同的方法对缺失数据进行敏感性分析。在21 922例COVID-19病例中，1734例具有完整数据被纳入推导集; 1796例被纳入验证集。年龄和合并症（特别是糖尿病、肾脏疾病和免疫功能低下）是死亡率的强预测因子。推导出四种基于点的预测规则（基础病例、排除吸烟、排除长期护理和基于考克斯模型）。在推导集中，所有人都表现出出色的辨别力（所有规则的曲线下面积> 0.92）和校准（通过Hosmer-Lemeshow检验，P> 0.50）。 在验证集中，所有的表现都很好，并且对替换缺失变量的不同方法具有鲁棒性。我们使用公共卫生病例管理数据系统，在加拿大安大略建立并验证了4个准确、校准良好、稳健的COVID-19死亡率临床预测规则。虽然这些规则需要外部验证，但它们可能是管理，风险分层和临床试验的有用工具。
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. Data from Ontario’s provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression–based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model–based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.
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影响因子：
13.8
作者：
Fisman, David N.;Bogoch, Isaac;Tuite, Ashleigh R.
通讯作者：
Tuite, Ashleigh R.
影响因子：
5.8
作者：
Hou, Wei;Zhang, Wei;Hu, Zhongjie
通讯作者：
Hu, Zhongjie
影响因子：
158.5
作者：
Fine, MJ;Auble, TE;Kapoor, WN
通讯作者：
Kapoor, WN
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
DOI：
10.1183/13993003.01761-2019
发表时间：
2020-04
期刊：
The European respiratory journal
影响因子：
--
作者：
Simpson CE;Chen JY;Damico RL;Hassoun PM;Martin LJ;Yang J;Nies M;Griffiths M;Vaidya RD;Brandal S;Pauciulo MW;Lutz KA;Coleman AW;Austin ED;Ivy DD;Nichols WC;Everett AD
通讯作者：
Everett AD