Prognostic models for COVID-19 needed updating to warrant transportability over time and space.
Prognostic models for COVID-19 needed updating to warrant transportability over time and space.
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DOI：
10.1186/s12916-022-02651-3
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发表时间：
2022-11-23
期刊：
影响因子：
9.3
通讯作者：
Kent, David M.
中科院分区：
文献类型：
--
作者：
van Klaveren, David;Zanos, Theodoros P.;Nelson, Jason;Levy, Todd J.;Park, Jinny G.;Helmrich, Isabel R. A. Retel;Rietjens, Judith A. C.;Basile, Melissa J.;Hajizadeh, Negin;Lingsma, Hester F.;Kent, David M.
支持使用新冠肺炎到急诊科(ED)的患者的决策需要准确的预测。我们的目标是评估预测新冠肺炎住院患者在不同地点和不同时间的预后的模型。我们纳入了到急诊室就诊的疑似新冠肺炎患者，他们住进了纽约市地区的12家医院和4家荷兰大医院。我们使用2020年9月至12月期间出现的第二波患者(纽约市和荷兰分别为2137人和3252人)来评估根据2020年3月至8月期间出现的第一波患者(12,163人和5831人)开发的模型。我们评估了两种预测院内死亡的模型：诺思韦尔新冠肺炎生存模型(NOCOS)是根据纽约市的数据开发的，而COP模型是根据荷兰的数据开发的。在同一地点(时间验证)和另一地点(地理验证)的后续第二波数据上对这些模型进行了验证。我们通过受试者工作特征曲线(AUC)下的面积、E统计量和净收益来评估模型的性能。与第二波(10.1%)和荷兰数据(第一波10.8%；第二波10.0%)相比，纽约第一波数据的28天死亡率(21.0%)要高得多。COPE在时间效度(AUC 0.82)、校正良好(E-统计量0.8%)上有很好的区分性。在地理验证中，辨别力是令人满意的(AUC 0.78)，但对死亡风险有适度的过度预测，特别是在高危患者(E-统计2.9%)。虽然NOCOS在第二波NYC数据(AUC 0.77)上进行测试时辨别力足够，但NOCOS系统高估了死亡风险(E-统计5.1%)。荷兰数据中的区分度是好的(AUC 0.81)，但过度预测了风险，特别是在低风险患者中(E-统计4.0%)。COPE和NOCOS的重新校准导致荷兰数据的净收益改善有限，但纽约市的数据净收益大幅改善。NOCOS的表现略逊于COP，可能反映了纽约市早期大流行的独特方面。在动态大流行期间，可能需要频繁更新预测模型，以便在时间和空间上具有可移动性。网上版载有补充材料，可在10.1186/s12916-022-02651-3查阅。
Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time. We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit. Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data. NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic. The online version contains supplementary material available at 10.1186/s12916-022-02651-3.
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DOI：
10.1161/circoutcomes.121.008487
发表时间：
2022-04
影响因子：
6.9
作者：
Gulati, Gaurav;Upshaw, Jenica;Wessler, Benjamin S.;Brazil, Riley J.;Nelson, Jason;van Klaveren, David;Lundquist, Christine M.;Park, Jinny G.;McGinnes, Hannah;Steyerberg, Ewout W.;Van Calster, Ben;Kent, David M.
通讯作者：
Kent, David M.
影响因子：
3.6
作者：
Vickers, Andrew J.;Elkin, Elena B.
通讯作者：
Elkin, Elena B.
DOI：
10.1056/nejmra2206573
发表时间：
2022-09-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1001/jama.2020.6775
发表时间：
2020-01-01
期刊：
JAMA, Journal of the American Medical Association
影响因子：
--
作者：
Richardson, Safiya;Hirsch, Jamie S.;,
通讯作者：
,
影响因子：
--
作者：
Schnellinger EM;Yang W;Kimmel SE
通讯作者：
Kimmel SE