Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.
Real-time electronic health record mortality prediction during the COVID-19 pandemic: a prospective cohort study.
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DOI：
10.1093/jamia/ocab100
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发表时间：
2021-10-12
期刊：
影响因子：
--
通讯作者：
Bennett TD
中科院分区：
文献类型：
--
作者：
Sottile PD;Albers D;DeWitt PE;Russell S;Stroh JN;Kao DP;Adrian B;Levine ME;Mooney R;Larchick L;Kutner JS;Wynia MK;Glasheen JJ;Bennett TD
快速开发、验证和实施新型的COVID-19大流行实时死亡率评分，以改善序贯器官衰竭评估（SOFA），为危机护理标准团队提供决策支持。我们开发、验证并部署了一个堆叠式泛化模型，通过结合5个先前验证的评分和报告的与COVID-19特定死亡率相关的其他新变量，使用电子健康记录（EHR）中的可用数据预测死亡率。我们使用2020年3月至2020年7月期间从科罗拉多的12家医院前瞻性收集的数据验证了该模型。我们将新模型的受试者操作曲线下面积（AUROC）与SOFA评分和Charlson Comorbid指数进行了比较。前瞻性队列包括27296例患者，其中1358例（5.0%）为SARS-CoV-2阳性，4494例（16.5%）需要重症监护室护理，1480例（5.4%）需要机械通气，717例（2.6%）最终死亡。Charlson合并症指数和SOFA评分预测死亡率的AUROC分别为0.72和0.90。我们的新评分预测死亡率AUROC为0.94。在COVID-19患者亚组中，堆叠模型预测死亡率的AUROC为0.90，而SOFA的AUROC为0.85。堆叠回归允许一个灵活的，可更新的，可实时实现的，道德上可辩护的预测分析工具，用于决策支持，从验证模型开始，只包括改进预测的新信息。我们开发并验证了一个准确的住院死亡率预测评分在现场EHR自动和连续计算使用一种新的模型，改进了SOFA。
To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team. We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index. The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85. Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction. We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.
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影响因子：
10.7
作者：
Khan, Z.;Hulme, J.;Sherwood, N.
通讯作者：
Sherwood, N.
影响因子：
39.2
作者：
Antommaria, Armand H. Matheny;Gibb, Tyler S.;Eberl, Jason T.
通讯作者：
Eberl, Jason T.
影响因子：
2.7
作者：
Collins, Sarah A.;Cato, Kenrick;Vawdrey, David K.
通讯作者：
Vawdrey, David K.
影响因子：
2.7
作者：
Grissom, Colin K.;Brown, Samuel M.;Kuttler, Kathryn G.;Boltax, Jonathan P.;Jones, Jason;Jephson, Al R.;Orme, James F., Jr.
通讯作者：
Orme, James F., Jr.
DOI：
10.1186/cc10001
发表时间：
2011
期刊：
Critical care (London, England)
影响因子：
--
作者：
Adeniji KA;Cusack R
通讯作者：
Cusack R