Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.
Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.
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DOI：
10.1001/jamanetworkopen.2021.16901
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发表时间：
2021-07-01
影响因子：
13.8
通讯作者：
National COVID Cohort Collaborative (N3C) Consortium
中科院分区：
文献类型：
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作者：
Bennett TD;Moffitt RA;Hajagos JG;Amor B;Anand A;Bissell MM;Bradwell KR;Bremer C;Byrd JB;Denham A;DeWitt PE;Gabriel D;Garibaldi BT;Girvin AT;Guinney J;Hill EL;Hong SS;Jimenez H;Kavuluru R;Kostka K;Lehmann HP;Levitt E;Mallipattu SK;Manna A;McMurry JA;Morris M;Muschelli J;Neumann AJ;Palchuk MB;Pfaff ER;Qian Z;Qureshi N;Russell S;Spratt H;Walden A;Williams AE;Wooldridge JT;Yoo YJ;Zhang XT;Zhu RL;Austin CP;Saltz JH;Gersing KR;Haendel MA;Chute CG;National COVID Cohort Collaborative (N3C) Consortium
在一个足够大的美国数据资源中，可以调整多种混杂因素，哪些风险因素与COVID-19的严重程度和严重程度随时间的变化趋势相关，机器学习模型能否预测临床严重程度？在这项针对174568名SARS-CoV-2成年人的队列研究中，32472人（18.6%）住院，6565人（20.2%）病情严重，第一天的机器学习模型准确预测了临床严重程度。整体死亡率为11. 6%，由二零二零年三月至四月的16. 4%下降至二零二零年九月至十月的8. 6%。这些研究结果表明，机器学习模型可用于预测COVID-19临床严重程度，并使用可用的大规模美国COVID-19数据资源。国家COVID队列协作（N3 C）是一个集中的，统一的，高粒度的电子健康记录库，是迄今为止最大的，最具代表性的COVID-19队列。该多中心数据集可以支持预测和诊断工具的稳健循证开发，并为临床护理和政策提供信息。评估COVID-19的严重程度和风险因素，并评估机器学习在预测临床严重程度方面的应用。在一项对1926526名美国成人SARS-CoV-2感染者进行的回顾性队列研究中，（聚合酶链反应>99%或抗原<1%）和2020年1月1日至2020年12月7日期间来自全国34个医疗中心的未感染SARS-CoV-2的成年患者作为对照，使用世界卫生组织COVID-19严重程度量表和人口统计学特征对患者进行分层。使用多变量逻辑回归评估组间随时间的差异。随机森林和XGBoost模型用于预测严重的临床过程（死亡，出院到临终关怀，侵入性呼吸支持，或体外膜氧合）。使用世界卫生组织COVID-19严重程度量表的患者人口统计学特征和COVID-19严重程度，以及使用多变量logistic回归的组间差异。该队列包括174568名SARS-CoV-2检测阳性的成年人（平均[SD]年龄，44.4 [18.6]岁; 53.2%为女性）和1133848名SARS-CoV-2检测阴性的成年对照（平均[SD]年龄，49.5 [19.2]岁; 57.1%为女性）。在174568名SARS-CoV-2成人患者中，有32472人（18.6%）住院，其中6565人（20.2%）有严重的临床病程（侵入性呼吸支持、体外膜肺氧合、死亡或出院到临终关怀机构）。在住院病人中，整体死亡率为11.6%，由2020年3月至4月的16.4%下降至2020年9月至10月的8.6%（每月趋势P = 0.002）。使用第一个住院日的64个输入，本研究使用随时间稳定的随机森林和XGBoost模型（两者的受试者工作曲线下面积= 0.87）预测严重的临床病程。与临床严重程度最密切相关的因素是pH值;这一结果在机器学习方法中是一致的。在一个单独的多变量逻辑回归模型中，年龄（比值比[OR]，1.03/年; 95% CI，1.03-1.04），男性（OR，1.60; 95% CI，1.51-1.69），肝脏疾病（OR，1.20; 95% CI，1.08-1.34），痴呆（OR，1.26; 95% CI，1.13-1.41），非裔美国人（OR，1.12; 95%CI，1.05-1.20）和亚裔（OR，1.33; 95%CI，1.12-1.57）种族和肥胖（OR，1.36; 95%CI，1.27-1.46）与较高的临床严重程度独立相关。这项队列研究发现，2020年COVID-19死亡率随时间下降，患者人口统计学特征和合并症与较高的临床严重程度相关。机器学习模型使用通常收集的住院前24小时的临床数据准确地预测了最终的临床严重程度。这项队列研究评估了COVID-19的严重程度以及随着时间推移与严重程度相关的因素，并评估了机器学习在预测临床严重程度方面的应用。
In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020. These findings suggest that machine learning models can be used to predict COVID-19 clinical severity with the use of an available large-scale US COVID-19 data resource. The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission. This cohort study evaluates COVID-19 severity and factors associated with severity over time and assesses the use of machine learning to predict clinical severity.
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DOI：
10.1056/nejmoa2022926
发表时间：
2020-11-19
期刊：
The New England journal of medicine
影响因子：
--
作者：
RECOVERY Collaborative Group;Horby P;Mafham M;Linsell L;Bell JL;Staplin N;Emberson JR;Wiselka M;Ustianowski A;Elmahi E;Prudon B;Whitehouse T;Felton T;Williams J;Faccenda J;Underwood J;Baillie JK;Chappell LC;Faust SN;Jaki T;Jeffery K;Lim WS;Montgomery A;Rowan K;Tarning J;Watson JA;White NJ;Juszczak E;Haynes R;Landray MJ
通讯作者：
Landray MJ
影响因子：
39.2
作者：
Ray, Joel G.;Schull, Michael J.;Park, Alison L.
通讯作者：
Park, Alison L.
影响因子：
15.2
作者：
Brat, Gabriel A.;Weber, Griffin M.;Kohane, Isaac S.
通讯作者：
Kohane, Isaac S.
影响因子：
8.8
作者：
Dennis JM;McGovern AP;Vollmer SJ;Mateen BA
通讯作者：
Mateen BA
风湿病和共同疾病19：流行病学和结果。
DOI：
10.1038/s41584-020-00562-2
发表时间：
2021-03
期刊：
Nature reviews. Rheumatology
影响因子：
--
作者：
Hyrich KL;Machado PM
通讯作者：
Machado PM